research paper on social network analysis

Social network analysis using deep learning: applications and schemes

• Review Paper
• Published: 25 October 2021
• Volume 11 , article number  106 , ( 2021 )

Cite this article

• Ash Mohammad Abbas   ORCID: orcid.org/0000-0002-2247-400X 1  

2464 Accesses

10 Citations

Explore all metrics

Online social networks (OSNs) are part of daily life of human beings. Millions of users are connected through online social networks. Due to very large number of users and huge amount of data, social network analysis is a challenging task. The emergence of deep learning techniques has enabled to carry out a rigorous analysis of OSNs. A lot of research is carried out in the area of social network analysis using deep learning techniques from different perspectives. In this paper, we provide an overview of state-of-the-art research for different applications of social network analysis using deep learning techniques. We consider applications such as opinion analysis, sentiment analysis, text classification, recommender systems, structural analysis, anomaly detection, and fake news detection. We compare different schemes on the basis of their focus and features. Further, we point out directions for future work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Deep learning methods for anomalies detection in social networks using multidimensional networks and multimodal data: a survey

Nour El Houda Ben Chaabene, Amel Bouzeghoub, … Henda Hajjami Ben Ghezala

Information Cascading in Social Networks

Topological to deep learning era for identifying influencers in online social networks :a systematic review

Yasir Rashid & Javaid Iqbal Bhat

https://snap.stanford.edu .

https://www.aminer.cn/data-sna .

https://www.kaggle.com/ .

Abbreviations

Auto-encoder

Attention mechanism

Artificial neural network

Bidirectional encoder representations from transformers

Convolutional neural network

Deep belief networks

Deep Boltzmann machine

Deep graph learning

Deep integration representation

Deep joint reconstruction

Deep multiple network fusion

Deep reinforcement learning

Friendship using deep pairwise learning

Generative adversarial network

Graph neural network

Hybrid personalized propagation of neural prediction

Knowledge graphs

Location-based social network

Latent Dirichlet allocation

Ladder neural network

Latent semantic analysis

Long short-term memory

Multi-granularity graph embedding

Multilayer perceptrons

Massive open online course

Multi-view deep network

Natural language processing

Ontology-based restricted Boltzmann machine

Online social network

Recurrent neural network

Social curation service

Social influence deep learning

• Social network analysis

Self-organizing map

Support vector machine

Abd El-Jawad MH, Hodhod R, Omar YMK (2018) Sentiment analysis of social media networks using machine learning. In: 14th International Computer Engineering Conference (ICENCO), pp 174–176

Acemoglu D, Ozdaglar A (2011) Opinion dynamics and learning in social networks. Dyn Games Appl 1:3–49

Article   MathSciNet   MATH   Google Scholar  

Aktunc R, Toroslu IH, Karagoz P (2020) Event detection on communities: tracking the change in community structure within temporal communication networks. Lecture notes in social networks. Springer, Berlin

Google Scholar  

Alharthi R, Alhothali A, Moria K (2021) A real-time deep-learning approach for filtering Arabic low-quality content and accounts on Twitter. Inf Syst 99:101740

Article   Google Scholar  

Al-Molhem NR, Rahal Y, Dakkak M (2019) Social network analysis in telecom data. J Big Data 6:99

Altay EV, Alatas B (2018) Detection of cyberbullying in social networks using machine learning methods. In: International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp 87–91

Alwehaibi A, Roy K (2018) Comparison of pre-trained word vectors for Arabic text classification using deep learning approach. In: 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp 1471–1474

Amelkin V, Bogdanov P, Singh AK (2019) A distance measure for the analysis of polar opinion dynamics in social networks. ACM Trans Knowl Discov Data 13(4):1–34

Amine BM, Drif A, Giordano S (2019) Merging deep learning model for fake news detection. In: International Conference on Advanced Electrical Engineering (ICAEE), pp 1–4

Arasu A, Novak J, Tomlin J, Tomlin J (2002) Pagerank computation and the structure of the web: experiments and algorithms

Arya D, Worring M (2018) Exploiting relational information in social networks using geometric deep learning on hypergraphs. In: Proceedings of the ACM on International Conference on Multimedia Retrieval (ICMR). Association for Computing Machinery, New York, pp 117–125

Bai N, Meng F, Rui X, Wang Z (2021) Rumour detection based on graph convolutional neural net. IEEE Access 9:21686–21693

Becker R, Coro F, D’Angelo G, Gilbert H (2020) Balancing spreads of influence in a social network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, no 1, pp 3–10

Beskow DM, Carley KM (2020) You are known by your friends: leveraging network metrics for bot detection in Twitter. Springer, Berlin

Bhattacharjee U (2019) Capsule network on social media text: an application to automatic detection of clickbaits. In: 11th International Conference on Communication Systems Networks (COMSNETS), pp 473–476

Campos V, Salvador A, Giro-i Nieto X, Jou B (2015) Diving deep into sentiment: understanding fine-tuned CNNs for visual sentiment prediction. In: Proceedings of the 1st International Workshop on Affect & Sentiment in Multimedia (ASM). Association for Computing Machinery, New York, pp 57–62

Chandra Y, Jana A (2020) Sentiment analysis using machine learning and deep learning. In: 7th International Conference on Computing for Sustainable Global Development (INDIACom), pp 1–4

Cheng L, Tsai S (2019) Deep learning for automated sentiment analysis of social media. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, pp 1001–1004

Conitzer V, Panigrahi D, Zhang H (2020) Learning opinions in social networks. In: III HD, Singh A (eds) Proceedings of the 37th International Conference on Machine Learning (ICML), volume 119 of Proceedings of Machine Learning Research. PMLR, pp 2122–2132

Cuomo S, Colecchia G, Piccialli F, Maiorano F (2018) Traditional and deep learning approaches to information and influence propagation in social networks. In: 14th International Conference on Signal-Image Technology Internet-Based Systems (SITIS), pp 480–484

Dessì D, Dragoni M, Fenu G, Marras M, Recupero DR (2019) Evaluating neural word embeddings created from online course reviews for sentiment analysis. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (SAC). Association for Computing Machinery, New York, pp 2124–2127

De A, Valera I, Ganguly N, Bhattacharya S, Gomez-Rodriguez M (2016) Learning and forecasting opinion dynamics in social networks. In: Proceedings of the 30th International Conference on Neural Information Processing Systems (NeurIPS). Curran Associates Inc., Red Hook, pp 397–405

Dinh XT, Van Pham H (2020) A proposal of deep learning model for classifying user interests on social networks. In: Proceedings of the 4th International Conference on Machine Learning and Soft Computing (ICMLSC). Association for Computing Machinery, New York, pp 10–14

Dogan E, Kaya B (2019) Text summarization in social networks by using deep learning. In: 1st International Informatics and Software Engineering Conference (UBMYK), pp 1–5

Dubova M, Moskvichev A, Goldstone R (2020) Reinforcement communication learning in different social network structures. In: Proceedings of 1st workshop on language in reinforcement learning in conjunction with International Conference on Machine Learning (ICML)

Dutta S, Masud S, Chakrabarti S, Chakraborty T (2020) Deep exogenous and endogenous influence combination for social chatter intensity prediction. Association for Computing Machinery, New York, pp 1999–2008

Fu S, Wang G, Xia S, Liu L (2020) Deep multi-granularity graph embedding for user identity linkage across social networks. Knowl-Based Syst 193:105301

Gao T, Bao W, Li J, Gao X, Kong B, Tang Y, Chen G, Li X (2018) Dancinglines: an analytical scheme to depict cross-platform event popularity. In: Hartmann S, Ma H, Hameurlain A, Pernul G, Wagner RR (eds) Proceedings of 29th international conference on Database and Expert Systems Applications (DEXA), volume 11029 of lecture notes in computer science. Springer, pp 283–299

Garimella K, Gionis A, Parotsidis N, Tatti N (2017). Balancing information exposure in social networks. In: Proceedings of the 31st international conference on Neural Information Processing Systems (NeurIPS), Curran Associates Inc., Red Hook, pp 4666–4674

Geng X, Zhang H, Song Z, Yang Y, Luan H, Chua T-S (2014). One of a kind: user profiling by social curation. In: Proceedings of the 22nd ACM international conference on Multimedia (MM). Association for Computing Machinery, New York, pp 567–576

Gharibshah Z, Zhu X, Hainline A, Conway M (2020) Deep learning for user interest and response prediction in online display advertising. Data Sci Eng 5:12–26

Goularas D, Kamis S (2019) Evaluation of deep learning techniques in sentiment analysis from Twitter data. In: IEEE international conference on Deep Learning and Machine Learning in emerging applications (Deep-ML). IEEE, pp 12–17

Graoui E, Zrira N, Mekouar S, Benelallam I, Bouyakhf E (2016) Outlier and anomalous behavior detection in social networks using constraint programming. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE Computer Society, Los Alamitos, pp 1–8

Guimaraes RG, Rosa RL, De Gaetano D, Rodríguez DZ, Bressan G (2017) Age groups classification in social network using deep learning. IEEE Access 5:10805–10816

Hallac IR, Ay B, Aydin G (2018) Experiments on fine tuning deep learning models with news data for tweet classification. In: International Conference on Artificial Intelligence and Data Processing (IDAP), pp 1–5

He Q, Yang J, Shi B (2020) Constructing knowledge graph for social networks in a deep and holistic way. In: Companion Proceedings of the Web Conference (WWW). Association for Computing Machinery, New York, pp 307–308

Huang R, Ma L, He J, Chu X (2021) T-gan: a deep learning framework for prediction of temporal complex networks with adaptive graph convolution and attention mechanism. Displays 68:102023

Hu P, He T, Chan KCC, Leung H (2017) Deep fusion of multiple networks for learning latent social communities. In: IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp 765–771

Ilias L, Roussaki I (2021) Detecting malicious activity in Twitter using deep learning techniques. Appl Soft Comput 107:107360

Islam J, Zhang Y (2016) Visual sentiment analysis for social images using transfer learning approach. In: IEEE international conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), pp 124–130

Islam MR, Muthiah S, Ramakrishnan N (2019) Rumorsleuth: joint detection of rumor veracity and user stance. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Association for Computing Machinery, New York, pp 131–136

Islam MR, Liu S, Wang X, Xu G (2020) Deep learning for misinformation detection on online social networks: a survey and new perspectives. Soc Netw Anal Min 10:1–20

Jaradat S, Dokoohaki N, Matskin M, Ferrari E (2018) Learning what to share in online social networks using deep reinforcement learning. Lecture notes in social networks. Springer, Berlin

Jiang Y, Ma H, Liu Y, Li Z, Chang L (2021) Enhancing social recommendation via two-level graph attentional networks. Neurocomputing 449:71–84

Jing N, Wu Z, Wang H (2021) A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Syst Appl 178:115019

Jin D, Ge M, Li Z, Lu W, He D, Fogelman-Soulie F (2017) Using deep learning for community discovery in social networks. In: IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), pp 160–167

Kapil P, Ekbal A (2020) A deep neural network based multi-task learning approach to hate speech detection. Knowl-Based Syst 210:106458

Kazanova M (2017) Sentiment140 dataset with 1.6 million tweets: sentiment analysis with tweets

Keikha MM, Rahgozar M, Asadpour M, Abdollahi MF (2020) Influence maximization across heterogeneous interconnected networks based on deep learning. Expert Syst Appl 140:112905

Khaled A, Ouchani S, Chohra C (2019) Recommendations-based on semantic analysis of social networks in learning environments. Comput Hum Behav 101:435–449

Khan BA, Abbas AM (2014) Goldencrops: a software tool for analysis of a social network. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, pp 1958–1962

Khan M, Malviya A (2020) Big data approach for sentiment analysis of Twitter data using Hadoop framework and deep learning. In: International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), pp 1–5

Kumar A, Srinivasan K, Cheng W-H, Zomaya AY (2020) Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf Process Manag 57(1):102141

Leung CK, Cuzzocrea A, Mai JJ, Deng D, Jiang F (2019) Personalized deepinf: enhanced social influence prediction with deep learning and transfer learning. In: IEEE International Conference on Big Data (Big Data), pp 2871–2880

Li J, Gao Y, Gao X, Shi Y, Chen G (2019) Senti2pop: sentiment-aware topic popularity prediction on social media. In: IEEE International Conference on Data Mining (ICDM), pp 1174–1179

Li D, Rzepka R, Ptaszynski M, Araki K (2020) Hemos: a novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media. Inf Process Manag 57(6):102290

Li B, Pi D, Lin Y (2021a) Learning ladder neural networks for semi-supervised node classification in social network. Expert Syst Appl 165:113957

Li G, Dong M, Ming L, Luo C, Yu H, Hu X, Zheng B (2021b) Deep reinforcement learning based ensemble model for rumor tracking. Inf Syst 103:101772

Li S, Jiang L, Wu X, Han W, Zhao D, Wang Z (2021c) A weighted network community detection algorithm based on deep learning. Appl Math Comput 401:126012

MathSciNet   MATH   Google Scholar  

Li X, Cao Y, Li Q, Shang Y, Li Y, Liu Y, Xu G (2021d) Rlink: deep reinforcement learning for user identity linkage. World Wide Web 24:85–103

Li Z, Wang X, Li J, Zhang Q (2021e) Deep attributed network representation learning of complex coupling and interaction. Knowl-Based Syst 212:106618

Liang B, Yin R, Gui L, Du J, He Y, Xu R (2020) Aspect-invariant sentiment features learning: adversarial multi-task learning for aspect-based sentiment analysis. Association for Computing Machinery, New York, pp 825–834

Liao W, Huang Y, Yang T, Wu Y (2019) Analyzing social network data using deep neural networks: a case study using twitter posts. In: IEEE International Symposium on Multimedia (ISM), pp 237–2371

Lim J, Liu Z, Zhou L (2019) Detection of fraudulent tweets: an empirical investigation using network analysis and deep learning technique. In: IEEE international conference on Intelligence and Security Informatics (ISI), pp 203–205

Lin G, Kang X, Liao K, Zhao F, Chen Y (2021) Deep graph learning for semi-supervised classification. Pattern Recognit 118:108039

Liu L, Lu Y, Luo Y, Zhang R, Itti L, Lu J (2016) Detecting “smart” spammers on social network: a topic model approach. arXiv:1604.08504

Lu Y (2019) Social network fake account dataset: detecting smart spammers

Lucci A (2018) Huawei social network data: multinet social network

Luceri L, Braun T, Giordano S (2019) Analyzing and inferring human real-life behavior through online social networks with social influence deep learning. Appl Netw Sci 4:34

Martinelli F, Mercaldo F, Santone A (2019) Social network polluting contents detection through deep learning techniques. In: International Joint Conference on Neural Networks (IJCNN), pp 1–10

Min S, Gao Z, Peng J, Wang L, Qin K, Fang B (2021) Stgsn—a spatial-temporal graph neural network framework for time-evolving social networks. Knowl-Based Syst 214:106746

Molokwu BC, Kobti Z (2019) Spatial event prediction via multivariate time series analysis of neighboring social units using deep neural networks. In: International Joint Conference on Neural Networks (IJCNN), pp 1–8

Nerabie AM, AlKhatib M, Mathew SS, Barachi ME, Oroumchian F (2021) The impact of Arabic part of speech tagging on sentiment analysis: a new corpus and deep learning approach. Procedia Comput Sci 184:148–155 ( The 12th International Conference on Ambient Systems, Networks and Technologies (ANT)/The 4th International Conference on Emerging Data and Industry 4.0 (EDI40)/Affiliated Workshops )

Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). Association for Computing Machinery, New York, pp 701–710

Pham P, Nguyen LT, Vo B, Yun U (2021) Bot2vec: a general approach of intra-community oriented representation learning for bot detection in different types of social networks. Inf Syst 103:101771

Phan N, Dou D, Wang H, Kil D, Piniewski B (2015) Ontology-based deep learning for human behavior prediction in health social networks. In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (BCB). Association for Computing Machinery, New York, pp 433–442

Pota M, Esposito M, Palomino MA, Masala GL (2018) A subword-based deep learning approach for sentiment analysis of political tweets. In: 32nd international conference on Advanced Information Networking and Applications Workshops (WAINA), pp 651–656

Preethi G, Krishna PV, Obaidat MS, Saritha V, Yenduri S (2017) Application of deep learning to sentiment analysis for recommender system on cloud. In: international conference on Computer, Information and Telecommunication Systems (CITS), pp 93–97

Qawasmeh E, Tawalbeh M, Abdullah M (2019) Automatic identification of fake news using deep learning. In: Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS), pp 383–388

Qiu J, Tang J, Ma H, Dong Y, Wang K, Tang J (2018) Deepinf: social influence prediction with deep learning. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). Association for Computing Machinery, New York, pp 2110–2119

Rafailidis D, Crestani F (2018) Friend recommendation in location-based social networks via deep pairwise learning. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Press, pp 421–428

Ramadhani AM, Goo HS (2017) Twitter sentiment analysis using deep learning methods. In: 7th International Annual Engineering Seminar (InAES). IEEE, pp 1–4

Ren Z, Shen Q, Diao X, Xu H (2021) A sentiment-aware deep learning approach for personality detection from text. Inf Process Manag 58(3):102532

Rossi RA, Ahmed NK (2015) The network data repository with interactive graph analytics and visualization. In: AAAI

Sadr H, Pedram MM, Teshnehlab M (2020) Multi-view deep network: a deep model based on learning features from heterogeneous neural networks for sentiment analysis. IEEE Access 8:86984–86997

Savage D, Zhang X, Yu X, Chou P, Wang Q (2014) Anomaly detection in online social networks. Soc Netw 39:62–70

Shang L, Zhang Y, Zhang D, Wang D (2020) Fauxward: a graph neural network approach to fauxtography detection using social media comments. Soc Netw Anal Min 10:1–16

Sinnema C, Daly AJ, Liou Y-H, Rodway J (2020) Exploring the communities of learning policy in New Zealand using social network analysis: a case study of leadership, expertise, and networks. Int J Educ Res 99:101492

Sun C, Lv L, Tian G, Liu T (2021) Deep interactive memory network for aspect-level sentiment analysis. ACM Trans Asian Low Resour Lang Inf Process 20(1):1–12

Tan Q, Liu N, Hu X (2019) Deep representation learning for social network analysis. Front Big Data 2:2

Tang W, Hui B, Tian L, Luo G, He Z, Cai Z (2021) Learning disentangled user representation with multi-view information fusion on social networks. Inf Fusion 74:77–86

Thovex C (2018) Deep probabilistic learning in hidden social networks and facsimile detection. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Press, pp 731–735

Tian S, Mo S, Wang L, Peng Z (2020) Deep reinforcement learning-based approach to tackle topic-aware influence maximization. Data Sci Eng 5:1–11

Tomasi LD (2019) Youtube social network: dataset for networks, graphs analysis

Tong A, Du D-Z, Wu W (2018) On misinformation containment in online social networks. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds) Proceedings of international conference on Neural Information Processing Systems (NeurIPS), vol 31. Curran Associates, Inc

Tu S, Aslay C, Gionis A (2020) Co-exposure maximization in online social networks. In: Proceedings of international conference on Neural Information Processing Systems (NeurIPS)

Tzogka C, Passalis N, Iosifidis A, Gabbouj M, Tefas A (2019) Less is more: deep learning using subjective annotations for sentiment analysis from social media. In: IEEE 29th international workshop on Machine Learning for Signal Processing (MLSP), pp 1–6

Uddin AH, Bapery D, Arif ASM (2019) Depression analysis from social media data in Bangla language using long short term memory (LSTM) recurrent neural network technique. In: International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), pp 1–4

Veyseh APB, Thai MT, Nguyen TH, Dou D (2019) Rumor detection in social networks via deep contextual modeling. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Association for Computing Machinery, New York, pp 113–120

Vu T, Parker DS (2015) Node embeddings in social network analysis. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Association for Computing Machinery, New York, pp 326–329

Wan F (2019) Sentiment analysis of weibo comments based on deep neural network. In: International Conference on Communications, Information System and Computer Engineering (CISCE), pp 626–630

Wanda P, Jie HJ (2021) Deepfriend: finding abnormal nodes in online social networks using dynamic deep learning. Soc Netw Anal Min 11:1–12

Wang J, He X, Gong Q, Chen Y, Wang T, Wang X (2018) Deep learning-based malicious account detection in the momo social network. In: 27th International Conference on Computer Communication and Networks (ICCCN), pp 1–2

Wang D, Al-Rubaie A, Hirsch B, Pole GC (2021) National happiness index monitoring using twitter for bilanguages. Soc Netw Anal Min 11:24

Weber CT, Syed S (2019) Interdisciplinary optimism? Sentiment analysis of Twitter data. R Soc Open Sci 6(7):190473

Wijenayake P, Silva Dd, Alahakoon D, Kirigeeganage S (2020) Automated detection of social roles in online communities using deep learning. In: Proceedings of the 3rd International Conference on Software Engineering and Information Management (ICSIM). Association for Computing Machinery, New York, pp 63–68

Wu K, Watters P, Magdon-Ismail M (2016) Network classification using adjacency matrix embeddings and deep learning. In: Proceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Press, pp 299–306

Wu L, Rao Y, Yu H, Wang Y, Ambreen N (2019) A multi-semantics classification method based on deep learning for incredible messages on social media. Chin J Electron 28(4):754–763

Wu Y, Fang Y, Shang S, Jin J, Wei L, Wang H (2021) A novel framework for detecting social bots with deep neural networks and active learning. Knowl-Based Syst 211:106525

Yu J, Gao M, Yin H, Li J, Gao C, Wang Q (2019) Generating reliable friends via adversarial training to improve social recommendation. In: IEEE International Conference on Data Mining (ICDM), pp 768–777

Zhang A, Lipton ZC, Li M, Smola AJ (2021) Dive into deep learning

Zheng D, Wang M, Gan Q, Zhang Z, Karypis G (2020) Scalable graph neural networks with deep graph library. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD). Association for Computing Machinery, New York, pp 3521–3522

Zhou F, Liu L, Zhang K, Trajcevski G, Wu J, Zhong T (2018) Deeplink: a deep learning approach for user identity linkage. In: IEEE Conference on Computer Communications (INFOCOM), pp 1313–1321

Zou W, Hu X, Pan Z, Li C, Cai Y, Liu M (2020) Exploring the relationship between social presence and learners’ prestige in mooc discussion forums using automated content analysis and social network analysis. Comput Hum Behav 115:106582

Download references

Author information

Authors and affiliations.

Department of Computer Engineering, Zakir Husain College of Engineering and Technology, Aligarh Muslim University, Aligarh, India

Ash Mohammad Abbas

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Ash Mohammad Abbas .

Ethics declarations

Conflict of interest.

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Abbas, A.M. Social network analysis using deep learning: applications and schemes. Soc. Netw. Anal. Min. 11 , 106 (2021). https://doi.org/10.1007/s13278-021-00799-z

Download citation

Received : 08 January 2021

Revised : 23 August 2021

Accepted : 14 September 2021

Published : 25 October 2021

DOI : https://doi.org/10.1007/s13278-021-00799-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Deep learning
• Sentiment analysis
• Opinion analysis
• Recommender systems
• Community detection
• Fake news detection
• Find a journal
• Publish with us
• Track your research

A Review on Social Network Analysis Methods and Algorithms

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

• Open access
• Published: 10 January 2022

The use of social network analysis in social support and care: a systematic scoping review protocol

• Rosario Fernández-Peña   ORCID: orcid.org/0000-0002-6830-6001 1 , 2 , 3 ,
• María-Antonia Ovalle-Perandones 3 , 4 ,
• Pilar Marqués-Sánchez 3 ,
• Carmen Ortego-Maté 1 , 2 &
• Nestor Serrano-Fuentes 3 , 5  

Systematic Reviews volume  11 , Article number:  9 ( 2022 ) Cite this article

5394 Accesses

2 Citations

11 Altmetric

Metrics details

In recent decades, the literature on Social Network Analysis and health has experienced a significant increase. Disease transmission, health behavior, organizational networks, social capital, and social support are among the different health areas where Social Network Analysis has been applied. The current epidemiological trend is characterized by a progressive increase in the population’s ageing and the incidence of long-term conditions. Thus, it seems relevant to highlight the importance of social support and care systems to guarantee the coverage of health and social needs within the context of acute illness, chronic disease, and disability for patients and their carers. Thus, the main aim is to identify, categorize, summarize, synthesize, and map existing knowledge, literature, and evidence about the use of Social Network Analysis to study social support and care in the context of illness and disability.

This scoping review will be conducted following Arksey and O'Malley's framework with adaptations from Levac et al. and Joanna Briggs Institute’s methodological guidance for conducting scoping reviews. We will search the following databases (from January 2000 onwards): PubMed, MEDLINE, Web of Science Core Collection, SCOPUS, CINAHL, PsycINFO, Cochrane Database of Systematic Reviews, PROSPERO, and DARE. Complementary searches will be conducted in selected relevant journals. Only articles related to social support or care in patients or caregivers in the context of acute illnesses, disabilities or long-term conditions will be considered eligible for inclusion. Two reviewers will screen all the citations, full-text articles, and abstract the data independently. A narrative synthesis will be provided with information presented in the main text and tables.

The knowledge about the scientific evidence available in the literature, the methodological characteristics of the studies identified based on Social Network Analysis, and its main contributions will highlight the importance of health-related research's social and relational dimensions. These results will shed light on the importance of the structure and composition of social networks to provide social support and care and their impact on other health outcomes. It is anticipated that results may guide future research on network-based interventions that might be considered drivers to provide further knowledge in social support and care from a relational approach at the individual and community levels.

Trial registration

Open Science Framework https://osf.io/dqkb5 .

Peer Review reports

Social Network Analysis (SNA) is a research approach within the social and behavioral sciences which focuses on ways of interaction and interconnection between individuals and social groups to explain social patterns of feelings, thoughts, and behaviors [ 1 , 2 ]. In recent decades, research based on SNA has been increasingly used in health, including areas such as disease transmission, health behavior, organizational networks, social capital, and social support [ 3 , 4 , 5 ].

The literature on social networks and health begins by referring to the idea that people are embedded in a network of relationships. The first empirical studies were published in the Annual Review of Public Health in the mid-1990s [ 6 ]. They showed the usefulness of specific SNA techniques to evaluate prevention programs among the involved organizations [ 7 ], or the relationship between HIV status, drug use and sexual relations [ 8 ]. Since then, there has been an exponential increase in scientific publications based on this methodology, especially in the last decade.

Different SNA studies have focused on showing the relationship between the characteristics of the social network and different health-related outcomes such as health behaviors [ 9 , 10 ], satisfaction with social support in chronic illness [ 11 ], quality of care and patient safety [ 12 ], the influence of social networks on HIV prevention and treatment outcomes [ 13 ], behavior change and risk of disease transmission [ 14 ], or performance in health care organizations and health care providers [ 15 , 16 , 17 ]. Also, SNA has been applied in health interventions based on social networks [ 18 , 19 , 20 , 21 ].

As mentioned above, one of the application areas of SNA is social support. The current epidemiological trend is characterized by a progressive increase in the population’s ageing and the incidence of long-term conditions. Thus, it seems relevant to highlight the importance of both social support and care systems to guarantee the coverage of health and social needs within the context of acute illness, chronic disease, and disability for patients and their careers. In its conceptual differentiation, caring and social support are dynamic processes that allude to interpersonal relationships [ 5 , 22 , 23 , 24 ]. However, they exist predominantly in separate domains. Care belongs to the professional context, while social support refers mainly to non-professional providers [ 25 ]. Unlike other approaches, research that uses SNA to study social support and care considers the network’s structural properties as the object of study [ 26 , 27 ] to know their relationship with other variables of interest. In this review, social networks are considered a structural framework to understand social support and care as relational concepts or as resources transferred through relationships [ 28 , 29 ]. Since there is no previous research that synthesizes the current knowledge on this research topic, we aim to identify, categorize, summarize, synthesize, and map existing knowledge, literature, and evidence about social network analysis to study social support and care in patients or caregivers in the context of illness, disability, or long-term conditions.

A scoping review is selected as an exploratory form of knowledge synthesis due to the extensive and growing literature that uses SNA in social support and care. This type of review is commonly undertaken to examine the extent, range, and nature of research activity in a topic area [ 30 ]: (a) to identify the types of available evidence in a given field, (b) to clarify key concepts/definitions in the literature, (c) to examine how research is conducted on a certain topic or area, (d) to identify key characteristics or factors related to a concept, (e) as a precursor to a systematic review, and (f) to identify and analyze knowledge gaps [ 31 ].

Arksey and O’Malley’s methodology framework [ 32 ], its advance by Levac and colleagues [ 33 ], and Joanna Briggs Institute’s methodological guidance [ 34 ] will be followed to conduct this scoping review through five stages: (a) identifying and stating research questions, (b) identifying relevant studies, (c) study selection, (d) charting data, and (e) collating and summarizing results [ 32 ].

This protocol is registered within the Open Science Framework platform (registration ID: https://osf.io/dqkb5 ). This scoping review has been reported using PRISMA-P [ 35 ] (Additional file 1 ). The final output will adhere to the Preferred Reporting for Systematic Reviews (PRISMA-ScR) checklist [ 36 ].

Stage 1: identification of the research question

The following research questions will guide the review:

What scientific evidence or studies are available in the literature on social support and care using the SNA methods?

What methodological characteristics constitute this body of literature?

What are the main contributions of these studies?

What knowledge and research gaps can be identified in the literature?

Stage 2: identifying relevant studies

The PCC framework (Population-Concept-Context) (Table 1 ) will be used to clearly define the concepts in the main review question, determine the eligibility of studies and guide the selection process [ 34 ]. We use a glossary of Terms for Community Heath Care from the World Health Organization to clarify the concepts used in our review [ 37 ].

The limits to be used in online databases searches will be: articles published in Spanish and English and the year of publication (from January 2000 onwards). The inclusion criteria will be (a) empirical studies with SNA methodology (quantitative or mixed methods design) and (b) studies whose participants are patients or caregivers as receivers of care or social support in the context of illness or disability provided by both, health professionals or personal/informal contacts with no age limits. The exclusion criteria will be (a) theoretical papers, (b) grey literature, and (c) qualitative studies.

The PRISMA flow chart [ 38 ] (Additional file 2 ) will capture and present our planned screening and selection process. The search strategy developed by MAOP will follow a comprehensive and sequential three steps and be checked by RMM. The Peer Review of Electronic Search Strategies Evidence-Based Checklist (PRESS EBC) will be followed to assess the search strategy's quality [ 39 ].

In the first step, the authors will work with an initial limited search in the PubMed database. The keywords and index terms will be identified in the titles and abstracts of the retrieved papers. In the second step, these keywords and index terms will be used to search across different databases. A structured search strategy will include Boolean operators (and, or, not), and truncations, either individually or in combination to ensure the search process. We will search the following databases: PubMed, MEDLINE, Web of Science Core Collection, SCOPUS, CINAHL, PsycINFO, Cochrane Database of Systematic Reviews, PROSPERO, and DARE (see Additional file 3 for search strategy). In a third step, a primary source search will be driven in the following journals: Social Networks, Connections, Journal of Social Structure, Redes, and Portularia. The retrieved references will be managed, and duplicates will also be removed using Mendeley and excel spreadsheet as a data extraction tool for the study selection.

Stage 3: study selection

Titles and abstracts of identified records will be assessed by two authors (RFP and NSF), independently. Disagreements will be resolved by consensus or with the assistance of a third author (PMS). The selected studies’ full text will be retrieved and checked independently by two authors (RFP and NSF). Sources of information that do not meet the eligibility criteria will be disregarded. A record of those sources and the reasons for their exclusion will be kept in a separate file.

Quality assessment

Scoping reviews are designed to provide an overview of the existing literature, regardless of quality. Therefore, a formal assessment of the quality of the included studies will not be conducted [ 32 ].

Stage 4: charting the data

The data charting aims to provide a descriptive summary of the results that align with this scoping review’s research questions. Thus, a data extraction tool designed for this study has been adapted from the template data extraction instrument for scoping reviews provided for JBI Manual for evidence synthesis [ 34 ] and will be used to capture the research purpose's most relevant information (see Table 2 ).

Charting the results will be an iterative process. Table 2 will be updated continuously until the end of the analysis. We will trial the extraction form on two or three sources to ensure all relevant results are extracted by at least two members of the review team [ 34 ].

Stage 5: collating and summarizing our results

According to the data extraction template, the obtained information will be part of built evidence tables with an overall description of the papers. We will follow the Arksey and O’Malley’s methods [ 32 ] to provide a descriptive numerical analysis of the topic, including the extent, characteristics, and their distribution in the included studies. We will present specific features and outcome measures of all included studies in a diagrammatic or tabular form. A descriptive summary will accompany the tabulated and/or charted results and will describe how the results relate to the review objectives and questions. This procedure will allow identifying specific gaps in the literature that might require further research.

The results of this scoping review will be added to the existing review articles on the use of the SNA in the health research area as complex health care interventions [ 40 ], the behavior change [ 41 ], nursing [ 42 ], inter-organizational networks [ 43 ], or healthcare providers [ 44 ]. Specifically, this protocol describes a systematic method synthesizing the existing literature on the use of SNA to study social support and care within the context of illness and disability.

This type of review is a convenient tool to determine the coverage of the body of literature on this specific area and will give a precise indication of the number of studies available and an overview of its focus. It might be useful for uncovering emerging evidence when it is still unclear what other more explicit questions can be addressed by a more precise systematic review [ 45 ]. Thus, the broader scope and nature justify the election of a scoping review versus a traditional systematic review that would answer specific questions and require more expansive inclusion criteria [ 31 ].

The authors anticipate that this review’s results will shed light on the importance of the structure and composition of social networks to provide social support and care and their impact on other health outcomes. This differs from many studies in this topic which use non-network approaches. The knowledge about the scientific evidence available in the literature, the methodological characteristics of the studies identified based on SNA, and its main contributions will highlight the importance of health-related research’s social and relational dimensions. Furthermore, it will identify areas for future research where social networks might be considered drivers to provide further knowledge in social support and care from a relational approach at the individual and community levels. The findings of this study will be disseminated through peer-review publications and national and international conferences.

Availability of data and materials

Further information related to this review can be provided upon reasonable request. Interested readers should contact the corresponding author.

Abbreviations

Social network analysis

Wasserman S, Faust K. Social network analysis. Methods and applications. Cambridge: Cambridge University Press; 1994.

Book   Google Scholar  

Scott J. What is social network analysis? Crow G, editor. New York: Bloomsbury Academic; 2012. p. 127.

Luke DA, Harris JK. Network analysis in public health: history, methods, and applications. Annu Rev Public Health. 2007;28(1):69–93. Available from. https://doi.org/10.1146/annurev.publhealth.28.021406.144132 .

Article   PubMed   Google Scholar  

Moore S, Kawachi I. Twenty years of social capital and health research: a glossary. J Epidemiol Community Health. 2017;71(5):513–7. Available from:. https://doi.org/10.1136/jech-2016-208313 .

Article   CAS   PubMed   Google Scholar  

Smith K, Christakis N. Social networks and health. Annu Rev Sociol. 2008;34:405–18. Available from. https://doi.org/10.1146/annurev.soc.34.040507.134601 .

Article   Google Scholar  

Valente TW. Networks and public health : a review of network epidemiology : a handbook for survey. Soc Networks. 2007;29:154–9. Available from. https://doi.org/10.1016/j.socnet.2006.07.002 .

Wickizer TM, Von Korff M, Cheadle A, Maeser J, Wagner EH, Pearson D, et al. Activating communities for health promotion: a process evaluation method. Am J Public Health. 1993;83(4):561–7. Available from. https://doi.org/10.1016/j.socnet.2006.07.002 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Friedman SR, Neaigus A, Jose B, Curtis R, Goldstein M, Ildefonso G, et al. Sociometric risk networks and risk for HIV infection. Am J Public Health. 1997;87(8):1289–97. Available from. https://doi.org/10.2105/AJPH.87.8.1289 .

Latkin C, Knowlton A. Social network assessments and interventions for health behavior change: a critical review. Behav Med. 2015;41(3):90–7. Available from. https://doi.org/10.1080/08964289.2015.1034645 .

Lin S, Faust L, Robles-Granda P, Kajdanowicz T, Chawla V. Social network structure is predictive of health and wellness. PLoS One. 2019;14(6):1–17. Available from. https://doi.org/10.1371/journal.pone.0217264 .

Article   CAS   Google Scholar  

Fernández-Peña R, Molina J, Valero O. Satisfaction with social support received from social relationships in cases of chronic pain: The influence of personal network characteristics in terms of structure, composition and functional content. Int J Environ Res Public Health. 2020, 17(8):1–19. Available from. https://doi.org/10.3390/ijerph17082706 .

Cunningham FC, Ranmuthugala G, Plumb J, Georgiou A, Westbrook JI, Braithwaite J. Health professional networks as a vector for improving healthcare quality and safety: A systematic review. BMJ Qual Saf. 2012;21(3):239–49. Available from:. https://doi.org/10.1136/bmjqs-2011-000187 .

Ghosh D, Krishnan A, Gibson B, Brown S-E, Latkin CA, Altice FL. Social network strategies to adress HIV prevention and treatment continuum of care amont at-risk and HIV-infected substance users: a systematic scoping review. AIDS Behav. 2017;21(4):1183–207. Available from. https://doi.org/10.1016/j.jaad.2017.03.040 .

Article   PubMed   PubMed Central   Google Scholar  

Perkins JM, Subramanian SV, Christakis NA. Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries. Soc Sci Med. 2015;125:60–78. Available from:. https://doi.org/10.1016/j.socscimed.2014.08.019 .

Tasselli S. Social networks of professionals in health care organizations: a review. Med Care Res Rev. 2014;71(6):619–60. Available from. https://doi.org/10.1177/1077558714557079 .

Chambers D, Wilson P, Thompson C, Harden M. Social network analysis in healthcare settings: a systematic scoping review. PLoS One. 2012;7(8):1–10. Available from. https://doi.org/10.1371/journal.pone.0041911 .

Sabot K, Wickremasinghe D, Blanchet K, Avan B, Schellenberg J. Use of social network analysis methods to study professional advice and performance among healthcare providers : a systematic review. Syst Rev. 2017;6:1–23. Available from. https://doi.org/10.1186/s13643-017-0597-1 .

Pinto R. Using social network interventions to improve mentally ill clients’well-being. Clin Soc Work J. 2006;34(1):83–100. Available from. https://doi.org/10.1007/s10615-005-0005-5 .

Valente T. Network interventions. Science. 2012;337(6090):49–53. Available from. https://doi.org/10.1126/science.1217330 .

Gesell S, Barkin S, Valente T. Social network diagnostics: a tool for monitoring group interventions. Implement Sci. 2013;8(1):1–12. Available from. https://doi.org/10.1186/1748-5908-8-116 .

Valente TW. Putting the network in network interventions. PNAS. 2017;114(36):1500–1. Available from. https://doi.org/10.1073/pnas.1712473114 .

Campbell SM, Roland MO, Buetow SA. Defining quality of care. Soc Sci Med. 2000;51:1611–25. Available from. https://doi.org/10.1016/S0277-9536(00)00057-5 .

House JS, Umberson D, Landis KR. Structures and processes of social support. Annu Rev or Sociol. 1988;1988(14):293–318.

Finfgeld-Connett D. Clarification of social support. J Nurs Scholarsh. 2005;37(1):4–9. Available from. https://doi.org/10.1111/j.1547-5069.2005.00004.x .

Finfgeld-Connett D. Concept comparison of caring and social support. Int J Nurs Terminol Classif. 2007;18(2):58–68. Available from. https://doi.org/10.1111/j.1744-618X.2007.00051.x .

O’Malley JA, Marsden PV. The analysis of social networks. Heal Serv Outcomes Res Methodol. 2008;8:222–69. Available from. https://doi.org/10.1007/s10742-008-0041-z .

Hawe P, Webster C, Shiell A. A glossary of terms for navigating the field of social network analysis. J Epidemiol Community Health. 2004;58(12):971–5. Available from:. https://doi.org/10.1136/jech.2003.014530 .

Pescosolido BA, Levy JA. The role of social networks in health, illness, disease and healing: the accepting present, the forgotten past, and the dangerous potential for a complacent future. In: Levy JA, Pescosolido BA, editors. Social networks and health: advances in medical sociology. Oxford: Elsevier Science; 2002. p. 3–25.

Chapter   Google Scholar  

Faber A, Wasserman S. Social support and social networks: synthesis and review. In: Levy J, Pescosolido B, editors. Social Networks and Health: Advances in Medical Sociology. Oxford: Elservier Science; 2002. p. 29–72.

Pham MTM, Rajić A, Greig JDJ, Sargeant JMJ, Papadopoulos A, Mcewen SAS. A scoping review of scoping reviews: advancing the approach and enhancing the consistency. Res Synth Methods. 2014;5(4):371–85. Available from. https://doi.org/10.1002/jrsm.1123 .

Munn Z, Peters M, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol. 2018;18(143):1–7. Available from. https://doi.org/10.1186/s12874-018-0611-x .

Arksey H, O’Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32. Available from. https://doi.org/10.1080/1364557032000119616 .

Levac D, Colquhoun H, Obrien K. Scoping studies:advancing the methodology. Implement Sci. 2010;5(69):1–9 Available from: http://www.implementationscience.com/content/5/1/69 .

Google Scholar  

Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil H. Scoping reviews. In: Z AE and M, editor. JBI Reviewer ’ s Manual: In; 2020. Available from: https://reviewersmanual.joannabriggs.org/ .

Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (Prisma-P) 2015: Elaboration and explanation. BMJ. 2015;349:1–25. Available from. https://doi.org/10.1136/bmj.g7647 .

Tricco A, Lillie E, Zarin W, O’Brien K, Al E. PRISMA extension for scoping reviews (PRISMA-ScR) : checklist and explanation. Ann Intern Med. 2018;169(7):467–73. Available from. https://doi.org/10.7326/M18-0850 .

Gary A, Faulkner D. A glossary of terms for community health care and services for older persons. Kobe, Japan: WHO Centre for Health Development. 2004;5. https://apps.who.int/iris/handle/10665/68896 .

Moher D, Liberati A, Tetzlaff J, Altman DG, Group TP. Preferred reporting items for systematic reviews and meta-analyses : The PRISMA Statement. PLoS Med. 2009;6(7):e1000097.

McGowan J, Sampson M, Salzwedel DM, Cogo E, Foerster V, Lefebvre C. PRESS peer review of electronic search strategies: 2015 Guideline Statement. J Clin Epidemiol. 2016;75:40–6. Available from:. https://doi.org/10.1016/j.jclinepi.2016.01.021 .

Smit L, Dikken J, Schuurmans M, De Wit N, Bleijenberg N. Value of social network analysis for developing and evaluating complex healthcare interventions: A scoping review. BMJ Open. 2020;10:1–15.

Chambers D, Wilson P, Thompson C, Harden M. Social network analysis in healthcare settings: a systematic scoping review. PLoS One. 2012;7(8):e41911. https://doi.org/10.1371/journal.pone.0041911 .

Benton DC, Pérez-Raya F, Fernández-Fernández MP, González-Jurado MA. A systematic review of nurse-related social network analysis studies. Int Nurs Rev. 2015;62(3):321–39.

Timm I, Rapp S, Jeuter C, Bachert P, Reichert M, Woll A, et al. Interorganizational networks in physical activity promotion: a systematic review. Int J Environ Res Public Health. 2021;18:1–17.

Bae S, Nikolaev A, Seo JY, Castner J. Health care provider social network analysis: a systematic review. Nurs Outlook. 2015;63(5):566–84.

Armstrong R, Hall BBJ, Doyle J, Waters E. ‘Scoping the scope’ of a cochrane review. J Public Health. 2011;33(1):147–50. Available from. https://doi.org/10.1093/pubmed/fdr015 .

Download references

Acknowledgements

To Mr. Roberto Martín Melón, Librarian of the Medicine Area of the University of Cantabria, for his collaboration in the literature search process. To Professor Jose Luis Molina of the Autonomous University of Barcelona, and Professor Isidro Maya-Jariego of the University of Seville for their support in consultations during the review work.

Not applicable

Author information

Authors and affiliations.

Department of Nursing, University of Cantabria, Santander, Spain

Rosario Fernández-Peña & Carmen Ortego-Maté

IDIVAL Nursing Research Group, Santander, Spain

SALBIS Research Group, Leon, University of Leon, Leon, Spain

Rosario Fernández-Peña, María-Antonia Ovalle-Perandones, Pilar Marqués-Sánchez & Nestor Serrano-Fuentes

Library and Information Science Department, Universidad Complutense de Madrid, Madrid, Spain

María-Antonia Ovalle-Perandones

NIHR ARC Wessex, School of Health Sciences, University of Southampton, Southampton, UK

Nestor Serrano-Fuentes

You can also search for this author in PubMed   Google Scholar

Contributions

RFP conceived the study. NSF, PMS, and COM contributed to the study conceptualization. MAOP will carry out the search strategy. RFP and NSF will perform the initial screening of the articles and PMS, COM, and MAOP will be involved in the study methodology, screening, and data extraction. All authors have read and approved the final version of this protocol.

Corresponding author

Correspondence to Rosario Fernández-Peña .

Ethics declarations

Ethics approval and consent to participate.

Not applicable. This review does not involve human participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1..

PRISMA-P 2015 Checklist.

Additional file 2.

PRISMA-Flow Diagram.

Additional file 3.

Literature search strategy.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Fernández-Peña, R., Ovalle-Perandones, MA., Marqués-Sánchez, P. et al. The use of social network analysis in social support and care: a systematic scoping review protocol. Syst Rev 11 , 9 (2022). https://doi.org/10.1186/s13643-021-01876-2

Download citation

Received : 04 February 2021

Accepted : 20 December 2021

Published : 10 January 2022

DOI : https://doi.org/10.1186/s13643-021-01876-2

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Health care
• Scoping review protocol
• Social care
• Social networks
• Social Network Analysis
• Social support

Systematic Reviews

ISSN: 2046-4053

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

• Research article
• Open access
• Published: 22 May 2019

The role of social network analysis as a learning analytics tool in online problem based learning

• Mohammed Saqr   ORCID: orcid.org/0000-0001-5881-3109 1 , 2 &
• Ahmad Alamro 3  

BMC Medical Education volume  19 , Article number:  160 ( 2019 ) Cite this article

42k Accesses

65 Citations

14 Altmetric

Metrics details

Social network analysis (SNA) might have an unexplored value in the study of interactions in technology-enhanced learning at large and in online (Problem Based Learning) PBL in particular. Using SNA to study students’ positions in information exchange networks, communicational activities, and interactions, we can broaden our understanding of the process of PBL, evaluate the significance of each participant role and learn how interactions can affect academic performance.

The aim of this study was to study how SNA visual and mathematical analysis can be sued to investigate online PBL, furthermore, to see if students’ position and interaction parameters are associated with better performance.

This study involved 135 students and 15 teachers in 15 PBL groups in the course of “growth and development” at Qassim University. The course uses blended PBL as the teaching method. All interaction data were extracted from the learning management system, analyzed with SNA visual and mathematical techniques on the individual student and group level, centrality measures were calculated, and participants’ roles were mapped. Correlation among variables was performed using the non-parametric Spearman rank correlation test.

The course had 2620 online interactions, mostly from students to students (89%), students to teacher interactions were 4.9%, and teacher to student interactions were 6.15%. Results have shown that SNA visual analysis can precisely map each PBL group and the level of activity within the group as well as outline the interactions among group participants, identify the isolated and the active students (leaders and facilitators) and evaluate the role of the tutor. Statistical analysis has shown that students’ level of activity (outdegree r s (133) = 0.27, p  = 0.01), interaction with tutors (r s (133) = 0.22, p  = 0.02) are positively correlated with academic performance.

Conclusions

Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA could reveal important information about the course, the group, and individual students. The insights generated by SNA may be useful in the context of learning analytics to help monitor students’ activity.

Peer Review reports

Social constructivists view learning as an active construction of knowledge that occurs through social interaction and dialogue among learners. Interaction becomes particularly meaningful when learners are coached in a positive atmosphere and when the students have the chance to argue, debate, and offer alternate perspectives or contribute to ideas [ 1 , 2 ]. Aiming to harness the potentials of the approach, the social constructivist methods have been embraced by several modern pedagogies such as problem and team-based learning that encourage collaboration and promote meaningful interactions among learners. Problem-based learning (PBL) uses problems as triggers to facilitate discourse and interaction among students, the discussions occur in small groups and are facilitated by teachers [ 3 ]. The PBL process is structured in a way to help students elaborate and activate previous information [ 3 , 4 ].

Online problem-based learning requires students to engage in active discussions in two types of dialogical spaces; the content and the relational spaces. Learners interact in the content space towards the goal of acquiring a deeper understanding of the domain of knowledge; activities include collecting information, discussing concepts and proposing solutions to the problem [ 5 , 6 , 7 ]. Communicative activities in the relational space deal with the interpersonal relations and interactions among collaborators. The primary goal of these interactions is to reach a common understanding of the concepts and content under discussion [ 5 , 8 ].

There is overwhelming evidence of the value of learners’ interaction with peers, teachers, and the content, which is widely recognized as a pivotal ingredient of modern education [ 9 , 10 ]. However, offering the learners the opportunity to interact does not directly translate to effective interactions and working online together does not mean collaboration [ 6 , 11 , 12 ]. Moreover, online collaboration requires instructional support, scaffolding by teachers, active coordinating of collaboration dynamics, engagement of learners in a stimulating environment [ 11 , 13 ], which begs the need for a mechanism to monitor the efficiency of online interactions and design a data-driven intervention that supports effective collaboration.

Social network analysis

Social network analysis (SNA) is a collection of methods and tools that could be used to study the relationships, interactions and communications. As such, SNA is suitable for the study and possibly monitoring of online interactions as it can automatically analyze interaction data, bringing a bird-eye view of the group social structure, the interaction patterns, as well as the mapping of all communications in the relational space [ 14 , 15 , 16 , 17 ]. SNA can be implemented in two main ways, visualization, and mathematical analysis.

SNA visualization renders relationships between actors in social networks by graphs known as sociograms; the sociogram portrays actors (nodes in SNA terms) as points, and relationships (edges in SNA terms) as arrows originating from the source of the interaction and pointing to the target of interaction [ 17 ]. SNA may help visualize the interactions among participants and may reveal who are the important actors in the interactions and who are the isolated actors, what are the groups that shows dense interactions or sparse interactions that may need support. It may also reveal the active moderators who are participating and interacting with students, and the extent of their interactions. PBL is a collaborative learning where small groups interdependently work together and as such evaluation of collaborative interactions is important.

The SNA mathematical analysis quantifies network parameters on individual actor levels, as well as group level. The mathematical analysis of SNA uses graph theory concepts to calculate metrics representing the nodes, the links or the network. Such as the distance to other actors in the network, the number of interactions with other actors, or how many times it bridged interactions between communities. These metrics are important in quantifying interactions, ranking nodes, or relationships. Parameters calculated at the actor level are called centrality scores, these scores are measures of the node position or importance in the social network. Since there are different contexts and probably different ways to consider the role important, there are different centrality scores. The measures chosen for this study are centrality measures of role and position in information exchange [ 17 ]. For example: number of received interactions are quantified as in-degree centrality, number of contributed interactions is called out-degree centrality. These parameters can add to our portfolio of information about students and might extend to learning analytics [ 14 , 15 ]. In the context of PBL, one would be interested in the interactivity of participants, and their groups, and how these metrics could tell us about online PBL.

Previous research has indicated the utility of SNA in the study of collaborative learning; J Zhang and J Zhang [ 18 ] used SNA to diagnose gaps in problem solving and knowledge construction. Their findings highlighted problems such as students with low levels of interaction, the presence of few central students to the exchange of information. A Bakharia and S Dawson [ 14 ] have shown how to use SNA to map the online interactions among groups of students and identify the communities of collaborators. They were also able to recognize influential and isolated students who might need to be stimulated or supported. M Saqr, U Fors and M Tedre [ 19 ] used social network analysis to monitor collaborative groups and create a data-driven intervention that results in a meaningful improvement in the way students collaborate. Previous research results in learning analytics are promising, and some studies have given an indication of the potential of SNA centrality measures in predicting students’ performance [ 20 , 21 ].

There is an apparent gap of the studies about the interactions in online PBL, particularly the relational and communicative activities. Most studies about the interactions in PBL targeted the content dimension through effortful resource intensive methods such as interviewing the learners, recording, and coding of PBL sessions or text mining techniques. Other researchers have used indirect examination and exploration using surveys and open-ended questionnaires [ 7 , 22 ]. Such methods are practically demanding as well as challenging to standardize and difficult to implement by instructors [ 23 , 24 ]. It is reasonable to argue that there is a need for mechanisms to ensure that online PBL meets the required curricular goals and objective and is actually collaborative. The methods need to be automatic, easy to implement and offer meaningful information that can help stakeholders improve the PBL process. In this study, we investigate the use the potentials of SNA in supporting learning and teaching. We have considered both visualization and mathematical analysis. Since the PBL process has three important aspects: the student, the actor and the group, we have used these aspects as the units of analysis.

The research question of this study can be formulated as follows:

What can SNA visual and mathematical analysis tell about PBL?

What interaction parameters as measured by SNA associated with better performance?

The study was designed as an exploratory case study; a case study design allows an in-depth investigation of a case (Online PBL course in our study) from different perspectives.

Participants

The study included data from 135 first year students (42 females and 93 males) and 15 tutors (10 males and 5 females), three students did not sign the ethical approval and 11 students dropped out of the course and so their data were not included. The course chosen was the Growth and Development of the year 2017, attended by first year students, the course is the third course in the year. The course covers issues of growth and development from different aspects, including anatomy, physiology and embryology. By the third course, students should have acquired a fair experience of the educational system and practiced online PBL for three months. The course duration is around 45 days, within them four weeks of PBL sessions were completed by all students.

Qassim College of Medicine, Saudi Arabia has introduced PBL since 2001. The college adopted the seven jumps approach, student is expected to attend two sessions physically, one at the beginning of the week and another at the end, in between these sessions, students are offered an online forum where they continue to interact. The interactions start on the first day of the week by posting the learning issues of the weekly problem. Students are encouraged to discuss the learning issues, share information, collaboratively construct the required knowledge and work towards achieving the goals and the objectives of the PBL problem. By the last day of the week, each group is expected to wrap up what they have learnt together. The online discussions are assigned to the same students in the physical group and facilitated by the same tutors [ 7 ].

The PBL represents the backbone of the educational system at the medical college and the objectives of the PBL sessions are written to cover the course objectives. The lectures, seminars and other educational activities are designed to support students understand the problem used in the PBL. The students are evaluated by multiple choice questions (MCQ), modified essay questions (MEQs) and Short-answer Essay Questions (SEQ) as well as practical exams. The grades considered for this study are only the sum of MCQ, MEQ and SEQ as they are designed to test students’ knowledge acquisition of PBL objectives and the exam blueprint is designed according to these objectives. The mean difficulty index of MCQs was 0.67, the mean discrimination index was 0.32 and the reliability index was 89.2%.

Data collection

Interaction data were extracted from the learning management system. Custom Structured Query Language (SQL) queries were used to extract interaction data along with attributes of students and properties of posts, the data extracted for each post were the id of the sender, the id of the receiver, time the post was written, time it was modified, the content of the post, and forum and thread IDs. For each user we extracted user Id, role, PBL group and the posts he contributed. All participant IDs were removed to protect their confidentiality and re-coded, so the students labeled as S1 to S135 and the tutors were recoded as T1 to T15.

SNA analysis

The analysis of SNA was done using Gephi version 9.2. Gephi is an open source application that has the capability to visually and mathematically analyze social networks through a graphical user interface. It can import a range of formats as well as export analysis data in an easy to use format for analysis [ 25 ].

SNA visual analysis

The course network, as well as group networks, were analyzed and studied visually. Visualization was implemented to describe the general properties of the course network, such as the interactivity level, participants’ contributions, who is participating and identify the types of communications. The same was performed for each PBL group with special emphasis on the student-student and student tutor communications.

SNA mathematical analysis

Mathematical analysis of participants’ interactivity may shed lights on important aspects such as interactivity and information exchange. As such, mathematical analysis was done at two levels, Individual students’ level (student and tutor), and group level, since they are the important factors in the PBL process.

Student level

For each student, we collected the following parameters: Type of interactions (student-student, student-tutor, and tutor-tutor). We also calculated centrality measures that are relevant to interactions and role in information exchange:

Indegree centrality: the number of posts that are directed to a user, such as replies. A user receives more replies to contribution if the contribution is noteworthy, useful or arguable. As such in-degree centrality in this context reflect the importance of a user contribution as voted by other users. Out-degree centrality: the frequency of posts made by the user and reflects the activity of the user in the discussions.

Degree centrality: is the sum of indegree and outdegree centralities and reflects the overall activity of the user

Betweenness centrality: is the frequency a user connected two other unconnected users, or lied in-between their interactions. As such, it may be viewed as a measure of bridging others and helping communications among interacting participants.

Closeness centrality: reflects the distance between a user and all other users in the group, a user with high closeness centrality can be easily reachable by all others.

As this article is beyond the discussion of the theory, mathematics and concepts of centrality measures, interested readers can have further details in this article [ 26 ].

Group level

For each group, we collected the following parameters: The total number of interactions and type (student-student, student-tutor, and tutor-tutor), average in-degree centrality (average in-degree of all group members), average degree centrality (average degree of group members).

Statistical analysis

Statistics were performed using Paleontological Statistics Software Package for Education and Data Analysis version 3.2. Correlation among variables was performed using the non-parametric Spearman rank correlation test [ 27 ].

Descriptive statistics

The course included 15 PBL groups; each group had a range of 6 to 12 students with one tutor. There were six discussion forums: five PBL discussion forums and a news forum (for course announcements, data of the news forum were excluded from the study. The course had 2620 interactions, mostly from students to students (89%), students to teacher interactions were 4.9% and teacher to student interactions were 6.15%. The range of interactions in each group was 48 to 332 (mean 216, SD 81.5). The picture was also similar on the course level; students dominated the interactions. Some groups were highly interactive such as groups 150, 144 and 149 and some were not such as groups 146, 140 and 141. A breakdown of each interaction type is presented in the course is presented in Table 1 for group level.

RQ1: what can SNA visual and mathematical analysis tell about PBL?

SNA can be used to map the relationships among actors, groups (visual analysis) and calculate the mathematical metrics of interactions and actors. The visual analysis was used to explore its role in shedding lights on the individual actors and their groups and how could this help learners or teachers. Furthermore, the mathematical analysis was used to quantify the interactions on the same levels, to demonstrate the utility of quantifications of interactions and how it compares to visual analysis.

Visual analysis

SNA visual analysis can be used on different levels, in our case three levels are relevant: the whole course level, the PBL group level and individuals’ level. Following is a demonstration of what each level can reveal visually.

On course level

One of SNA biggest advantages is the ability to sum up and map all interactions, as such it offers a bird-eye view of the relational dynamics in a course or a group. In the next Fig. 1 , SNA rendered each PBL separately and identified each group, a property known as finding communities . This property is useful in finding the group of members who frequently interact with each other. The Figure shows each PBL group and the interactions among participants. Active groups can be recognized; an example is group A, B, and C; the three groups have dense interactions among participants. Groups D, E, and F, have a moderate level of activity; while G and H groups are less active with very few interactions among participants. Using such method may help pick the groups that need attention, a focused review or support.

Interactions in all groups. Each circle represents an actor, each arrow represents an interaction, groups were coloured with unique colours to be easy to distinguish

On group level

While the insights from the whole course network can be helpful to have a course overview, a focus on each group separately can demonstrate deeper information. In each group, the visual analysis using SNA can reveal the patterns of the interactions occurring in the group and how the students and the tutor perform; which are relevant factors for the evaluation of the PBL process. To demonstrate the potential of SNA and how much insight it can give, we present three Figures, in Fig. 2 , group (A) is visually rendered in a sociogram. Regarding participants’ roles, it can be noted that student S83, S84, S15 are the most active students, while S110, S98 are the least active and are relatively isolated. The interactions are mostly occurring among students S15, S101, S84, S106 and the tutor. The role of the tutor who is coded as T150 can also be recognized, the node size indicates a moderate level of activity, and the arrows directions are indications that he received many interactions as well as communicated back with most students, the dark node color is an indication that his role was mostly moderating discussions among students. It is reasonable to say here that the information about the tutor was assuring that that tutor acted as expected from him. Regarding the group, the multiple interactions are an indication of an active group with diverse participation among most participants. To summarize, the picture SNA is rendered in Fig. 2 , a picture of an active group with many active students, and few inactive students S110 who might need more attention from the tutor.

Interactions in group A. The node size was configured as degree centrality to reflect participants’ activity. The edges were configured so that the more frequent the interactions between two participants are, the thicker the line will be. The colour intensity reflects role in moderating interactions

In contrast to the previous group, the next group (I) shows far lower level of activity. However, as shown in Fig. 3 , student S5 is the most active participant, less so are students s58, S61 and S68, the remaining students are mostly inactive, the tutor role was very limited and made a single interaction with S5. Apparently, such group requires attention and the tutor might be contacted to play a more active role in the group, to stimulate students and promote interactions.

Interactions in group I. The node size was configured as degree centrality to reflect participants’ activity. The edges were configured so that the more frequent the interactions between two participants are, the thicker the line will be. The colour intensity reflects role in moderating interactions

On individual level

On the individual level, SNA can highlight the network of individual users. For instance, we can have a student of interest that we would like to see his performance. For that situation, we can isolate his network in what is known as an ego network. To demonstrate this, we demonstrate the ego network of the tutor in another group, namely the ego network of the tutor of group I in Fig. 4 . Although the group included 11 students, the tutor had only five students who communicated with him. A look at the full network in the side image, we can see that the students who needed help the most (the less active) were not helped, this is a clear example of an opportunity where an improvement can be made, by alerting the tutor to diversify his interactions and focus on the isolated students.

The tutor ego network compared to the whole network showing the tutor has interacted with a limited number of students. The node size was configured as degree centrality to reflect participants’ activity. The edges were configured so that the more frequent the interactions between two participants are, the thicker the line will be. The colour intensity reflects role in moderating interactions

Mathematical analysis

On the course level.

Mathematical analysis offers a quantification of interactions and roles which may enable accurate comparison of students and groups. It is also potentially useful as a measurement that can be used to forecast performance. The descriptive properties of participants in our study were as follows: the average outdegree centrality was 17.47, which means each participant has contributed with an average of 3.5 posts/week during the five weeks duration of the course, which is an indication of a relatively interactive course. The centrality measures of information exchange were also acceptable, the closeness centrality normalized score was 0.66, meaning that most students had moderate to high reachability. Same parameters can be obtained for the group and they are presentenced in the next section.

Further details are in Table 2 .

On the group and individual level

As a demonstration, the mathematical parameters of a student are summarized in Fig.  5 , compared to own group. The student, were actively contributing (outdegree 50) which is average 10 posts every week, received 27 replies (indegree 27), an average of 5.4 posts/week, an indication of the interest of peers to reply to the posts. Furthermore, closeness centrality is high which means he was close in the discussions to all other collaborators. However, betweenness 28.8 centrality is high, an indication that the student has connected and bridged interactions with others. The parameters of the group were also listed on the side to see how the student compared to others in the same group. The parameters were relatively high (high indegree, high outdegree, high closeness and betweenness centralities). One can reach the conclusion that that this was an active student in an active group.

A social network profile model

Social profile

To sum up a student or a user activity, we propose using SNA as a monitoring tool that displays the activity of a use (visual and mathematical) compared to own group for the teachers to use. The social network profile concept we propose can be applied in a learning management system dashboard to show social profile, connections and parameters. To demonstrate this, we chose a student from group A, showing ego network, the activity level and parameters for clarity, we also compare the activity to the group side by side. Comparison to his group will show if his activity level is proportional to colleagues or not and will highlight the other participants’ role. In case there is an unsatisfactory level of activity, one can understand the reason.

RQ2 do interaction parameters - as measured by SNA - correlate with better performance?

To see which of the SNA factors might correlate with better achievement, we categorized the parameters into three main groups: personal, group and tutor factors. The results indicated that outdegree centrality of a student, being in a group with higher average grade, communicating with the tutors, are the factors most correlated with better learning. The full details of correlation coefficient are displayed in Tables 3 .

This study was done to evaluate the role SNA can be used to evaluate online PBL and offer insights to administrators, tutors, and students. We have looked at the visual analysis and the quantitative interaction statistics. The type of interaction based on the involved participant whether he is a tutor or a student was considered. We also used role classification to try to understand the dynamics of intragroup interactions.

On the quantitative level, SNA interaction analysis offered simple, easy to interpret indicators for the level of activity of students and tutors; these indicators helped identify the active groups, tutors, and students alike. The indicators also helped flag a potentially dysfunctional or inactive group and the inactive student or a disengaged tutor. The SNA measures of role and position in information transfer highlighted qualities that can’t be easily recognized by the interaction parameters (number of posts). For instance, betweenness centrality reflected the role of students in moderating information, connecting the unconnected students and brings them into the discussion. Closeness centrality pointed to students who were not easily engaged in the discussions.

When coupled with visual analysis, it gave further insights into the dynamics of the interactions in the groups, such as who was active, who were the students interacting together and highlighted the isolated students. The role analysis we have performed in our study added another dimension to the utility of SNA. Identifying each role and who plays it enables tutors to understand dynamics in the group, helps motivate discussions and mitigate conflict or prevent dysfunction in interacting groups as well as appreciate different points of view [ 28 , 29 ].

Studies of interaction analysis in online PBL are scarce, our review of the literature identified one study about the analysis of problem-solving online discussions, which is apparently not precisely online PBL [ 30 , 31 ]. Another study by Saqr et al. that focused on using social network metrics as proxy leaning analytics indicators to predict performance using machine learning methods and advanced modelling techniques. The importance of our study is that it offers a practical monitoring solution that can be implemented and interpreted by different stakeholders, the insights generated have the potential to improve many aspects of the online PBL, and at times help intervention in a dysfunctional group or a disengaged student [ 32 ]. SNA based intervention has proved practical and effective in bringing tangible results that could support teaching and learning in other settings and such methods are similarly applicable in PBL [ 32 ].

While content analysis might have added insights about the exchanged information in online PBL, it is resource intensive and requires long, exhaustive work. Since content analysis is usually done manually, it is not a practical choice for automatic monitoring of thousands of interactions. Content analysis has also been criticized for being subjective and difficult to standardize [ 33 , 34 , 35 ]. Educational data mining (EDM) offers a potential alternative as it can automatically analyze text in the real-time. Nonetheless, EDM is impractical beyond research purposes since the instruments and frameworks used are difficult to use by teachers. The most challenging hindrance we found in the context of online PBL was the absence of context-specific tools since most of the available tools are from outside the field of education, a problem Alejandro [ 36 ] stated as “Many of the EDM researchers are in reality users of Data mining frameworks and tools. Because student modeling commands the focus of more than half the approaches.”

While our research was feasible due to the availability of data recorded by the learning management system and the relative ease of obtaining it, a similar approach can be extended to include the face-to-face PBL. The modern voice recognition techniques can accomplish it, since transcribing tools that are becoming more accurate, cheaper and easier to implement. The implementation would require more discussion about what to transcribe, what to store and how to handle this data.

We think that our study offered a demonstration of the potentials of SNA as a tool for interaction analysis of online PBL. We hope that this study can kindle the discussions about the need for automatic monitoring of PBL. We hope that future research can examine, improve or critique our approach. An obvious future approach to online PBL could couple learning analytics methods with SNA methods.

While this study has shed lights on the communicative and relational aspects of the online PBL, it is not without limitations. We recognize the limitation and the need for a reliable content analysis method. An automatic EDM method that is designed to capture the PBL elements and give a credible insight into the content of the discussions. We also recognize that our results might need further confirmation in other contexts to advance our understanding of the process, especially different implementations of online PBL.

Social network analysis is a practical method that can reliably monitor the interactions in an online PBL environment. Using SNA can reveal important information about the course, such as the general activity and the active groups. On the group level, it renders a more detailed picture about the group and the participants. Thus, helping to identify the active, inactive or isolated students and tutors alike. Using mathematical parameters can also help add insights about the level of activity in a precise way. Furthermore, the insights generated by SNA can be useful in the context of learning analytics to help monitor students’ activity.

Abbreviations

Educational Data mining

Multiple Choice Questions

Modified Essay Questions

Problem Based Learning

Research Question

Standard Deviation

Short-answer Essay Questions

Social Network Analysis

Structured Query Language

Woo Y, Reeves TC. Meaningful interaction in web-based learning: a social constructivist interpretation. Internet High Educ. 2007;10(1):15–25.

Article   Google Scholar  

Michael Nussbaum E. Collaborative discourse, argumentation, and learning: preface and literature review. Contemp Educ Psychol. 2008;33(3):345–59.

Neville AJ. Problem-based learning and medical education forty years on. Med Princ Pract. 2009;18(1):1–9.

Schmidt HG, Rotgans JI, Yew EHJ. The process of problem-based learning: what works and why. Med Educ. 2011;45(8):792–806.

Slof B, Erkens G, Kirschner PA, Jaspers JG, Janssen J. Guiding students’ online complex learning-task behavior through representational scripting. Comput Hum Behav. 2010;26(5):927–39.

Barron B. When smart groups fail. J Learn Sci. 2003;12(3):307–59.

Alamro AS, Schofield S. Supporting traditional PBL with online discussion forums: a study from Qassim medical school. Medical teacher. 2012;34(sup1):S20–4.

Janssen J, Bodemer D. Coordinated computer-supported collaborative learning : awareness and awareness tools. Educ Psychol. 2013;48(December 2014):40–55.

Borokhovski E, Bernard RM, Tamim RM, Schmid RF, Sokolovskaya A. Technology-supported student interaction in post-secondary education: a meta-analysis of designed versus contextual treatments. Comput Educ. 2016;96:15–28.

Bernard RM, Abrami PC, Borokhovski E, Wade CA, Tamim RM, Surkes MA, Bethel EC. A meta-analysis of three types of interaction treatments in distance education. Rev Educ Res. 2009;79(3):1243–89.

Laal M, Naseri AS, Laal M, Khattami-Kermanshahi Z. What do we achieve from learning in collaboration? Procedia - Social and Behavioral Sciences. 2013;93(2012):1427–32.

Kreijns K, Kirschner PA, Jochems W. Identifying the pitfalls for social interaction in computer-supported collaborative learning environments: a review of the research. Comput Hum Behav. 2003;19(3):335–53.

Abrami PC, Bernard RM, Bures EM, Borokhovski E, Tamim RM. Interaction in distance education and online learning: using evidence and theory to improve practice. J Comput High Educ. 2011;23(2–3):82–103.

Bakharia A, Dawson S: SNAPP: a Bird’s-eye view of temporal participant interaction. Proceedings of the 1st international conference on learning analytics and knowledge - LAK ‘11 2011:168–168.

de Laat M, Lally V, Lipponen L, Simons R-J. Investigating patterns of interaction in networked learning and computer-supported collaborative learning: a role for social network analysis. Int J Comput-Support Collab Learn. 2007;2(1):87–103.

Rabbany R, Takaffoli M, Zaïane O. Analyzing participation of students in online courses using social network analysis techniques. Eindhoven: Proceedings of educational data mining: 2011; 2011. p. 21–30.

Borgatti SP, Mehra A, Brass DJ, Labianca G. Network analysis in the social sciences. science. 2009;323(5916):892–5.

Zhang J, Zhang J. A case study on web-based knowledge construction in Moodle platform. In: Computer science and education (ICCSE), 2010 5th international conference on, vol. 2010. Hefei: IEEE; 2010. p. 1110–4.

Saqr M, Fors U, Tedre M. How the study of online collaborative learning can guide teachers and predict students' performance in a medical course. BMC Med Educ. 2018;18(1):24.

Saqr M, Fors U, Tedre M. How learning analytics can early predict under-achieving students in a blended medical education course. Med Teach. 2017;39(7):757–67.

Lockyer L, Heathcote E, Dawson S. Informing pedagogical action: aligning learning analytics with learning design. Am Behav Sci. 2013;57(10):1439–59.

Azer SA, Azer D. Group interaction in problem-based learning tutorials: a systematic review. Eur J Dent Educ. 2015;19(4):194–208.

De Wever B, Schellens T, Valcke M, Van Keer H. Content analysis schemes to analyze transcripts of online asynchronous discussion groups: a review. Comput Educ. 2006;46(1):6–28.

Calvani A, Fini A, Molino M, Ranieri M. Visualizing and monitoring effective interactions in online collaborative groups. Br J Educ Technol. 2010;41(2):213–26.

Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Icwsm. 2009;8:361–2.

Google Scholar  

Das K, Samanta S, Pal M. Study on centrality measures in social networks: a survey. Soc Netw Anal Min. 2018;8(1):13.

Hammer Ø, Harper DA, Ryan PD. PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron. 2001;4(1):9.

Kollar I, Fischer F, Hesse FW. Collaboration scripts – a conceptual analysis. Educ Psychol Rev. 2006;18(2):159–85.

Marcos-García J-A, Martínez-Monés A, Dimitriadis Y. DESPRO: a method based on roles to provide collaboration analysis support adapted to the participants in CSCL situations. Comput Educ. 2015;82:335–53.

Hou H-T, Chang K-E, Sung Y-T. Analysis of problem-solving-based online asynchronous discussion pattern. J Educ Technol Soc. 2008;11(1):17–28.

Saqr M, Fors U, Nouri J. Using social network analysis to understand online problem-based learning and predict performance. PLoS One. 2018;13(9):e0203590.

Saqr M, Fors U, Tedre M, Nouri J. How social network analysis can be used to monitor online collaborative learning and guide an informed intervention. PLoS One. 2018;13(3):e0194777.

Romero C, Ventura S. Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2013;3(1):12–27.

Papamitsiou ZK, Economides AA. Learning analytics and educational data Mining in Practice: a systematic literature review of empirical evidence. Educational Technology & Society. 2014;17(4):49–64.

Al-Razgan M, Al-Khalifa AS, Al-Khalifa HS. Educational data mining: a systematic review of the published literature 2006-2013. In: Herawan T, Deris MM, Abawajy J, editors. Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013). Singapore: Springer Singapore; 2014. p. 711–9.

Chapter   Google Scholar  

Peña-Ayala A. Educational data mining: a survey and a data mining-based analysis of recent works. Expert Syst Appl. 2014;41(4):1432–62.

Download references

Acknowledgements

Not applicable

There was no funding source to report for this research.

Availability of data and materials

The data used in this can be obtained upon request from the corresponding author Mohammed Saqr after approval of the ethical approval body of the university. The data cannot be shared publicly because it contains private information and student’s grade data that if released would compromise articles 5, 6,7,10 of Data usage and protection policy section of the Qassim College of Medicine Privacy Policy and User Agreement.

Author information

Authors and affiliations.

School of Computing, University of Eastern Finland, Joensuu Campus, Yliopistokatu 2, P.O. Box 111, fi-80100, Joensuu, Finland

Mohammed Saqr

Department of Computer and System Sciences (DSV), Stockholm University, Borgarfjordsgatan 12, PO Box 7003, SE-164 07, Kista, Sweden

College of Medicine, Qassim University, Qassim, Kingdom of Saudi Arabia

Ahmad Alamro

You can also search for this author in PubMed   Google Scholar

Contributions

MS: Made significant contributions to the conception, design, data acquisition, analysis, interpretation and reporting of the results as well as drafting and revising the manuscript. Moreover, agree to be accountable for all aspects of the work. AA: Made significant work in the design of the study, analysis of the data, drafting and revising it critically for important intellectual content, He also approved the version to be published and agree to be accountable for all aspects of the work. Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohammed Saqr .

Ethics declarations

Ethics approval and consent to participate.

The study was granted an approval by the Medical Research Ethics Committee of Qassim University College of Medicine. The participants in this study agreed to participate by electronically signing the participation agreement and the College Data Privacy Policy that details user rights and protection guarantees. All data in this study were anonymized, and private data were excluded. College Privacy guidelines and policies of dealing with students’ data were strictly followed.

Consent for publication

Not applicable.

Competing interests

The authors have no competing interests to report.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Cite this article.

Saqr, M., Alamro, A. The role of social network analysis as a learning analytics tool in online problem based learning. BMC Med Educ 19 , 160 (2019). https://doi.org/10.1186/s12909-019-1599-6

Download citation

Received : 01 January 2019

Accepted : 08 May 2019

Published : 22 May 2019

DOI : https://doi.org/10.1186/s12909-019-1599-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Social network analysis, problem-based learning
• Blended learning, blended problem-based learning
• Learning analytics

BMC Medical Education

ISSN: 1472-6920

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Open access
• Published: 22 July 2019

Thematic series on Social Network Analysis and Mining

• Rodrigo Pereira dos Santos 1 &
• Giseli Rabello Lopes 2  

Journal of Internet Services and Applications volume  10 , Article number:  14 ( 2019 ) Cite this article

5945 Accesses

2 Citations

4 Altmetric

Metrics details

Social networks were first investigated in social, educational and business areas. Academic interest in this field though has been growing since the mid twentieth century, given the increasing interaction among people, data dissemination and exchange of information. As such, the development and evaluation of new techniques for social network analysis and mining (SNAM) is a current key research area for Internet services and applications. Key topics include contextualized analysis of social and information networks, crowdsourcing and crowdfunding, economics in networks, extraction and treatment of social data, mining techniques, modeling of user behavior and social networks, and software ecosystems. These topics have important areas of application in a wide range of fields, such as academia, politics, security, business, marketing, and science.

1 Introduction

This Thematic Series of the Journal of Internet Services and Applications (JISA) presents a collection of articles around the topic of Social Network Analysis and Mining (SNAM). From advances in Computer Science research and practice, the field of SNAM has become an important subject due to (i) the large amount and diversity of data that could be analyzed, (ii) the capacity of processing and solving complex analysis with efficiency, (iii) the development of new solutions for visualization of complex networks, and (iv) the application of SNAM concepts in different domains.

The study of social networks was leveraged by the social, educational and business communities. Academic interest in this field has been growing since the mid twentieth century [ 1 ], given the increasing interaction among people, data dissemination and exchange of information. In this scenario, big data sets require more accurate analyses. As such, the development and evaluation of new techniques for social network analysis and mining (SNAM) is a current key research area for Internet services and applications. These topics have important areas of application in a wide range of fields.

A social network is composed of actors who have relationships with each other. Networks can have a few to many actors (nodes) and one or many types of relationships (arrows) between pairs of actors [ 2 ]. In our daily life, we have several practical examples of social networks: our family, friends, and colleagues from the university, gym, work, or casual meetings. Individuals and organizations – seen as nodes in social networks – can be connected due to several reasons, such as friendship and genealogy, but also values, visions, ideas, finances, disagreements, conflicts, services, computer networks, air routes etc. The structure created from such a large amount of relationships is complex. Therefore, researchers study the network as a whole from a sociocentric view (all the links referring to specific relations in a given population), or as a social structure in an egocentric view (with links selected from specific people) [ 3 ].

In addition, people join and create groups in any society [ 4 ], but the web platform fostered critical changes in the way people can interact and think about the reality. Interactions (i) become easier, (ii) allow a frequent exchange of information, and (iii) transform communications tools and social media (e.g., microblogs, blogs, wikis, Facebook) to mass communication means that are more agile and far-reaching. As such, the use of social media contributes to the sharing of different types of information, especially in real time. Some examples are personal data, location, opinions and preferences. In this context, SNAM can support the understanding of preferences and associations, the identification of interactions, the recognition of influences, and the comprehension of information flow (context and concepts) among network actors.

Finally, the understanding of interactions in a specific scenario can produce concrete results. In an organization, employees should work to avoid problems regarding knowledge sharing [ 5 ]. In natural science, social networks can aid in the study of endemies and epidemies propagation [ 6 ]. In marketing, SNAM can be used as a tool for brand spread, or for the study of a market segment towards the understanding of how information propagates [ 7 ]. The last (but not least) example is the use of SNAM for the identification of criminal networks [ 8 ].

This JISA Thematic Series originates from the 6th Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2017) that was held in São Paulo, Brazil, on July 04–05, 2017. BraSNAM 2017 was affiliated with the 37th Brazilian Computer Society Congress (CSBC 2017) which is the official event of the Brazilian Computer Society (SBC). BraSNAM is focused on bringing together researchers and professionals interested in social networks and related fields. The workshop aims at providing innovative contributions to the research, development and evaluation of novel techniques for SNAM and applications. Finally, the main goal is to provide a valuable opportunity for multidisciplinary groups to meet and engage in discussions on SNAM.

Continuing in this direction, this JISA Thematic Series targets new techniques for the field of SNAM, mainly fostered by the context of Internet services and applications. We received contributions at various levels: from theoretical foundations to experiments and case studies based on real cases and applications; from modeling to mining and analysis of big data sets; and from different subjects and domains, such as entertainment, public transportation, elections, and personal social circles.

This Thematic Series presents high-quality research and technical contributions. We received six submissions as extended versions of the best papers of BraSNAM 2017. Topics included: analysis of online discussion and comments, complex networks, graph mining, government open data, power metrics, community detection, link assessment, homophily, and sentiment analysis. The five out of six submissions that were selected for publication and appear in this issue are summarized in the following section.

2 The papers

Loures et al. [ 9 ] investigate the potential that online comments have to describe television series. The authors implement and evaluated several different summarization methods. Their results reveal that a small set of comments can help to describe the corresponding episodes and, when taken together, the series as a whole.

Caminha et al. [ 10 ] use graph mining techniques for the detection of overcrowding and waste of resources in public transport. The authors propose a new data processing methodology for the evaluation of collective transportation systems. The results show that their approach is capable of identifying global imbalances in the system based on an evaluation of the weight distributions of the edges of the supply and demand networks.

Verona et al. [ 11 ] propose metrics for power analysis on political and economic networks based on a sociology theory and network topology. The authors present a case study using a network built on data from Brazilian Elections about electoral donations explaining how the metrics can help in the analysis of power and influence of the different actors (corporations and persons) in this network.

Leão et al. [ 12 ] propose a method to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random relationships, the authors observe that social networks converge to a topology with more pure social relationships and better quality community structures.

Finally, Caetano et al. [ 13 ] propose an analysis of political homophily among Twitter users during the 2016 US presidential election. Their results showed that the homophily level increases when there are reciprocal connections, similar speeches or multiplex connections.

3 Paper selection process

The paper selection process was run during 2018 and the papers were published as soon as they were accepted and online-first versions became ready. Each submission went through two to four revisions before the final decision. We invited leading experts who are international researchers in the field of SNAM and related topics to form this Thematic Series’ editorial committee. All manuscripts were reviewed by at least three members of this editorial committee. Guest editors checked the new version produced after each review cycle in order to decide whether the authors carefully addressed the reviewers’ comments. Otherwise, a further review cycle was requested by the guest editors. The papers were reviewed by a total of 19 reviewers. The names of the editorial committee members are listed on the acknowledgements of this editorial.

4 Conclusion

The future of research and practice in the field of SNAM is challenging. Opportunities are many: theoretical and applied research has been published in specific conferences and journals, but also in traditional venues since it requires a multidisciplinary arrangement. Based on the papers accepted to this Thematic Series, we can highlight some research gaps. For example, Loures et al. [ 9 ] point out the challenge of abstractive summarization for online comments : it is usually a much more complex task than the extractive one, since it requires a natural language generation module and a domain dependent component to process and rank the extracted knowledge. In turn, Caminha et al. [ 10 ] point out the need for a simulator for reproducing the dynamics of human mobility through the bus system in the case of a large metropolis. In this context, the use of data mining to estimate probability can represent the current demand for a bus system.

Regarding applications of SNAM in the context of presidential elections, Verona et al. [ 11 ] point out the challenge of redesigning the power metric to show relative values inside the network, instead of big absolute values. Moreover, information about company owners should be integrated in order to reveal hidden connections behind donations and politicians. In turn, Caetano et al. [ 13 ] point out the need for further investigation on the temporal political homophily analysis correlating it with external events that may have influenced the users’ sentiments. This effort can allow user classification through data mining techniques to identify candidates’ advocates, political bots, and other actors. Finally, regarding community detection, Leão et al. [ 12 ] point out the challenge of adopting different approaches for community detection , consider additional algorithms to explore temporal aspects or identify overlapping communities, and evaluate filtered networks. Moreover, different alternatives to measure the strength of ties should be investigated.

In the end, this Thematic Series comes out with some meaningful over-arching results:

SNAM researchers and practitioners recognize the importance of sentiment analysis for in the identification of conflicts and agreements, as well as social trends and movements, in different domains. As such, new methods and techniques should be developed based on the large set of existing empirical studies on this topic;

The dynamic nature of social networks makes community detection somehow a hard work. Different algorithms exist and are many. However, the treatment of randomness and noise in social relations requires further investigation. In addition, the assessment of those relations over time is also a topic of interested in SNAM;

Another challenge in the area is the understanding of social power and the way it manifests in social networks. In this context, power is tightly related to the notion of influence and authority. Research can vary from the development and use of SNAM algorithms and tools to the theorization based on qualitative studies (e.g., case studies, ethnography, sociotechnical approaches);

SNAM opens opportunities to investigate different types of systems, such as (i) systems-of-systems: a set of constituent software-intensive systems that are managerially and operationally independent, and present some emergent behavior and evolutionary development (e.g., smart cities, transportation, air space, flood monitoring), and (ii) software ecosystems: a set of actors and artifacts as well as their relations over a common technological platform (e.g., iOS, Android, Eclipse, SAP);

Finally, SNAM can support research on new trends of collaborative systems, such as crowdsourcing, free and open source software development, accountability, transparency and community engagement. A common interest lies on how to improve information visualization and recommendation based on actors’ characteristics and behaviors as well as the changes in their relations over time.

Berkowitz SD. An introduction to structural analysis: the network approach to social research. Toronto: Butterworth; 1982.

Google Scholar  

Wasserman S, Faust K. Social network analysis: methods and applications. Cambridge: Cambridge University Press; 1994. p. 1994.

Book   Google Scholar  

Hanneman RA, Riddle M. Introduction to social network methods. Riverside, CA: University of California, Riverside; 2005.

Castells, M., 2000, The Rise of the Network Society (The Information Age: Economy, Society and Culture, Volume 1, 2nd ed Wiley-Blackwell.

Studart RM, Oliveira J, Faria FF, Ventura LVF, Souza JM, Campos MLM. Using social networks analysis for collaboration and team formation identification. In: Proceedings of the 15th international conference on computer supported cooperative work in design, Lausanne; 2011. p. 562–9.

Mikolajczyk RT, Kretzschmar M. Collecting social contact data in the context of disease transmission: prospective and retrospective study designs. Soc Networks. 2008;30(2):127–35.

Article   Google Scholar  

Kempe D, Kleinberg J, Tardos É. Maximizing the spread of influence through a social network. In: Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining. Washington, D.C., USA; 2003.

Svenson P, Svensson P, Tullberg H. Social network analysis and information fusion for anti-terrorism. In: Proceedings of the conference on civil and military readiness. Sweden: Enköping. p. 2006.

Loures TC, de Melo PO, Veloso AA. Is it possible to describe television series from online comments? J Internet Serv Appl. 2018;9:25.

Caminha C, Furtado V, Pinheiro V, Ponte C. Graph mining for the detection of overcrowding and waste of resources in public transport. J Internet Serv Appl. 2018;9:22.

Verona L, Oliveira J, Hisse JVC, Campos MLM. Metrics for network power based on Castells’ network theory of power: a case study on Brazilian elections. J Internet Serv Appl. 2018;9:23.

Leão, J. C., Brandão, M. A., VAZ DE Melo, P. O. S., Laender, A. H. F. “Who is really in my social circle?”, Journal of Internet Services and Applications (2018) 9:23.

Caetano JA, Lima HS, Santos MF, Marques-Neto HT. Using sentiment analysis to define twitter political users’ classes and their homophily during the 2016 American presidential election. J Internet Serv Appl. 2018;9:18.

Download references

Acknowledgments

We thank all the authors, reviewers, editors-in-chief, and staff for the great work, which supported this Thematic Series on a very important topic both for the research community and for the industry. In particular, we thank all the editorial committee members: Alessandro Rozza ( lastminute.com Group, ITALY), Altigran Soares da Silva (Federal University of Minas Gerais, BRAZIL), Antonio Loureiro (Federal University of Minas Gerais, BRAZIL), Ari-Veikko Anttiroiko (Tampere University), Artur Ziviani (National Laboratory for Scientific Computing, BRAZIL), Bernardo Pereira Nunes (Pontifical Catholic University of Rio de Janeiro, BRAZIL), Claudio Miceli de Farias (Federal University of Rio de Janeiro, BRAZIL), Daniel Batista (University of São Paulo, BRAZIL), Flavia Bernardini (Federal Fluminense University, BRAZIL), Giacomo Livan (University College London, UK), Isabela Gasparini (Santa Catarina State University, BRAZIL), Jesús Mena-Chalco (Federal University of ABC, BRAZIL), Jonice Oliveira (Federal University of Rio de Janeiro, BRAZIL), Leandro Augusto Silva (Mackenzie Presbyterian University, BRAZIL), Luciano Antonio Digiampietri (University of São Paulo, BRAZIL), Luiz André Portes Paes Leme (Federal Fluminense University, BRAZIL), Mirella Moro (Federal University of Minas Gerais, BRAZIL), Raimundo Moura (Federal University of Piauí, BRAZIL), Yosh Halberstam (University of Toronto, CANADA). The voluntary work of these researchers was crucial for this Thematic Series.

Author information

Authors and affiliations.

Department of Applied Informatics, Federal University of the State of Rio de Janeiro – UNIRIO, Rio de Janeiro, Brazil

Rodrigo Pereira dos Santos

Department of Computer Science, Federal University of Rio de Janeiro – UFRJ, Rio de Janeiro, Brazil

Giseli Rabello Lopes

You can also search for this author in PubMed   Google Scholar

Contributions

Both authors read and approved the final manuscript.

Corresponding author

Correspondence to Rodrigo Pereira dos Santos .

Ethics declarations

Competing interests.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and permissions

About this article

Cite this article.

dos Santos, R.P., Lopes, G.R. Thematic series on Social Network Analysis and Mining. J Internet Serv Appl 10 , 14 (2019). https://doi.org/10.1186/s13174-019-0113-z

Download citation

Published : 22 July 2019

DOI : https://doi.org/10.1186/s13174-019-0113-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.8(4); 2022 Apr

Social network analysis in business and management research: A bibliometric analysis of the research trend and performance from 2001 to 2020

Adhe rizky anugerah.

a Bioresource Management Lab, Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia

Prafajar Suksessanno Muttaqin

b Department of Logistics Engineering, School of Industrial and System Engineering, Telkom University, 40257 Bandung, Indonesia

Wahyu Trinarningsih

c Faculty of Economics and Business, Universitas Sebelas Maret, 57126 Surakarta, Indonesia

Associated Data

Data included in article/supplementary material/referenced in article.

In the past years, research in Social Network Analysis (SNA) has increased. Initially, the research area was limited to sociology and anthropology but has now been used in numerous disciplines. The business and management discipline has many potentials in employing the SNA approach due to enormous relational data, ranging from employees, stakeholders to organisations. The study aims to analyse the research trend, performance, and the utilisation of the SNA approach in business and management research. Bibliometric analysis was conducted by employing 2,158 research data from the Scopus database published from 2001 to 2020. Next, the research quantity and quality were calculated using Harzing's Publish or Perish while VOSviewer visualised research topics and cluster analysis. The study found an upward trend pattern in SNA research since 2005 and reached the peak in 2020. Generally, six subjects under the business and management discipline have used SNA as a methodology tool, including risk management, project management, supply chain management (SCM), tourism, technology and innovation management, and knowledge management. To the best of the authors' knowledge, the study is the first to examine the performance and analysis of SNA in the overall business and management disciplines. The findings provide insight to researchers, academicians, consultants, and other stakeholders on the practical use of SNA in business and management research.

Social network analysis; Bibliometrics; Clustering analysis; Business and management; Literature.

1. Introduction

The SNA is a theory investigating the relations and interactions based on anthropology, sociology, and social psychology to assess social structures ( Erçetin and Neyişci, 2014 ). The social structure in a network theory comprises individuals or organisations named nodes linked through one or more types of interdependencies, such as friendship, kinship, financial exchange, knowledge or prestige ( Parell, 2012 ). The actors range across different levels, from individuals, web pages, families, large organisations, and nations. Nowadays, SNA usage has grown, utilised in anthropology and sociology and several fields of science, including business and management disciplines.

However, studies on the SNA trends and applications in business and management are limited. Although published articles provide a catalogue of SNA concepts, they lack explanatory mechanisms on its application ( Borgatti and Li, 2009 ). Thus, the study aims to assess publication performances and explore SNA usage in business and management studies using Bibliometric analysis. The bibliometric methodology has been widely used to provide quantitative analysis of written publications using statistical tools ( Ellegaard and Wallin, 2015 ). It can help detect established and emergent topical areas, research clusters and scholars, and others ( Fahimnia et al., 2015 ). This analysis reveals important publications and objectively depicts the linkages between and among articles about a specific research topic or field by examining how frequently they have been co-cited by other published articles ( Fetscherin and Usunier, 2012 ).

Bibliometric analysis has at least two primary objectives: 1) to quantitatively measure the quality of journals or authors using statistical indicators such as citations rates ( Vieira et al., 2021 ), and 2) to analyse the knowledge structure and development of specific research fields ( Jing et al., 2015 ). Hence, the study addresses the following research questions: RQ1: What is the current research trend of SNA in business and management research ? RQ2: What is the most productive year of SNA in the business and management discipline ? RQ3: What are the most influential and productive institutions, authors, journals, and countries ? RQ4: What is the use of SNA in the business and management discipline and their cluster topics in the past 20 years ?

Several literature reviews and bibliometric papers on the use of SNA in general business and management areas have been published, but the number is limited. Monaghan, Lavelle, & Gunnigle (2017) analysed SNA usage in management research and practice to discuss the critical dimensions for handling and analysing network data for business research. The authors discovered four dimensions in initial engagement with SNA in business and management research: structure of research design, data collection, handling of data and data interpretation. Nonetheless, studies did not explain the distribution of research clusters and how SNA can be used in practical business and management research. Specifically, Su et al. (2019) conducted a Bibliometric analysis on SNA literature with no limitation of subject discipline and collected the data from Web of Science (WoS), covering 20 publication years from 1999 to 2018. Nevertheless, Su et al. (2019) mainly discussed the SNA publication performance but not how the approach was used previously.

In the more specific subject area of business and management, SNA has been explored to unveil the relationship between organisations, as conducted by Sozen et al. (2009) . The SNA has been used to measure the organisations' social capital, map resource dependency relations, and discover coalitions and cliques between organisations. Kurt and Kurt (2020) have explored the potential of SNA in international business (IB) research, because of two fundamental phemomena: firm internationalisation and multinational enterprises (MNEs). From the marketing perspective, SNA could detect the most influential actors to efficiently spread a message in online communities for marketing purposes ( Litterio et al., 2017 ).

The current study conducted a clustering analysis to identify and analyse SNA performance and its application in general business and management discipline using bibliometrics information. Thus, academicians, managers, consultants, and other stakeholders could understand when and how to apply the SNA approach. For instance, SNA can identify potential risks contributing to schedule delays in project risk management ( Li et al., 2016 ). The discussion section explores how SNA has been previously used in business and management research. Besides, the study addresses the problem in Borgatti and Li (2009) , exploring the actual application of SNA in management and business research.

2.1. Data sources and search strategy

The primary study objective is to analyse the research trend and explore the SNA approach in business and management research. A Bibliometric analysis was employed due to its accuracy in quantifying and evaluating scientific publications ( Carmona-Serrano et al., 2020 ). Additionally, the data were collected through the Scopus database. Although Scopus and WoS are the main and most comprehensive sources for Bibliometric analysis, Scopus has more advantages: more inclusive content coverage, more openness to society, and available individual profiles for all authors, institutions, and serial sources. Additionally, many papers have confirmed that Scopus provides wider overall coverage and Scopus indexing a greater amount of unique sources not covered by WoS ( Pranckutė, 2021 ). In the business, economics, and management area, 89% of articles listed in WoS are listed in Scopus. Hence, the study area (business and management) chose the Scopus database for further analysis.

The next step involved determining the search string, including all documents with the title, abstract, and keywords containing "network analysis" or "Social Network Analysis". These two main keywords are representative enough to reach the objective; they are not too wide and specific. The main goal is the utilisation of SNA as a concept and as a methodology can be widely captured. These two versions of keywords “Social Network Analysis” and “Network Analysis” without the “social” has a significant impact. Some articles did not put the complete sentence of SNA, although the articles mainly discussed the concept of network analysis. One of the examples is the ownership structure related research developed by Vitali et al. (2011) which changed the word "social network analysis" to "corporate network analysis". The term “social” in SNA refers to people interaction, while in the operation research, the relationship could be between airport, stakeholders, corporation, etc.

In the first run, 113,945 research related to SNA was found in the Scopus database, mainly Engineering and Computer Science fields. Besides, the search results were limited to the subject area in business, management, and accounting and covered publication from 2001 to 2020 (20 years). The study also excluded non-journal articles, such as conference proceedings, trade reports, book chapters, and others.

The search limitations have resulted in 2,881 articles, but many were still not related to network analysis or business and management. Further, 723 articles were excluded, covering articles in neuroscience, bibliometric, circuit network (engineering), earth and planetary science, chemistry, etc., although the articles employed network analysis as a methodology. The exclusion was also applied to articles that use SNA in multi-subject journals with little or no explanation in business, management, and accounting perspectives. One example is the Journal of Cleaner Production listed in four subject areas: business, management and accounting; energy; industrial and manufacturing engineering; and environmental science. In this journal, SNA theory is used to identify the relationship between ecosystems by measuring the flow of energy or material between organisms, which has little or no explanation from business and management perspectives. At the end of the search, 2,158 articles were extracted for further analysis. The flow chart on the data collection strategy is presented in Figure 1 .

The search strategy flow diagram (adopted from ( Zakaria et al., 2021 )).

2.2. Data analysis

The first stage involved analysing the data descriptively to identify the quality and quantity using standard Bibliometric measures ( Hirsch, 2005 ). The total number of publications (TP) assessed the quantity dimension, whereas other metrics assessed the quality dimension, such as total citation (TC), number of cited publications (NCP), average citation per publication (C/P), average citations per cited publication (C/CP) ( Hirsch, 2007 ). Additionally, the g-index ( g ) and h-index ( h ) are usually included in the Bibliometric measures to predict future achievement rather than standard measures. The indicators are applied to various levels: country-level, organisation-level, journal-level, and author-level. The information is processed and analysed using Harzing publish or perish (PoP) software by extracting Research Information System (RIS) data from the Scopus database.

The second stage visualised the research network to understand the relationship between nodes, including authors, affiliations (organisations), countries, citations, and keywords. Nonetheless, only keywords co-occurrence was carried out to examine the past, current, and future potential of SNA in business and management research. The study analysed the keywords based on the frequency, edges, and clusters. The combination between nodes (keywords) and edges (the relationship between keywords) form clusters with numerous research themes ( Dhamija and Bag, 2020 ). The bigger nodes show a higher occurrence in the keyword visualisations, and the thicker edges show the higher link strength. Meanwhile, cluster analysis in the study represents a set of similar keywords in one group, different in other groups to identify the research interest and keywords combination within the group. The cluster mapping was performed by VOSviewer, an open-access programme to construct and view Bibliometric maps ( van Eck and Waltman, 2010 ). Besides, the study developed an overlay visualisation to explore research evolution in SNA over time.

3.1. Description of retrieved literature

The study is limited to SNA research in business and management research published between 2001 to 2020. The study also excluded review papers, conference papers, editorials, and other documents besides journal articles for further analysis. Ultimately, the study retrieved a total of 2,158 articles. Although the search was limited to only English articles, the study identified seven bilingual articles in Spanish (3 articles), Chinese (2 articles), Lithuanian (1 article), and Portuguese (1 article). The articles had 58,522 citations, an average of 2,926 citations per year, and 27 citations per paper. The complete citation metrics for the articles are shown in Table 1 .

Table 1

Citations metrics.

Besides SNA as a primary keyword, the top keywords were "innovation" (6.16%), "project management" (3.48%), "knowledge management" (3.29%), "decision making" (2.97%), "complex networks" (2.69%), and others. The top keywords are listed in Table 2 . High-frequency keywords show the popularity of a specific topic ( Pesta et al., 2018 ). The listed keywords in the study usually appear together in SNA and are used to explore the potential use of SNA in business and management research.

Table 2

Top keywords.

3.2. Research Growth

Although SNA research productivity presented the ups and downs throughout the year, a consistent upward trend was found in the pattern. Research in the first five years (2001–2005) was limited and never reached 30 publications per year. In 2002, only 10 articles were published, increasing almost five times in 2007 (n = 49 documents). The number increased until 2020, slightly decreasing in 2011, 2015, and 2019. Meanwhile, the most productive year was in 2020, with 334 published articles.

The articles published in 2001 had the highest average citation per publication (c/p = 186.69). However, the highest h-index were in 2010 ( h = 43) and 2014 ( h = 36) which indicate high cumulative impact of the articles measured by its quantity with quality. Low citations per publication in recent years were expected due to increasing citation counts over time. The publication trend and average citations per publication are presented in Figure 2 .

Total publications and citations by year.

3.3. Top countries, institutions, and authors in SNA business and management research

The United States (US), the United Kingdom (UK), and China were the most prolific countries with 605, 230 and 215 articles, respectively. The study discovered no dominating continent that produced SNA business and management research, and all were equally distributed except for Africa. The top ten countries list (see Table 3 ) showed one North American, four Asian and Oceanian, and five European countries. As the top most productive countries, the United States and the United Kingdom published quality articles with highest average citation per publication of 39.34 and 32.80, respectively. Meanwhile, Asian countries had small citations (based on the top ten most productive countries): South Korea (c/p = 21.84) and China (c/p = 27.08) were at the bottom of the ranking according to the c/p calculation.

Table 3

The top ten countries contributed to the publications.

Notes: TP = total number of publications; NCP = number of cited publications; TC = total citations; C/P = average citations per publication; C/CP = average citations per cited publication; h = h-index; and g = g-index.

Hong Kong Polytechnic University was the most productive institution with 45 published articles and had the highest h- and g- index (see Table 4 ). The publication number was higher than the second most productive, Università Bocconi (n = 25). However, the top ten list showed that the University of Arizona had the highest c/p with 102.47, followed by the University of Kentucky (c/p = 58.40) and the Università Bocconi (c/p = 58.36). As the most productive country, there were four United States universities listed as the top most productive institutions but it only covered 10.9% from the total US articles. This indicates that the publications were distributed to other US institutions. Nevertheless, articles from two Hong Kong insitutions, Hong Kong Polytechic University and City University Hongkong accumulated 57 articles (82.61% of the country's total articles).

Table 4

Top 10 most influential institutions in SNA (business and management) research.

The two most productive institutions conducted different research themes. Figure 3 shows that Hong Kong Polytechnic University use SNA in numerous areas, such as "supply chain management", "transportation", "big data", "knowledge management", “stakeholder analysis in construction project” and others. Nonetheless, Università Bocconi research mostly involved tourism; the keywords were "tourist destination", "tourism management", "stakeholder", and “hospitality”. Besides, 17 of the 25 articles (68.0%) from Università Bocconi were written by Rodolfo Baggio, the author with the most article in SNA business and management. Table 5 presents the most productive authors with at least six published articles in SNA.

Research topic comparison between A) Hong Kong Polytechnic University and B) Università Bocconi.

Table 5

Most productive authors with a minimum of six articles.

Rodolfo Baggio was the most productive author with the most publications, followed by Noel Scott (n = 8) from Australia and Andrea Fronzetti Colladon (n = 7) from Italy. Based on the average citations per document, Carlos Casanueva from the Universidad de Sevilla, Spain, had the highest score with an average of 95.33 citations per document. Baggio, R. and Scott, N, as the most productive authors have similar research interest, which is in tourism management. Based on the study database, they had written four articles together using a network analysis approach in tourism management. Fronzetti Colladon, A. is an expert in big data, creativity and innovation management while Hossain L., utilising SNA in organisational communication network during crisis or emergency events.

3.4. Most active journals

The majority of the articles were mostly published in Elsevier's journals. Specifically, seven out of 12 source titles were Elsevier's journals; two journals were published by the American Society of Civil Engineers (ASCE), two journals by Taylor's and Francis, and one by Emerald. The Journal of Technological Forecasting and Social Change was the most active source with 84 articles, followed by Transportation Research Part E: Logistics and Transportation Review and Knowledge based Systems with 45 and 44 articles, respectively. Based on the average citations per publication, Construction Management and Economics had the highest score (c/p = 78.50), followed by Decision Support Systems (c/p = 55.33) and Transportation Research Part E (c/p = 50.51). Table 6 demonstrates a list of the most active sources in publishing research in SNA (business and management) and its impact score (cite score and SCImago Journal Rank (SJR) 2019).

Table 6

Most active source title.

Notes: TP = total number of publications; C/P = average citations per publication.

The SNA usage in business and management varies and depends on the journal scope. Particularly, SNA is used to study the interaction between people in the social environment and in various other subjects. The use of SNA in specific journals was explored by visualising the network of keywords relationship, as presented in Figure 4 . The selected three journals publishing research in SNA (business and management) had a different perspective in employing SNA as a tool to analyse the relationship between nodes.

Most frequent keywords in a) Technological Forecasting and Social Change; b) Transportation Research Part E: Logistics and Transportation Review; and c) Journal of Business Research.

Journal of Technological Forecasting and Social Change primarily utilised "innovation", "technological development", "patents and inventions", "technology adoption", "emerging technology", and others. SNA can also be used for transportation research as published in Transportation Research Part E: Logistics and Transportation Review with keywords "numerical model", "transportation planning", "air transportation", "optimisation", "freight transport", and others. Meanwhile, research published in Journal of Business Research published articles with keywords “business networks”, “social closure”, “collaboration”, “diffusion”, and others. The SNA can also be used in construction and project management, tourism management, urban planning and development, and organisational study (coordination and competition).

3.5. Highly-cited articles

Tsai (2002) published the top-cited article in SNA business and management research titled " Social structure of "coopetition" within a multiunit organisation: Coordination, Competition, and Intra organisational Knowledge Sharing" . The publication had 1,124 or 56.2 citations per year. Besides, the article explored another use of SNA, explained in the "most active journals" section in the organisational study. The study revealed that most of the top 15 articles related to inter- or intra- organisational networks, and some papers explored the use of SNA in social communication, supply chain, tourism marketing, and others. Table 7 shows the top 15 highly-cited articles.

Table 7

Top 15 Highly-cited articles.

3.6. The use of SNA in business and management research

The study conducted a keywords cluster analysis to highlight SNA usage in business and management research and identify how keywords are linked. The keywords cluster analysis was presented in two ways; the first is based on the occurrence level (see Figure 5 ), and the second is based on the year of publications (see Figure 6 ). Based on the level of keywords occurrence, SNA research was classified into six clusters: cluster 1 (red nodes) covered research in construction, project management, and information management; cluster 2 (green nodes) covered research in transportation and tourism management; cluster 3 (dark blue nodes) covered research in semantic, big data, and decision support system; cluster 4 (yellow nodes) included research in innovation, international trade, and globalisation; cluster 5 (purple node) explored research in knowledge management and knowledge sharing; cluster 6 (light blue nodes) included research in social capital, and financial performance and management.

Keywords analysis of SNA in business and management publications.

Keywords evolution of SNA in business and management research.

According to publication years, the study discovered that from 2012 to 2014, the most frequent keywords were "project management", "optimisation", "technology transfer", and "construction industry". From 2015 to 2016, the keywords shifted to "data mining", "information management", "decision-making", "tourist destination", "air transportation", "airline industry", and "innovation". Recently, "sentiment analysis", "text mining", and "big data" became popular in SNA research.

4. Discussion

The study was conducted to analyse SNA research in business and management subjects. Generally, an upward trend was found in the number of publications, significantly increasing since 2005. A significant increase was also discovered in 2020, a 40.3% increase from the previous year, the second-highest increase in the past 20 years. A similar Bibliometric analysis on SNA research without subject limitation had a similar pattern with the study, whereby SNA publications increased gradually since 2005 Based on quality metrics, articles published in 2001 and 2002 had the highest average citations per publication. Moreover, several articles published in those years also had the highest number of citations published in organisational science.

Tsai (2002) articles had the highest citation number (c = 1,124), followed by Reagans and Zuckerman (2001) , with 1009 citations. Tsai (2002) displayed intra organisational as a set of social networks and examined networks of collaborative and competitive ties within the organisation. Each unit collaborates for knowledge sharing and competes for resources and market share. Additionally, the centrality concept was used to measure the ability of intra organisational units in the knowledge sharing behaviour. In the same journal, Reagans and Zuckerman (2001) employed the SNA approach to examine the relationship between team density and heterogeneity to its performance using two network metrics: network density to assess communication frequency between team members, and network heterogeneity to explore time allocation of scientists to colleagues far removed in the team tenure distribution. According to above explanation, SNA can be used at different organisation levels, one in unit levels-organisation, and another in person-level interactions under one team.

The study revealed the US, the UK, and China as the most productive countries. Moreover, the study had similar findings as in Su et al. (2019) ; they found that the US had dominated the research in SNA, UK ranked second, and followed by China. Based on the average c/p, institutions from Asia such as South Korea, China, and Taiwan (average c/p = 23.77) received lower scores than European institutions (average c/p = 28.31). Better quality and impactful research are needed for authors from Asian institutions. The following section describes SNA usage in business and management themes.

4.1. Research themes

The SNA is a set of formal methods for studying social structures according to graph theory. Individuals and social actors, such as groups and organisations, are shown in points and their social relations in lines ( Korom, 2015 ). Meanwhile, the structure relations and the location of individual actors have substantial behavioural consequences for individuals and social structure as a whole ( Wellman, 1988 ). The SNA has become a multidisciplinary endeavour extending beyond sociology and social anthropology sciences and to many other disciplines, such as politics, epidemiology, communication science, and others. The next section explores the use of SNA in business and management discipline from publication records between 2001 to 2020 by analysing the keywords co-occurrences.

4.2. Project management

The first paper that employed SNA in project management was published in 1997 by Loosemore, believing that construction project participants are embedded in complex social networks that are constantly changing. Furthermore, Loosemore (1997) analysed communication efficiency in the engineering project organisation during crises. Besides, Pryke (2004) had the highest citations in the construction and project management area. Contrary to Loosemore, who applied network analysis at the individual level, Pryke examined the relationship between project actors at the firm level. Pryke also used network analysis in the comparative analysis of procurement and project management of construction projects. Subsequently, SNA publication in project management literature increased substantially.

Chinowsky, Diekmann, & O'Brien (2010) summarised the use of network analysis in project management research. After examining communication efficiency, network analysis analysed networks in relationship-based procurement, the effect of centrality on project coordination, the effect of cultural diversity on project performance, collaboration effectiveness to achieve high-performance teams, and others. The study discovered several popular keywords in project management research, such as "communication", "decision-making", "stakeholder", "accident prevention", "scheduling", "information exchange", "collaborative projects" and others, which explained the use of SNA in this subject. The top three journals publishing SNA usage in project and construction management were the Journal of Construction Engineering and Management (ASCE), International Journal of Project Management (Elsevier), and Construction Management and Economics (Taylor & Francis).

4.3. Risk management

The SNA publications in risk assessment and management usually relate to other subjects, such as project management, SCM, knowledge management, and others. The study found 42 related articles, mostly on construction and project management. Notably, SNA improved the effectiveness and accuracy of stakeholder and risk analysis in green building projects ( Yang et al., 2016 ). The model considered the risk associated with stakeholders and the interdependencies of risks for better decision-making. Additionally, Li et al. (2016) employed SNA for risk evaluation and risk response processes in construction projects.

The SNA effectively evaluates the potential risk contributing to schedule delays in project processes by removing key nodes and links, ultimately removing stakeholder risk that is highly interconnected to another risk. Similar to Li, Yu et al. (2017) specifically utilised SNA for social risk in urban redevelopment projects during the housing demolition stage with an identical process. The approach extends beyond construction and project management subject and any other subjects that employed risk assessment or evaluation in their risk management process.

4.4. tourism management

The SNA approach in tourism-related subjects was widespread, with "tourist destinations" and “tourism” among the most frequent and top 25 keywords (see Table 2 ). Besides, Baggio, R. was the most productive author who used SNA in tourism research. Three research streams are related to network analysis in the travel industry setting: the Bibliometric analysis on research collaboration and knowledge creation; network analysis on the travel industry supply, destination, and policy systems; and tourist movements and behavioural patterns ( Liu et al., 2017 ). Besides, the study discovered three significant journals with the most publications in tourism research: Annals of Tourism Research, Tourism Management, and Current Issues in Tourism, similar to Casanueva et al. (2016) ; whereby tourism management was the most productive journal. Recently, the Annals of Tourism Research surpassed Tourism Management.

Most tourism supply and destination research highlighted collaboration and partnership among tourism stakeholders. Baggio, Scott and Cooper (2010) applied network analysis to explain the topology of stakeholders in Elba, Italy's tourism organisations (hotels, travel agencies, associations, public bodies, and others). The tourism stakeholders were described in quantitative (network metrics) and qualitative (figure) ways. For instance, the percentage of non-connected networks described the sparseness of a network and showed a low degree of collaboration or cooperation between stakeholders. Nonetheless, Leung et al. (2012) and Asero et al. (2016) , and others studied tourist movement and behavioural patterns by analysing tourists' itineraries with traditional (interview or online travel diaries) or more-advanced technologies (geographic information system (GIS), global positioning system (GPS), timing systems, camera-based systems, and others). The primary objective is to analyse the main tourist attraction, main tourism movement patterns and change patterns in tourist attractions.

4.5. Supply chain management

The use of SNA in supply chain management research had developed in 2010, and numerous scholars were unaware of the possibilities of the SNA approach in the SCM field ( Wichmann and Kaufmann, 2016 ). The supply chain is a network of companies comprising interconnected actors, such as suppliers, manufacturers, logistic providers, and customers ( Bellamy et al., 2014 ). Three journals with the most SNA articles in SCM are the International Journal of Production Economics, International Journal of Production Research, and Journal of Operations Management.

Y. Kim et al. (2011) employed SNA to analyse the structural characteristics of supply networks in a buyers-suppliers network of automotive industries. The networks in the supply chain were classified into the material flow (supply load, demand load, and operational criticality) and the contractual relationship between actors (influential scope, informational independence, and relational mediation). The study discovered that network metrics could be used to analyse the characteristic of supply network structures.

The SNA can also measure and reduce supply chain complexity ( Allesina et al., 2010 ), a concept based on ecological theory. Additionally, eight entropic performance indexes were used: total system throughput, average mutual information, development capacity, overhead in input, export, and dissipation, etc. Contrarily, Ting & Tsang (2014) utilised SNA to identify the possibility of counterfeit products from infiltrating into the supply chain using the transaction records history to detect problematic parties and their suspicious trails. Three SNA measures were included in the study: degree centrality, betweenness centrality, and closeness centrality.

4.6. Knowledge management

The SNA usage in knowledge management varies; for instance, Parise (2007) used SNA for knowledge management of human resources, including knowledge creation and innovation, knowledge transfer and retention, and job succession planning. The SNA in knowledge creation and innovation is used to identify the flow of ideas and bottlenecks in the decision-making process. The SNA is also applied to analyse the structure of regional knowledge in the technology specialisation ( Cantner et al., 2010 ), knowledge transfer analysis on sustainable construction projects ( Schröpfer et al., 2017 ), predicting and evaluating future knowledge flows in insurance organisations ( Leon et al., 2017 ) and knowledge transfer from experts to newcomers ( Guechtouli et al., 2013 ), and others. The top productive journals are the Journal of Knowledge Management, Technological Forecasting and Social Change, and the Journal of Construction Engineering and Management.

4.7. Technology and innovation management

The SNA is used for innovation and technology transfer. Keywords under this subject include "citation "innovation", "patents and inventions", "technological development", and others. The SNA metrics utilised to assess the performance and centrality of individuals in virtual research and design (R&D) groups by analysing their e-mails ( Ahuja et al., 2003 ), identifying the position and relationships between innovators ( Cantner and Graf, 2006 ), research collaboration network between university-industry ( Balconi and Laboranti, 2006 ), and others.

The SNA in patents and inventions identify companies with a significant legal influence on the applied technologies by analysing intellectual property lawsuits between companies (H. Kim and Song, 2013 ). Patents data were also popular to study technological innovation of electronic companies; SNA was employed to cluster the patents and find vacant technology domains ( Jun and Sung Park, 2013 ). Furthermore, patent data in SNA enables exploring the technology evolution of certain products ( Lee et al., 2010 ). Specifically, the top three journals were: Technological Forecasting and Social Change, Technological Analysis and Strategic Management, and Industry and Innovation.

5. Conclusion

After SNA was introduced in 1969 by Mitchell, many researchers from various fields were interested in studying the relationship between nodes. The most frequent disciplines that used SNA are sociology, anthropology, social psychology, and communication. Nevertheless, SNA usage in business and management discipline was limited. Hence, the study analysed the trend and performance of SNA in the business and management discipline from 2001 to 2020. The study revealed a steady upward trend of publications in this field and increased significantly since 2005. The US, the UK, and China were the most productive countries. Although the study found three Asian institutions as the most productive countries, the average c/p was lower than the European and American countries. Besides, SNA as a research tool has been published in multidisciplinary journals, ranging from Journal of Management in Engineering to Journal of Knowledge Management, depending on the subject of investigation.

The study also performed a co-occurrence keywords analysis to examine the research cluster and emerging research topics in SNA, especially in business and management studies. The study revealed six clusters, each containing one to two research disciplines. The SNA has been employed in numerous topics, including project management, risk management, tourism management, supply chain, knowledge management, and technological management. Observably, big data, social media and sentiment analysis are the trending topic in SNA.

The research contributions include: first, the publication trend and research productivity show the current issue and development of SNA in the business and management discipline; second, the data on most productive authors and institutions academic communication and cooperation among scholars in related fields; and lastly, the visualisation of research topics mapping and the cluster analysis explored the current use of SNA in different discipline and formulated future research agenda.

The study limitations are: first, the study only considered the Scopus database and SNA literature could be more extensive. Other significant databases, such as WoS and CNKI (Chinese National Knowledge Infrastructure) should be considered for future research. Second, in the co-occurrence keywords analysis, a threshold was set to limit important keywords; thus, the study might not include several research topics using SNA. Third, although the study has cleaned the database, titles not purely from business and management disciplines might be included due to journal sources with multi subject classification. Lastly, we only employed standard bibliometric measures as a quantitative assessment in this research; the inclusion of SNA’ centrality index can be considered in future study to assess the power and importance of authors, institutions, countries, and journals.

Institutional review board statement

Not applicable.

Informed consent statement

Decalarations, author contribution statement.

All authors listed have significantly contributed to the development and the writing of this article.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interests statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

• Acedo F.J., Barroso C., Casanueva C., Galán J.L. Co-authorship in management and organizational studies: an empirical and network analysis. J. Manag. Stud. 2006; 43 (5):957–983. [ Google Scholar ]
• Ahuja M.K., Galletta D.F., Carley K.M. Individual centrality and performance in virtual R& D groups: an empirical study. Manag. Sci. 2003; 49 (1):21–38. [ Google Scholar ]
• Allesina S., Azzi A., Battini D., Regattieri A. Performance measurement in supply chains: new network analysis and entropic indexes. Int. J. Prod. Res. 2010; 48 (8):2297–2321. [ Google Scholar ]
• Asero V., Gozzo S., Tomaselli V. Building tourism networks through tourist mobility. J. Trav. Res. 2016; 55 (6):751–763. [ Google Scholar ]
• Baggio R., Scott N., Cooper C. Network science: a review focused on tourism. Ann. Tourism Res. 2010; 37 (3):802–827. [ Google Scholar ]
• Balconi M., Laboranti A. University–industry interactions in applied research: the case of microelectronics. Res. Pol. 2006; 35 (10):1616–1630. [ Google Scholar ]
• Bellamy M.A., Ghosh S., Hora M. The influence of supply network structure on firm innovation. J. Oper. Manag. 2014; 32 (6):357–373. [ Google Scholar ]
• Borgatti S.P., Li X. On social network analysis in a supply chain context. J. Supply Chain Manag. 2009; 45 (2):5–22. [ Google Scholar ]
• Boschma R.A., ter Wal A.L.J. Knowledge networks and innovative performance in an industrial district: the case of a footwear district in the South of Italy. Ind. Innovat. 2007; 14 (2):177–199. [ Google Scholar ]
• Brown J., Broderick A.J., Lee N. Word of mouth communication within online communities: conceptualizing the online social network. J. Interact. Market. 2007; 21 (3):2–20. [ Google Scholar ]
• Cantner U., Graf H. The network of innovators in Jena: an application of social network analysis. Res. Pol. 2006; 35 (4):463–480. [ Google Scholar ]
• Cantner U., Meder A., ter Wal A.L.J. Innovator networks and regional knowledge base. Technovation. 2010; 30 (9–10):496–507. [ Google Scholar ]
• Carmona-Serrano N., López-Belmonte J., Cuesta-Gómez J.-L., Moreno-Guerrero A.-J. Documentary analysis of the scientific literature on autism and technology in web of science. Brain Sci. 2020; 10 (12):985. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Casanueva C., Gallego Á., García-Sánchez M.-R. Social network analysis in tourism. Curr. Issues Tourism. 2016; 19 (12):1190–1209. [ Google Scholar ]
• Chinowsky P.S., Diekmann J., O’Brien J. Project organizations as social networks. J. Construct. Eng. Manag. 2010; 136 (4):452–458. [ Google Scholar ]
• Dhamija P., Bag S. Role of artificial intelligence in operations environment: a review and bibliometric analysis. The TQM Journal. 2020; 32 (4):869–896. [ Google Scholar ]
• Ellegaard O., Wallin J.A. The bibliometric analysis of scholarly production: how great is the impact? Scientometrics. 2015; 105 (3):1809–1831. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Erçetin Ş.Ş., Neyişci N.B. Chaos, Complexity and Leadership. 2014. Social network analysis : a brief introduction; pp. 167–171. [ Google Scholar ]
• Fahimnia B., Sarkis J., Davarzani H. Green supply chain management: a review and bibliometric analysis. Int. J. Prod. Econ. 2015; 162 :101–114. [ Google Scholar ]
• Fetscherin M., Usunier J.C. Corporate branding: an interdisciplinary literature review. Eur. J. Market. 2012; 46 (5):733–753. [ Google Scholar ]
• Guechtouli W., Rouchier J., Orillard M. Structuring knowledge transfer from experts to newcomers. J. Knowl. Manag. 2013; 17 (1):47–68. [ Google Scholar ]
• Hafner-Burton E.M., Kahler M., Montgomery A.H. Network analysis for international relations. Int. Organ. 2009; 63 (3):559–592. [ Google Scholar ]
• Hirsch J.E. An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. Unit. States Am. 2005; 102 (46):16569–16572. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Hirsch J.E. Does the h index have predictive power? Proc. Natl. Acad. Sci. Unit. States Am. 2007; 104 (49):19193–19198. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jing S., Qinghua Z., Landström H. Handbook of Research on Global Competitive Advantage through Innovation and Entrepreneurship. 2015. Entrepreneurship across regions: internationalization and/or contextualization? pp. 372–392. [ Google Scholar ]
• Jun S., Sung Park S. Examining technological innovation of Apple using patent analysis. Ind. Manag. Data Syst. 2013; 113 (6):890–907. [ Google Scholar ]
• Kim H., Song J. Social network analysis of patent infringement lawsuits. Technol. Forecast. Soc. Change. 2013; 80 (5):944–955. [ Google Scholar ]
• Kim Y., Choi T.Y., Yan T., Dooley K. Structural investigation of supply networks: a social network analysis approach. J. Oper. Manag. 2011; 29 (3):194–211. [ Google Scholar ]
• Korom P. International Encyclopedia of the Social & Behavioral Sciences. Vol. 16. 2015. Network analysis, history of; pp. 524–531. Second Edi. [ Google Scholar ]
• Kurt Y., Kurt M. Social network analysis in international business research: an assessment of the current state of play and future research directions. Int. Bus. Rev. 2020; 29 (2):101633. [ Google Scholar ]
• Lee P.-C., Su H.-N., Wu F.-S. Quantitative mapping of patented technology — the case of electrical conducting polymer nanocomposite. Technol. Forecast. Soc. Change. 2010; 77 (3):466–478. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Leon R.–D., Rodríguez-Rodríguez R., Gómez-Gasquet P., Mula J. Social network analysis: a tool for evaluating and predicting future knowledge flows from an insurance organization. Technol. Forecast. Soc. Change. 2017; 114 :103–118. [ Google Scholar ]
• Leung X.Y., Wang F., Wu B., Bai B., Stahura K.A., Xie Z. A social network analysis of overseas tourist movement patterns in beijing: the impact of the olympic games. Int. J. Tourism Res. 2012; 14 (5):469–484. [ Google Scholar ]
• Li C.Z., Hong J., Xue F., Shen G.Q., Xu X., Mok M.K. Schedule risks in prefabrication housing production in Hong Kong: a social network analysis. J. Clean. Prod. 2016; 134 (Part B):482–494. [ Google Scholar ]
• Li N., Wu D.D. Using text mining and sentiment analysis for online forums hotspot detection and forecast. Decis. Support Syst. 2010; 48 (2):354–368. [ Google Scholar ]
• Litterio A.M., Nantes E.A., Larrosa J.M., Gómez L.J. Marketing and social networks: a criterion for detecting opinion leaders. Eur. J. Manag. Bus. Econ. 2017; 26 (3):347–366. [ Google Scholar ]
• Liu B., Huang S., Fu H. An application of network analysis on tourist attractions: the case of Xinjiang, China. Tourism Manag. 2017; 58 :132–141. [ Google Scholar ]
• Loosemore M. Construction crises as periods of social adjustment. J. Manag. Eng. 1997; 13 (4):30–37. [ Google Scholar ]
• Monaghan S., Lavelle J., Gunnigle P. Mapping networks: exploring the utility of social network analysis in management research and practice. J. Bus. Res. 2017; 76 :136–144. [ Google Scholar ]
• Nagurney A., Dong J., Zhang D. A supply chain network equilibrium model. Transport. Res. E Logist. Transport. Rev. 2002; 38 (5):281–303. [ Google Scholar ]
• Netzer O., Feldman R., Goldenberg J., Fresko M. Mine your own business: market-structure surveillance through text mining. Market. Sci. 2012; 31 (3):521–543. [ Google Scholar ]
• Pan B., MacLaurin T., Crotts J.C. Travel blogs and the implications for destination marketing. J. Trav. Res. 2007; 46 (1):35–45. [ Google Scholar ]
• Parell C. SAGE Publication; Chennai: 2012. Social Network Analysis: History, Theory, and Methodology. [ Google Scholar ]
• Parise S. Knowledge management and human resource development: an application in social network analysis methods. Adv. Develop. Hum. Resour. 2007; 9 (3):359–383. [ Google Scholar ]
• Pesta B., Fuerst J., Kirkegaard E. Bibliometric keyword analysis across seventeen years (2000–2016) of intelligence articles. J. Intell. 2018; 6 (4):46. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Pranckutė R. Web of science (WoS) and Scopus: the titans of bibliographic information in today’s academic world. Publications. 2021; 9 (1):12. [ Google Scholar ]
• Provan K.G., Milward H.B. Do networks really work? A framework for evaluating public-sector organizational networks. Publ. Adm. Rev. 2001; 61 (4):414–423. [ Google Scholar ]
• Pryke S.D. Analysing construction project coalitions: exploring the application of social network analysis. Construct. Manag. Econ. 2004; 22 (8):787–797. [ Google Scholar ]
• Reagans R., Zuckerman E.W. Networks, diversity, and productivity: the social capital of corporate R&D teams. Organ. Sci. 2001; 12 (4):502–517. [ Google Scholar ]
• Schröpfer V.L.M., Tah J., Kurul E. Mapping the knowledge flow in sustainable construction project teams using social network analysis. Eng. Construct. Architect. Manag. 2017; 24 (2):229–259. [ Google Scholar ]
• Sosa M.E., Eppinger S.D., Rowles C.M. The misalignment of product architecture and organizational structure in complex product development. Manag. Sci. 2004; 50 (12):1674–1689. [ Google Scholar ]
• Sozen C., Basim N., Hazir K. Social network analysis in organizational studies. Int. J. Bus. Manag. 2009; 1 (1):21–35. [ Google Scholar ]
• Stephen A.T., Toubia O. Deriving value from social commerce networks. J. Market. Res. 2010; 47 (2):215–228. [ Google Scholar ]
• Su Y.-S., Lin C.-L., Chen S.-Y., Lai C.-F. Bibliometric study of social network analysis literature. Libr. Hi Technol. 2019; 38 (2):420–433. [ Google Scholar ]
• Ting S.L., Tsang A.H.C. Using social network analysis to combat counterfeiting. Int. J. Prod. Res. 2014; 52 (15):4456–4468. [ Google Scholar ]
• Tsai W. Social structure of “coopetition” within a multiunit organization: coordination, competition, and intraorganizational knowledge sharing. Organ. Sci. 2002; 13 (2):179–190. [ Google Scholar ]
• van Eck N.J., Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010; 84 (2):523–538. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Vieira E.S., Madaleno M., Azevedo G. In: Comparative Research on Earnings Management, Corporate Governance, and Economic Value. Vieira E.S., Madaleno M., Azevedo G., editors. 2021. Research on earnings management: bibliometric analysis; pp. 1–26. [ Google Scholar ]
• Vitali S., Glattfelder J.B., Battiston S. The network of global corporate control. PLoS One. 2011; 6 (10) [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Wellman B. Social Structures: A Network Approach. Cambridge University Press; 1988. Structural analysis: from method and metaphor to theory and substance. [ Google Scholar ]
• Wichmann B.K., Kaufmann L. Social network analysis in supply chain management research. Int. J. Phys. Distrib. Logist. Manag. 2016; 46 (8):740–762. [ Google Scholar ]
• Yang R.J., Zou P.X.W., Wang J. Modelling stakeholder-associated risk networks in green building projects. Int. J. Proj. Manag. 2016; 34 (1):66–81. [ Google Scholar ]
• Yu T., Shen G.Q., Shi Q., Lai X., Li C.Z., Xu K. Managing social risks at the housing demolition stage of urban redevelopment projects: a stakeholder-oriented study using social network analysis. Int. J. Proj. Manag. 2017; 35 (6):925–941. [ Google Scholar ]
• Zakaria R., Ahmi A., Ahmad A.H., Othman Z. Worldwide melatonin research: a bibliometric analysis of the published literature between 2015 and 2019. Chronobiol. Int. 2021; 38 (1):27–37. [ PubMed ] [ Google Scholar ]

• Privacy Policy

Buy Me a Coffee

Home » Social Network Analysis – Types, Tools and Examples

Social Network Analysis – Types, Tools and Examples

Table of Contents

Social Network Analysis

Social Network Analysis (SNA) is an analytical method used to study social structures through the use of networks and graph theory. It identifies the relationships between individuals, organizations, or other entities and examines the patterns and implications of these relationships.

The nodes in the network represent the actors within the networks and the ties or edges represent relationships between the actors. These might be, for example, friendship ties between people, business relationships between companies, or communication patterns between individuals.

By analyzing the network structure and the characteristics of the actors within the network, SNA can reveal properties such as the distribution of resources, the flow of information, or the overall connectivity of the network.

Here are a few key concepts in SNA:

• Centrality : This measures the importance of a node in the network. Various centrality measures exist, each emphasizing a different aspect of a node’s position within the network, such as degree centrality (the number of direct connections a node has), betweenness centrality (the number of times a node acts as a bridge along the shortest path between two other nodes), and eigenvector centrality (the sum of the centrality scores of all nodes that one node is connected to).
• Density : This is a measure of the proportion of possible connections in a network that are actual connections. A high density suggests that the network participants are highly interconnected.
• Clusters or Communities : These are groups of nodes that are more densely connected with each other than with the rest of the network.
• Structural Holes : These are gaps in the network where a node could potentially act as a bridge between two unconnected parts of the network.

Types of Social Network Analysis

Social Network Analysis can be broadly categorized based on the type of networks being analyzed, the level of analysis, and the methodologies employed. Here are a few ways to categorize SNA:

Whole Network Analysis

This type of analysis focuses on the structure and properties of the network as a whole. This might include measures of network cohesion, centralization, and density. It also looks at the overall distribution of relationships and identifies key groups or clusters within the network.

Ego Network Analysis

In this type of analysis, the focus is on a single actor (the ‘ego’) and their immediate network (the ‘alters’). It’s often used when interest is in the personal networks of individuals. Measures can include the size of the network, the composition of the network in terms of the types of ties and nodes, and measures of network density or diversity.

Two-mode (or Bipartite) Network Analysis

This type of SNA is used when there are two different types of nodes, and connections are only possible between nodes of different types (not within types). For example, authors and the books they write, or actors and the movies they appear in. In such a network, you can study the connections between nodes of one type, mediated by nodes of the other type.

Dynamic Network Analysis (DNA)

This is used to study how social networks evolve over time. This could involve studying how ties between actors develop or disappear, or how actors move around within the network. In addition to traditional network measures, DNA also considers measures that are dynamic in nature, such as change in centrality over time.

Semantic Network Analysis

This type of SNA focuses on the relationships between concepts or ideas, rather than individuals or organizations. For instance, semantic network analysis could map out how different scientific concepts are related to each other in the literature.

Social Media Network Analysis

A specialized form of SNA, this deals with the study of social relationships as expressed through social media platforms. It allows for the mapping and measuring of relationships and flows between people, groups, organizations, computers, URLs, and other connected information/knowledge entities.

Social Network Analysis Techniques

Social Network Analysis involves various techniques to understand the structure and patterns of relationships among actors (people, organizations, etc.) in a network. These techniques may be mathematical, visual, or computational, and often involve the use of specialized software. Here are several common SNA techniques:

Network Visualization

One of the most basic SNA techniques involves creating a visual representation of the network. This can help to reveal patterns and structures within the network that may not be immediately obvious from the raw data. There are various ways to create such visualizations, depending on the specifics of the network and the goals of the analysis. Software such as Gephi or Cytoscape can be used for network visualization.

Centrality Measures

These are techniques used to identify the most important nodes within a network. Various measures of centrality exist, each highlighting different aspects of a node’s position in the network. These include degree centrality (the number of connections a node has), betweenness centrality (how often a node appears on the shortest path between other nodes), closeness centrality (how quickly a node can reach all other nodes in the network), and eigenvector centrality (a measure of the influence of a node in a network).

Community Detection

Also known as clustering, this technique aims to identify groups of nodes that are more closely connected with each other than with the rest of the network. This can help to reveal sub-groups or communities within the network.

Structural Equivalence and Blockmodeling

Structural equivalence is a measure of how similarly two nodes are connected to the rest of the network. Nodes that are structurally equivalent often play similar roles in the network. Blockmodeling is a technique used to simplify a network by grouping together structurally equivalent nodes.

Dynamic Network Analysis

This involves studying how a network changes over time. This can help to reveal patterns of network evolution, including how relationships form and dissolve, how centrality measures change over time, and how communities evolve.

Network Correlation and Regression

These are statistical techniques used to identify and test for patterns within the network. For example, one might use these techniques to test whether nodes with certain characteristics are more likely to form connections with each other.

Social Network Analysis Tools

There are several tools available that can be used to conduct Social Network Analysis (SNA). These range from open-source software to commercial offerings, each with their own strengths and weaknesses. Here are a few examples:

• Gephi : Gephi is an open-source, interactive visualization and exploration platform for all kinds of networks and complex systems. It’s user-friendly and allows users to interactively manipulate the network visualization, perform network analysis, and export results in various formats.
• UCINet : UCINet is a comprehensive package for the analysis of social network data as well as other 1-mode and 2-mode data. It’s widely used in social science research.
• NetDraw : Often used in conjunction with UCINet, NetDraw is a free tool for visualizing networks. It supports the visualization of large networks and allows for various customization options.
• Pajek : Pajek is a program for the analysis and visualization of large networks. It’s an extensive tool, offering a range of complex network metrics, and is free for non-commercial use.
• NodeXL : NodeXL is a free, open-source template for Microsoft Excel that allows users to display and analyze network graphs. Its integration with Excel makes it user-friendly, particularly for those already familiar with Excel.
• Cytoscape : Originally designed for biological research, Cytoscape is now a popular open-source software platform for visualizing complex networks and integrating these with any type of attribute data.
• SocioViz : SocioViz is a social media analytics platform for Twitter data, focused on network analysis and visualization. It’s a powerful tool for researchers interested in online social networks.
• NetworkX : NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It integrates well with other scientific Python tools like SciPy and Matplotlib.
• igraph : igraph is a library available in R, Python, and C for creating, manipulating, and analyzing networks. It’s highly efficient and can handle large networks.
• RSiena : RSiena is an R package dedicated to the statistical analysis of network data, with a particular focus on longitudinal social networks.

Social Network Analysis Examples

Social Network Analysis Examples are as follows:

• Public Health – COVID-19 Pandemic : During the COVID-19 pandemic, SNA was used to model the spread of the virus. The interactions between individuals were mapped as a network, helping identify super-spreader events and informing public health interventions.
• Business – Google’s “PageRank” Algorithm : Google’s PageRank algorithm, which determines the order of search engine results, is a type of SNA. It considers web pages as nodes and hyperlinks as connections, determining a page’s importance by looking at the number and quality of links to it.
• Sociology – Stanley Milgram’s “Small World” Experiment : This is one of the most famous social network experiments, where Milgram demonstrated that any two people in the United States are separated on average by only six acquaintances, leading to the phrase “six degrees of separation.”
• Online Social Networks – Facebook’s “People You May Know” Feature : Facebook uses SNA to suggest new friends. The platform analyzes your current network and suggests people you’re likely to know, typically friends of friends or people who share common networks.
• Criminal Network Analysis – Capture of Osama bin Laden : SNA was reportedly used in the operation to capture Osama bin Laden. By mapping the social connections of known associates, intelligence agencies were able to locate the Al-Qaeda leader.
• Academic Research – Collaboration Networks : SNA is used in scientometrics to analyze collaboration networks among researchers . For example, a study on co-authorship networks in scientific articles can reveal patterns of collaboration and the flow of information in different disciplines.

When to use Social Network Analysis

Social Network Analysis is a powerful tool for studying the relationships between entities (like people, organizations, or even concepts) and the overall structure of these relationships. Here are several situations when SNA might be particularly useful:

• Understanding Complex Systems : SNA is well-suited to studying complex, interconnected systems. If you’re interested in not just individual entities but also the relationships between them, SNA can provide valuable insights.
• Identifying Key Actors : SNA can help identify the most important entities in a network based on their position and connections. These might be influential people within a social network, critical servers in a computer network, or key scholars in a field of study.
• Studying Diffusion Processes : If you’re interested in how something (like information, behaviors, diseases) spreads through a network, SNA can be a valuable tool. It allows for the examination of diffusion pathways and identification of nodes that speed up or hinder diffusion.
• Detecting Communities : SNA can be used to identify clusters or communities within a network. These might be groups of friends within a social network, clusters of companies in a business network, or research clusters in scientific collaboration networks.
• Mapping Out Large Systems : In cases where you have a large system of many interconnected entities, SNA can provide a visual representation of the system, making it easier to understand and analyze.
• Investigating Structural Roles : If you’re interested in the roles individuals or entities play within their network, SNA offers methods to classify these roles based on the pattern of their relationships.

Purpose of Social Network Analysis

Social Network Analysis serves a wide range of purposes across different fields, given its versatile nature. Here are several key purposes:

• Understanding Network Structure : One of the key purposes of SNA is to understand the structure of relationships between actors within a network. This includes understanding how the network is organized, the distribution of connections, and the patterns of interaction.
• Identifying Key Actors or Nodes : SNA can identify crucial nodes within a network. These could be individuals with many connections, or nodes that serve as critical links between different parts of the network. In a business, for instance, such nodes might be key influencers or innovators.
• Detecting Subgroups or Communities : SNA can identify clusters or communities within a network, i.e., groups of nodes that are more connected to each other than to the rest of the network. This can be valuable in numerous contexts, from identifying communities in social media networks to detecting collaboration clusters in scientific networks.
• Analyzing Information or Disease Spread : In public health and communication studies, SNA is used to study the patterns and pathways of information or disease spread. Understanding these patterns can be critical for designing effective interventions or campaigns.
• Analyzing Social Capital : SNA can help understand an individual or group’s social capital – the resources they can access through their network relationships. This analysis can offer insights into power dynamics, access to resources, and inequality within a network.
• Studying Network Dynamics : SNA can examine how networks change over time. This could involve studying how relationships form or dissolve, how centrality measures change over time, or how communities evolve.
• Predicting Future Interactions : SNA can be used to predict future interactions or relationships within a network, which can be useful in a variety of settings such as recommender systems, predicting disease spread, or forecasting emerging trends in social media.

Applications of Social Network Analysis

Social Network Analysis has a wide range of applications across different disciplines due to its capacity to analyze relationships and interactions. Here are some common areas where it is applied:

• Public Health : SNA can be used to understand the spread of infectious diseases within a community or globally. It helps identify “super spreaders” and optimizes strategies for vaccination or containment.
• Business and Organizations : Companies use SNA to analyze communication and workflow patterns, enhance collaboration, boost efficiency, and detect key influencers within their organization. It can also be applied in understanding and leveraging informal networks within a business.
• Social Media Analysis : On platforms like Facebook, Twitter, or Instagram, SNA helps analyze user behavior, track information dissemination, identify influencers, detect communities, and develop recommendation systems.
• Criminal Justice : Law enforcement and intelligence agencies use SNA to understand the structure of criminal or terrorist networks, identify key figures, and predict future activities.
• Internet Infrastructure : SNA helps in mapping the internet, identifying critical nodes, and developing strategies for robustness against cyberattacks or outages.
• Marketing : In marketing, SNA can track the diffusion of advertising messages, identify influential consumers for targeted marketing, and understand consumer behavior and brand communities.
• Scientometrics : SNA is used in academic research to map co-authorship networks or citation networks. It can uncover patterns of collaboration and the flow of knowledge in scientific fields.
• Politics and Policy Making : SNA can help understand political alliances, lobby networks, or policy networks, which can be critical for strategic decision-making in politics.
• Ecology : In ecological studies, SNA can help understand the relationships between different species in an ecosystem, providing valuable insights into ecological dynamics.

Advantages of Social Network Analysis

Social Network Analysis offers several advantages when studying complex systems and relationships. Here are a few key advantages:

• Reveals Complex Relationships : SNA allows for the study of relationships between entities (be they people, organizations, computers, etc.) in a way that many other methodologies do not. It emphasizes the importance of these relationships and helps reveal complex interaction patterns.
• Identifies Key Players : SNA can identify the most influential or important nodes in a network, whether they are individuals within a social network, key servers in an internet network, or central scholars in an academic field.
• Unveils Network Structure and Communities : SNA can help visualize the overall structure of a network and can reveal communities or clusters of nodes within a network. This can provide valuable insights into the organization and division of a network.
• Tracks Changes Over Time : Dynamic SNA allows the study of networks over time. This can help to track changes in the network structure, the role of specific nodes, or the flow of information or resources through the network.
• Helps Predict Future Interactions : Based on the analysis of current and past relationships, SNA can be used to predict future interactions, which can be useful in many fields including public health, marketing, and national security.
• Aids in Designing Effective Strategies : The insights gained from SNA can be used to design targeted strategies, whether that’s intervening in the spread of misinformation online, designing a targeted marketing campaign, disrupting a criminal network, or managing collaboration in an organization.
• Versatility : SNA can be applied to a vast array of fields, from sociology to computer science, biology to business, making it a versatile tool.

Disadvantages of Social Network Analysis

While Social Network Analysis is a powerful tool with wide-ranging applications, it also has certain limitations and disadvantages that are important to consider:

• Data Collection Challenges : Collecting complete and accurate network data can be a major challenge. For larger networks, it may be nearly impossible to collect data on all relevant relationships. There’s also a risk of response bias, as people may forget, overlook, or misinterpret their relationships when providing data.
• Time and Resource Intensive : Collecting network data, especially from primary sources, can be extremely time-consuming and expensive. Additionally, analyzing network data can also require significant computational resources for larger networks.
• Complexity : SNA involves complex concepts and measures, which can be difficult to understand without specialized knowledge. This complexity can make it difficult to communicate findings to a non-technical audience.
• Privacy and Ethical Concerns : SNA often involves sensitive data about individuals’ relationships and interactions, raising important privacy and ethical concerns. It’s important to handle this data carefully to respect individuals’ privacy.
• Static Snapshots : Traditional SNA often provides a static snapshot of a network at a particular point in time, which may not capture the dynamic nature of social relationships. While dynamic SNA does exist, it adds additional complexity and data demands.
• Dependence on Quality of Data : The insights and conclusions drawn from SNA are only as good as the data used. Incomplete, inaccurate, or biased data can lead to misleading results.
• Difficulties in Establishing Causality : While SNA can reveal patterns and associations in network data, it can be difficult to establish causal relationships. For instance, do strong connections between two individuals lead to similar behavior, or does similar behavior lead to strong connections?
• Assumptions about Relationships : SNA often assumes that relationships are equally important, which might not always be the case. Different relationships might have different strengths or meanings, which can be challenging to represent in a network.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Digital Ethnography – Types, Methods and Examples

Predictive Analytics – Techniques, Tools and...

Big Data Analytics -Types, Tools and Methods

Diagnostic Analytics – Methods, Tools and...

Blockchain Research – Methods, Types and Examples

What is Data Science? Components, Process and Tools

This paper is in the following e-collection/theme issue:

Published on 11.4.2024 in Vol 26 (2024)

Patients’ Experiences With Digitalization in the Health Care System: Qualitative Interview Study

Authors of this article:

Original Paper

• Christian Gybel Jensen 1 * , MA   ; 
• Frederik Gybel Jensen 1 * , MA   ; 
• Mia Ingerslev Loft 1, 2 * , MSc, PhD  

1 Department of Neurology, Rigshospitalet, Copenhagen, Denmark

2 Institute for People and Technology, Roskilde University, Roskilde, Denmark

*all authors contributed equally

Corresponding Author:

Mia Ingerslev Loft, MSc, PhD

Department of Neurology

Rigshospitalet

Inge Lehmanns Vej 8

Phone: 45 35457076

Email: [email protected]

Background: The digitalization of public and health sectors worldwide is fundamentally changing health systems. With the implementation of digital health services in health institutions, a focus on digital health literacy and the use of digital health services have become more evident. In Denmark, public institutions use digital tools for different purposes, aiming to create a universal public digital sector for everyone. However, this digitalization risks reducing equity in health and further marginalizing citizens who are disadvantaged. Therefore, more knowledge is needed regarding patients’ digital practices and experiences with digital health services.

Objective: This study aims to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients in the neurology field and address the following research questions: (1) How do patients use digital services and digital tools? (2) How do they experience them?

Methods: We used a qualitative design with a hermeneutic approach. We conducted 31 semistructured interviews with patients who were hospitalized or formerly hospitalized at the department of neurology in a hospital in Denmark. The interviews were audio recorded and subsequently transcribed. The text from each transcribed interview was analyzed using manifest content analysis.

Results: The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools. Our findings show that digital tools were experienced differently, and specific conditions were important for the possibility of engaging in digital practices, including having access to social resources; possessing physical, cognitive, and communicative capabilities; and feeling motivated, secure, and comfortable. These prerequisites were necessary for participants to have positive experiences using digital tools in the health care system. Those who did not have these prerequisites experienced challenges and, in some cases, felt left out.

Conclusions: Experiences with digital practices and digital health services are complex and multifaceted. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges. To ensure that digitalization does not create inequities in health, it is necessary for developers and institutions to be aware of the differences in digital health literacy, focus on simplifying communication with patients and next of kin, and find flexible solutions for citizens who are disadvantaged.

Introduction

In 2022, the fourth most googled question in Denmark was, “Why does MitID not work?” [ 1 ]. MitID (My ID) is a digital access tool that Danes use to enter several different private and public digital services, from bank accounts to mail from their municipality or the state. MitID is a part of many Danish citizens’ everyday lives because the public sector in Denmark is digitalized in many areas. In recent decades, digitalization has changed how governments and people interact and has demonstrated the potential to change the core functions of public sectors and delivery of public policies and services [ 2 ]. When public sectors worldwide become increasingly digitalized, this transformation extends to the public health sectors as well, and some studies argue that we are moving toward a “digital public health era” that is already impacting the health systems and will fundamentally change the future of health systems [ 3 ]. While health systems are becoming more digitalized, it is important that both patients and digitalized systems adapt to changes in accordance with each other. Digital practices of people can be understood as what people do with and through digital technologies and how people relate to technology [ 4 ]. Therefore, it is relevant to investigate digital practices and how patients perceive and experience their own use of digital tools and services, especially in relation to existing digital health services. In our study, we highlight a broad perspective on experiences with digital practices and particularly add insight into the challenges with digital practices faced by patients who have acute or chronic illness, with some of them also experiencing physical, communicative, or cognitive difficulties.

An international Organization for Economic Cooperation and Development report indicates that countries are digitalized to different extents and in different ways; however, this does not mean that countries do not share common challenges and insights into the implementation of digital services [ 2 ].

In its global Digital Government Index, Denmark is presented as one of the leading countries when it comes to public digitalization [ 2 ]. Recent statistics indicate that approximately 97% of Danish families have access to the internet at home [ 5 ]. The Danish health sector already offers many different digital services, including web-based delivery of medicine, e-consultations, patient-related outcome questionnaires, and seeking one’s own health journal or getting test results through; “Sundhed” [ 6 ] (the national health portal) and “Sundhedsjournalen” (the electronic patient record); or the apps “Medicinkortet” (the shared medication record), “Minlæge” (My Doctor, consisting of, eg, communication with the general practitioner), or “MinSP” (My Health Platform, consisting of, eg, communication with health care staff in hospitals) [ 6 - 8 ].

The Danish Digital Health Strategy from 2018 aims to create a coherent and user-friendly digital public sector for everyone [ 9 ], but statistics indicate that certain groups in society are not as digitalized as others. In particular, the older population uses digital services the least, with 5% of people aged 65 to 75 years and 18% of those aged 75 to 89 years having never used the internet in 2020 [ 5 ]. In parts of the literature, it has been problematized how the digitalization of the welfare state is related to the marginalization of older citizens who are socially disadvantaged [ 10 ]. However, statistics also indicate that the probability of using digital tools increases significantly as a person’s experience of using digital tools increases, regardless of their age or education level [ 5 ].

Understanding the digital practices of patients is important because they can use digital tools to engage with the health system and follow their own health course. Researching experiences with digital practices can be a way to better understand potential possibilities and barriers when patients use digital health services. With patients becoming more involved in their own health course and treatment, the importance of patients’ health literacy is being increasingly recognized [ 11 ]. The World Health Organization defines health literacy as the “achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions” [ 12 ]. Furthermore, health literacy can be described as “a person’s knowledge and competencies to meet complex demands of health in modern society, ” and it is viewed as a critical step toward patient empowerment [ 11 , 12 ]. In a digitalized health care system, this also includes the knowledge, capabilities, and resources that individuals require to use and benefit from eHealth services, that is, “digital health literacy (eHealth literacy)” [ 13 ]. An eHealth literacy framework created by Norgaard et al [ 13 ] identified that different aspects, for example, the ability to process information and actively engage with digital services, can be viewed as important facets of digital health literacy. This argument is supported by studies that demonstrate how patients with cognitive and communicative challenges experience barriers to the use of digital tools and require different approaches in the design of digital solutions in the health sector [ 14 , 15 ]. Access to digital services and digital literacy is becoming increasingly important determinants of health, as people with digital literacy and access to digital services can facilitate improvement of health and involvement in their own health course [ 16 ].

The need for a better understanding of eHealth literacy and patients’ capabilities to meet public digital services’ demands as well as engage in their own health calls for a deeper investigation into digital practices and the use of digital tools and services from the perspective of patients with varying digital capabilities. Important focus areas to better understand digital practices and related challenges have already been highlighted in various studies. They indicate that social support, assessment of value in digital services, and systemic assessment of digital capabilities are important in the use and implementation of digital tools, and they call for better insight into complex experiences with digital services [ 13 , 17 , 18 ]. Therefore, we aimed to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients, addressing the following research questions: how do patients use digital services and digital tools, and how do they experience them?

We aimed to investigate digital practices and experiences with digital health services and digital tools; therefore, we used a qualitative design and adopted a hermeneutic approach as the point of departure, which means including preexisting knowledge of digital practices but also providing room for new comprehension [ 19 ]. Our interpretive approach is underpinned by the philosophical hermeneutic approach by Gadamer et al [ 19 ], in which they described the interpretation process as a “hermeneutic circle,” where the researcher enters the interpretation process with an open mind and historical awareness of a phenomenon (preknowledge). We conducted semistructured interviews using an interview guide. This study followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist [ 20 ].

Setting and Participants

To gain a broad understanding of experiences with public digital health services, a purposive sampling strategy was used. All 31 participants were hospitalized or formerly hospitalized patients in a large neurological department in the capital of Denmark ( Table 1 ). We assessed whether including patients from the neurological field would give us a broad insight into the experiences of digital practices from different perspectives. The department consisted of, among others, 8 inpatient units covering, for example, acute neurology and stroke units, from which the patients were recruited. Patients admitted to a neurological department can have both acute and transient neurological diseases, such as infections in the brain, stroke, or blood clot in the brain from which they can recover completely or have persistent physical and mental difficulties, or experience chronic neurological and progressive disorders such as Parkinson disease and dementia. Some patients hospitalized in neurological care will have communicative and cognitive difficulties because of their neurological disorders. Nursing staff from the respective units helped the researchers (CGJ, FGJ, and MIL) identify patients who differed in terms of gender, age, and severity of neurological illness. Some patients (6/31, 19%) had language difficulties; however, a speech therapist assessed them as suitable participants. We excluded patients with severe cognitive difficulties and those who were not able to speak the Danish language. Including patients from the field of neurology provided an opportunity to study the experience of digital health practice from various perspectives. Hence, the sampling strategy enabled the identification and selection of information-rich participants relevant to this study [ 21 ], which is the aim of qualitative research. The participants were invited to participate by either the first (CGJ) or last author (MIL), and all invited participants (31/31, 100%) chose to participate.

All 31 participants were aged between 40 to 99 years, with an average age of 71.75 years ( Table 1 ). Out of the 31 participants, 10 (32%) had physical disabilities or had cognitive or communicative difficulties due to sequela in relation to neurological illness or other physical conditions.

Data Collection

The 31 patient interviews were conducted over a 2-month period between September and November 2022. Of the 31 patients, 20 (65%) were interviewed face-to-face at the hospital in their patient room upon admission and 11 (35%) were interviewed on the phone after being discharged. The interviews had a mean length of 20.48 minutes.

We developed a semistructured interview guide ( Table 2 ). The interview questions were developed based on the research aim, findings from our preliminary covering of literature in the field presented in the Introduction section, and identified gaps that we needed to elaborate on to be able to answer our research question [ 22 ]. The semistructured interview guide was designed to support the development of a trusting relationship and ensure the relevance of the interviews’ content [ 22 ]. The questions served as a prompt for the participants and were further supported by questions such as “please tell me more” and “please elaborate” throughout the interview, both to heighten the level of detail and to verify our understanding of the issues at play. If the participant had cognitive or communicative difficulties, communication was supported using a method called Supported Communication for Adults with Aphasia [ 23 ] during the interview.

The interviews were performed by all authors (CGJ, FGJ, and MIL individually), who were skilled in conducting interviews and qualitative research. The interviewers are not part of daily clinical practice but are employed in the department of neurology from where the patients were recruited. All interviews were audio recorded and subsequently transcribed verbatim by all 3 authors individually.

a PRO: patient-related outcome.

Data Analysis

The text from each transcribed interview was analyzed using manifest content analysis, as described by Graneheim and Lundman [ 24 ]. Content analysis is a method of analyzing written, verbal, and visual communication in a systematic way [ 25 ]. Qualitative content analysis is a structured but nonlinear process that requires researchers to move back and forth between the original text and parts of the text during the analysis. Manifest analysis is the descriptive level at which the surface structure of the text central to the phenomenon and the research question is described. The analysis was conducted as a collaborative effort between the first (CGJ) and last authors (MIL); hence, in this inductive circular process, to achieve consistency in the interpretation of the text, there was continued discussion and reflection between the researchers. The transcriptions were initially read several times to gain a sense of the whole context, and we analyzed each interview. The text was initially divided into domains that reflected the lowest degree of interpretation, as a rough structure was created in which the text had a specific area in common. The structure roughly reflected the interview guide’s themes, as guided by Graneheim and Lundman [ 24 ]. Thereafter, the text was divided into meaning units, condensed into text-near descriptions, and then abstracted and labeled further with codes. The codes were categorized based on similarities and differences. During this process, we discussed the findings to reach a consensus on the content, resulting in the final 4 categories presented in this paper.

Ethical Considerations

The interviewees received oral and written information about the study and its voluntary nature before the interviews. Written informed consent was obtained from all participants. Participants were able to opt of the study at any time. Data were anonymized and stored electronically on locked and secured servers. The Ethics Committee of the Capitol Region in Denmark was contacted before the start of the study. This study was registered and approved by the ethics committee and registered under the Danish Data Protection Agency (number P2021-839). Furthermore, the ethical principles of the Declaration of Helsinki were followed for this study.

The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools.

Social Resources as a Digital Lifeline

Throughout the analysis, it became evident that access to both material and social resources was of great importance when using digital tools. Most participants already possessed and had easy access to a computer, smartphone, or tablet. The few participants who did not own the necessary digital tools told us that they did not have the skills needed to use these tools. For these participants, the lack of material resources was tied particularly to a lack of knowledge and know-how, as they expressed that they would not know where to start after buying a computer—how to set it up, connect it to the internet, and use its many systems.

However, possessing the necessary material resources did not mean that the participants possessed the knowledge and skill to use digital tools. Furthermore, access to material resources was also a question of having access to assistance when needed. Some participants who had access to a computer, smartphone, and tablet and knew how to use these tools still had to obtain help when setting up hardware, updating software, or getting a new device. These participants were confident in their own ability to use digital devices but also relied on family, friends, and neighbors in their everyday use of these tools. Certain participants were explicitly aware of their own use of social resources when expressing their thoughts on digital services in health care systems:

I think it is a blessing and a curse. I think it is both. I would say that if I did not have someone around me in my family who was almost born into the digital world, then I think I would be in trouble. But I feel sorry for those who do not have that opportunity, and I know quite a few who do not. They get upset, and it’s really frustrating. [Woman, age 82 years]

The participants’ use of social resources indicates that learning skills and using digital tools are not solely individual tasks but rather continuously involve engagement with other people, particularly whenever a new unforeseen problem arises or when the participants want a deeper understanding of the tools they are using:

If tomorrow I have to get a new ipad...and it was like that when I got this one, then I had to get XXX to come and help me move stuff and he was sweet to help with all the practical stuff. I think I would have cursed a couple of times (if he hadn’t been there), but he is always helpful, but at the same time he is also pedagogic so I hope that next time he showed me something I will be able to do it. [Man, age 71 years]

For some participants, obtaining assistance from a more experienced family member was experienced as an opportunity to learn, whereas for other participants, their use of public digital services was even tied directly to assistance from a spouse or family member:

My wife, she has access to mine, so if something comes up, she can just go in and read, and we can talk about it afterwards what (it is). [Man, age 85 years]

The participants used social resources to navigate digital systems and understand and interpret communication from the health care system through digital devices. Another example of this was the participants who needed assistance to find, answer, and understand questionnaires from the health care department. Furthermore, social resources were viewed as a support system that made participants feel more comfortable and safer when operating digital tools. The social resources were particularly important when overcoming unforeseen and new challenges and when learning new skills related to the use of digital tools. Participants with physical, cognitive, and communicative challenges also explained how social resources were of great importance in their ability to use digital tools.

Possessing the Necessary Capabilities

The findings indicated that possessing the desire and knowing how to use digital tools are not always enough to engage with digital services successfully. Different health issues can carry consequences for motor skills and mobility. Some of these consequences were visibly affecting how our participants interacted with digital devices, and these challenges were somewhat easy to discover. However, our participants revealed hidden challenges that posed difficulties. In some specific cases, cognitive and communicative inabilities can make it difficult to use digital tools, and this might not always be clear until the individual tries to use a device’s more complex functions. An example of this is that some participants found it easy to turn on a computer and use it to write but difficult to go through security measures on digital services or interpret and understand digital language. Remembering passwords and logging on to systems created challenges, particularly for those experiencing health issues that directly affect memory and cognitive abilities, who expressed concerns about what they were able to do through digital tools:

I think it is very challenging because I would like to use it how I used to before my stroke; (I) wish that everything (digital skills) was transferred, but it just isn’t. [Man, age 80 years]

Despite these challenges, the participants demonstrated great interest in using digital tools, particularly regarding health care services and their own well-being. However, sometimes, the challenges that they experienced could not be conquered merely by motivation and good intentions. Another aspect of these challenges was the amount of extra time and energy that the participants had to spend on digital services. A patient diagnosed with Parkinson disease described how her symptoms created challenges that changed her digital practices:

Well it could for example be something like following a line in the device. And right now it is very limited what I can do with this (iPhone). Now I am almost only using it as a phone, and that is a little sad because I also like to text and stuff, but I also find that difficult (...) I think it is difficult to get an overview. [Woman, age 62 years]

Some participants said that after they were discharged from the hospital, they did not use the computer anymore because it was too difficult and too exhausting , which contributed to them giving up . Using digital tools already demanded a certain amount of concentration and awareness, and some diseases and health conditions affected these abilities further.

Big Feelings as Facilitators or Barriers

The findings revealed a wide range of digital practices in which digital tools were used as a communication device, as an entertainment device, and as a practical and informative tool for ordering medicine, booking consultations, asking health-related questions, or receiving email from public institutions. Despite these different digital practices, repeating patterns and arguments appeared when the participants were asked why they learned to use digital tools or wanted to improve their skills. A repeating argument was that they wanted to “follow the times, ” or as a participant who was still not satisfied with her digital skills stated:

We should not go against the future. [Woman, age 89 years]

The participants expressed a positive view of the technological developments and possibilities that digital devices offered, and they wanted to improve their knowledge and skills related to digital practice. For some participants, this was challenging, and they expressed frustration over how technological developments “moved too fast ,” but some participants interpreted these challenges as a way to “keep their mind sharp. ”

Another recurring pattern was that the participants expressed great interest in using digital services related to the health care system and other public institutions. The importance of being able to navigate digital services was explicitly clear when talking about finding test answers, written electronic messages, and questionnaires from the hospital or other public institutions. Keeping up with developments, communicating with public institutions, and taking an interest in their own health and well-being were described as good reasons to learn to use digital tools.

However, other aspects also affected these learning facilitators. Some participants felt alienated while using digital tools and described the practice as something related to feelings of anxiety, fear, and stupidity as well as something that demanded “a certain amount of courage. ” Some participants felt frustrated with the digital challenges they experienced, especially when the challenges were difficult to overcome because of their physical conditions:

I get sad because of it (digital challenges) and I get very frustrated and it takes a lot of time because I have difficulty seeing when I look away from the computer and have to turn back again to find out where I was and continue there (...) It pains me that I have to use so much time on it. [Man, age 71 years]

Fear of making mistakes, particularly when communicating with public institutions, for example, the health care system, was a common pattern. Another pattern was the fear of misinterpreting the sender and the need to ensure that the written electronic messages were actually from the described sender. Some participants felt that they were forced to learn about digital tools because they cared a lot about the services. Furthermore, fears of digital services replacing human interaction were a recurring concern among the participants. Despite these initial and recurring feelings, some participants learned how to navigate the digital services that they deemed relevant. Another recurring pattern in this learning process was repetition, the practice of digital skills, and consistent assistance from other people. One participant expressed the need to use the services often to remember the necessary skills:

Now I can figure it out because now I’ve had it shown 10 times. But then three months still pass... and then I think...how was it now? Then I get sweat on my forehead (feel nervous) and think; I’m not an idiot. [Woman, age 82 years]

For some participants, learning how to use digital tools demanded time and patience, as challenges had to be overcome more than once because they reappeared until the use of digital tools was more automatized into their everyday lives. Using digital tools and health services was viewed as easier and less stressful when part of everyday routines.

Life Without Digital Tools: Not a Free Choice

Even though some participants used digital tools daily, other participants expressed that it was “too late for them.” These participants did not view it as a free choice but as something they had to accept that they could not do. They wished that they could have learned it earlier in life but did not view it as a possibility in the future. Furthermore, they saw potential in digital services, including digital health care services, but they did not know exactly what services they were missing out on. Despite this lack of knowledge, they still felt sad about the position they were in. One participant expressed what she thought regarding the use of digital tools in public institutions:

Well, I feel alright about it, but it is very, very difficult for those of us who do not have it. Sometimes you can feel left out—outside of society. And when you do not have one of those (computers)...A reference is always made to w and w (www.) and then you can read on. But you cannot do that. [Woman, age 94 years]

The feeling of being left out of society was consistent among the participants who did not use digital tools. To them, digital systems seemed to provide unfair treatment based on something outside of their own power. Participants who were heavily affected by their medical conditions and could not use digital services also felt left out because they saw the advantages of using digital tools. Furthermore, a participant described the feelings connected to the use of digital tools in public institutions:

It is more annoying that it does not seem to work out in my favour. [Woman, age 62 years]

These statements indicated that it is possible for individuals to want to use digital tools and simultaneously find them too challenging. These participants were aware that there are consequences of not using digital tools, and that saddens them, as they feel like they are not receiving the same treatment as other people in society and the health care system.

Principal Findings

The insights from our findings demonstrated that our participants had different digital practices and different experiences with digital tools and services; however, the analysis also highlighted patterns related to how digital services and tools were used. Specific conditions were important for the possibility of digital practice, including having access to social resources; possessing the necessary capabilities; and feeling motivated, secure, and comfortable . These prerequisites were necessary to have positive experiences using digital tools in the health care system, although some participants who lived up to these prerequisites were still skeptical toward digital solutions. Others who did not live up to these prerequisites experienced challenges and even though they were aware of opportunities, this awareness made them feel left out. A few participants even viewed the digital tools as a threat to their participation in society. This supports the notion of Norgaard et al [ 13 ] that the attention paid to digital capability demands from eHealth systems is very important. Furthermore, our findings supported the argument of Hjeltholt and Papazu [ 17 ] that it is important to better understand experiences related to digital services. In our study, we accommodate this request and bring forth a broad perspective on experiences with digital practices; we particularly add insight into the challenges with digital practices for patients who also have acute or chronic illness, with some of them also experiencing physical, communicative, and cognitive difficulties. To our knowledge, there is limited existing literature focusing on digital practices that do not have a limited scope, for example, a focus on perspectives on eHealth literacy in the use of apps [ 26 ] or intervention studies with a focus on experiences with digital solutions, for example, telemedicine during the COVID-19 pandemic [ 27 ]. As mentioned by Hjeltholt et al [ 10 ], certain citizens are dependent on their own social networks in the process of using and learning digital tools. Rasi et al [ 28 ] and Airola et al [ 29 ] argued that digital health literacy is situated and should include the capabilities of the individual’s social network. Our findings support these arguments that access to social resources is an important condition; however, the findings also highlight that these resources can be particularly crucial in the use of digital health services, for example, when interpreting and understanding digital and written electronic messages related to one’s own health course or when dealing with physical, cognitive, and communicative disadvantages. Therefore, we argue that the awareness of the disadvantages is important if we want to understand patients’ digital capabilities, and the inclusion of the next of kin can be evident in unveiling challenges that are unknown and not easily visible or when trying to reach patients with digital challenges through digital means.

Studies by Kayser et al [ 30 ] and Kanoe et al [ 31 ] indicated that patients’ abilities to interpret and understand digital health–related services and their benefits are important for the successful implementation of eHealth services—an argument that our findings support. Health literacy in both digital and physical contexts is important if we want to understand how to better design and implement services. Our participants’ statements support the argument that communication through digital means cannot be viewed as similar to face-to-face communication and that an emphasis on digital health literacy demonstrates how health systems are demanding different capabilities from the patients [ 13 ]. We argue that it is important to communicate the purposes of digital services so that both the patient and their next of kin know why they participate and how it can benefit them. Therefore, it is important to make it as clear as possible that digital health services can benefit the patient and that these services are developed to support information, communication, and dialogue between patients and health professionals. However, our findings suggest that even after interpreting and understanding the purposes of digital health services, some patients may still experience challenges when using digital tools.

Therefore, it is important to understand how and why patients learn digital skills, particularly because both experience with digital devices and estimation of the value of digital tools have been highlighted as key factors for digital practices [ 5 , 18 ]. Our findings indicate that a combination of these factors is important, as recognizing the value of digital tools was not enough to facilitate the necessary learning process for some of our participants. Instead, our participants described the use of digital tools as complex and continuous processes in which automation of skills, assistance from others, and time to relearn forgotten knowledge were necessary and important facilitators for learning and understanding digital tools as well as becoming more comfortable and confident in the use of digital health services. This was particularly important, as it was more encouraging for our participants to learn digital tools when they felt secure, instead of feeling afraid and anxious, a point that Bailey et al [ 18 ] also highlighted. The value of digital solutions and the will to learn were greater when challenges were viewed as something to overcome and learn from instead of something that created a feeling of being stupid. This calls for attention on how to simplify and explain digital tools and services so that users do not feel alienated. Our findings also support the argument that digital health literacy should take into account emotional well-being related to digital practice [ 32 ].

The various perspectives that our participants provided regarding the use of digital tools in the health care system indicate that patients are affected by the use of digital health services and their own capabilities to use digital tools. Murray et al [ 33 ] argued that the use of digital tools in health sectors has the potential to improve health and health delivery by improving efficacy, efficiency, accessibility, safety, and personalization, and our participants also highlighted these positive aspects. However, different studies found that some patients, particularly older adults considered socially vulnerable, have lower digital health literacy [ 10 , 34 , 35 ], which is an important determinant of health and may widen disparities and inequity in health care [ 16 ]. Studies on older adult populations’ adaptation to information and communication technology show that engaging with this technology can be limited by the usability of technology, feelings of anxiety and concern, self-perception of technology use, and the need for assistance and inclusive design [ 36 ]. Our participants’ experiences with digital practices support the importance of these focus areas, especially when primarily older patients are admitted to hospitals. Furthermore, our findings indicate that some older patients who used to view themselves as being engaged in their own health care felt more distanced from the health care system because of digital services, and some who did not have the capabilities to use digital tools felt that they were treated differently compared to the rest of society. They did not necessarily view themselves as vulnerable but felt vulnerable in the specific experience of trying to use digital services because they wished that they were more capable. Moreover, this was the case for patients with physical and cognitive difficulties, as they were not necessarily aware of the challenges before experiencing them. Drawing on the phenomenological and feministic approach by Ahmed [ 37 ], these challenges that make patients feel vulnerable are not necessarily visible to others but can instead be viewed as invisible institutional “walls” that do not present themselves before the patient runs into them. Some participants had to experience how their physical, cognitive, or communicative difficulties affected their digital practice to realize that they were not as digitally capable as they once were or as others in society. Furthermore, viewed from this perspective, our findings could be used to argue that digital capabilities should be viewed as a privilege tied to users’ physical bodies and that digital services in the health care system are indirectly making patients without this privilege vulnerable. This calls for more attention to the inequities that digital tools and services create in health care systems and awareness that those who do not use digital tools are not necessarily indifferent about the consequences. Particularly, in a context such as the Danish one, in which the digital strategy is to create an intertwined and user-friendly public digital sector for everyone, it needs to be understood that patients have different digital capabilities and needs. Although some have not yet had a challenging experience that made them feel vulnerable, others are very aware that they receive different treatment and feel that they are on their own or that the rest of the society does not care about them. Inequities in digital health care, such as these, can and should be mitigated or prevented, and our investigation into the experiences with digital practices can help to show that we are creating standards and infrastructures that deliberately exclude the perspectives of those who are most in need of the services offered by the digital health care system [ 8 ]. Therefore, our findings support the notions that flexibility is important in the implementation of universal public digital services [ 17 ]; that it is important to adjust systems in accordance with patients’ eHealth literacy and not only improve the capabilities of individuals [ 38 ]; and that the development and improvement of digital health literacy are not solely an individual responsibility but are also tied to ways in which institutions organize, design, and implement digital tools and services [ 39 ].

Limitations

This qualitative study provided novel insights into the experiences with public digital health services from the perspective of patients in the Danish context, enabling a deeper understanding of how digital health services and digital tools are experienced and used. This helps build a solid foundation for future interventions aimed at digital health literacy and digital health interventions. However, this study has some limitations. First, the study was conducted in a country where digitalization is progressing quickly, and people, therefore, are accustomed to this pace. Therefore, readers must be aware of this. Second, the study included patients with different neurological conditions; some of their digital challenges were caused or worsened by these neurological conditions and are, therefore, not applicable to all patients in the health system. However, the findings provided insights into the patients’ digital practices before their conditions and other challenges not connected to neurological conditions shared by patients. Third, the study was broad, and although a large number of informants was included, from a qualitative research perspective, we would recommend additional research in this field to develop interventions that target digital health literacy and the use of digital health services.

Conclusions

Experiences with digital tools and digital health services are complex and multifaceted. The advantages in communication, finding information, or navigating through one’s own health course work as facilitators for engaging with digital tools and digital health services. However, this is not enough on its own. Furthermore, feeling secure and motivated and having time to relearn and practice skills are important facilitators. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges that require assistance. Digitalization of the health care system means that patients do not have the choice to opt out of using digital services without having consequences, resulting in them receiving a different treatment than others. To ensure digitalization does not create inequities in health, it is necessary for developers and the health institutions that create, design, and implement digital services to be aware of differences in digital health literacy and to focus on simplifying communication with patients and next of kin through and about digital services. It is important to focus on helping individuals meet the necessary conditions and finding flexible solutions for those who do not have the same privileges as others if the public digital sector is to work for everyone.

Acknowledgments

The authors would like to thank all the people who gave their time to be interviewed for the study, the clinical nurse specialists who facilitated interviewing patients, and the other nurses on shift who assisted in recruiting participants.

Conflicts of Interest

None declared.

• Year in search 2022. Google Trends. URL: https://trends.google.com/trends/yis/2022/DK/ [accessed 2024-04-02]
• Digital government index: 2019. Organisation for Economic Cooperation and Development. URL: https://www.oecd-ilibrary.org/content/paper/4de9f5bb-en [accessed 2024-04-02]
• Azzopardi-Muscat N, Sørensen K. Towards an equitable digital public health era: promoting equity through a health literacy perspective. Eur J Public Health. Oct 01, 2019;29(Supplement_3):13-17. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Digital practices. Umeå University. URL: https://www.umu.se/en/humlab/research/digital-practice/ [accessed 2024-04-02]
• It-anvendelse i befolkningen 2020. Danmarks Statistik. URL: https://www.dst.dk/da/Statistik/nyheder-analyser-publ/Publikationer/VisPub?cid=29450 [accessed 2024-04-02]
• Sundhed.dk homepage. Sundhed.dk. URL: https://www.sundhed.dk/borger/ [accessed 2024-04-02]
• Nøhr C, Bertelsen P, Vingtoft S, Andersen SK. Digitalisering af Det Danske Sundhedsvæsen. Odense, Denmark. Syddansk Universitetsforlag; 2019.
• Eriksen J, Ebbesen M, Eriksen KT, Hjermitslev C, Knudsen C, Bertelsen P, et al. Equity in digital healthcare - the case of Denmark. Front Public Health. Sep 6, 2023;11:1225222. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Digital health strategy. Sundhedsdatastyrelsen. URL: https://sundhedsdatastyrelsen.dk/da/english/digital_health_solutions/digital_health_strategy [accessed 2024-04-02]
• Hjelholt M, Schou J, Bojsen LB, Yndigegn SL. Digital marginalisering af udsatte ældre: arbejdsrapport 2. IT-Universitetet i København. 2018. URL: https://egv.dk/images/Projekter/Projekter_2018/EGV_arbejdsrapport_2.pdf [accessed 2024-04-02]
• Sørensen K, Van den Broucke S, Fullam J, Doyle G, Pelikan J, Slonska Z, et al. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health. Jan 25, 2012;12(1):80. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Improving health literacy. World Health Organization. URL: https://www.who.int/activities/improving-health-literacy [accessed 2024-04-02]
• Norgaard O, Furstrand D, Klokker L, Karnoe KA, Batterham R, Kayser L, et al. The e-health literacy framework: a conceptual framework for characterizing e-health users and their interaction with e-health systems. Knowl Manag E Learn. 2015;7(4). [ CrossRef ]
• Kramer JM, Schwartz A. Reducing barriers to patient-reported outcome measures for people with cognitive impairments. Arch Phys Med Rehabil. Aug 2017;98(8):1705-1715. [ CrossRef ] [ Medline ]
• Menger F, Morris J, Salis C. Aphasia in an internet age: wider perspectives on digital inclusion. Aphasiology. 2016;30(2-3):112-132. [ CrossRef ]
• Richardson S, Lawrence K, Schoenthaler AM, Mann D. A framework for digital health equity. NPJ Digit Med. Aug 18, 2022;5(1):119. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hjelholt M, Papazu I. "De har fået NemID, men det er ikke nemt for mig” - Digital rum(me)lighed i den danske velfærdsstat. Social Kritik. 2021;2021-2(163). [ FREE Full text ]
• Bailey C, Sheehan C. Technology, older persons’ perspectives and the anthropological ethnographic lens. Alter. 2009;3(2):96-109. [ CrossRef ]
• Gadamer HG, Weinsheimer HG, Marshall DG. Truth and Method. New York, NY. Crossroad Publishing Company; 1991.
• Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. Dec 16, 2007;19(6):349-357. [ CrossRef ] [ Medline ]
• Polit DF, Beck CT. Nursing Research: Generating and Assessing Evidence for Nursing Practice. Philadelphia, PA. Lippincott Williams & Wilkins, Inc; 2012.
• Kvale S, Brinkmann S. InterViews: Learning the Craft of Qualitative Research Interviewing. Thousand Oaks, CA. SAGE Publications; 2009.
• Kagan A. Supported conversation for adults with aphasia: methods and resources for training conversation partners. Aphasiology. Sep 1998;12(9):816-830. [ CrossRef ]
• Graneheim UH, Lundman B. Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Educ Today. Feb 2004;24(2):105-112. [ CrossRef ] [ Medline ]
• Krippendorff K. Content Analysis: An Introduction to Its Methodology. Thousand Oaks, CA. SAGE Publications; 1980.
• Klösch M, Sari-Kundt F, Reibnitz C, Osterbrink J. Patients' attitudes toward their health literacy and the use of digital apps in health and disease management. Br J Nurs. Nov 25, 2021;30(21):1242-1249. [ CrossRef ] [ Medline ]
• Datta P, Eiland L, Samson K, Donovan A, Anzalone AJ, McAdam-Marx C. Telemedicine and health access inequalities during the COVID-19 pandemic. J Glob Health. Dec 03, 2022;12:05051. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Rasi P, Lindberg J, Airola E. Older service users’ experiences of learning to use eHealth applications in sparsely populated healthcare settings in Northern Sweden and Finland. Educ Gerontol. Nov 24, 2020;47(1):25-35. [ CrossRef ]
• Airola E, Rasi P, Outila M. Older people as users and non-users of a video conferencing service for promoting social connectedness and well-being – a case study from Finnish Lapland. Educ Gerontol. Mar 29, 2020;46(5):258-269. [ CrossRef ]
• Kayser L, Kushniruk A, Osborne RH, Norgaard O, Turner P. Enhancing the effectiveness of consumer-focused health information technology systems through eHealth literacy: a framework for understanding users' needs. JMIR Hum Factors. May 20, 2015;2(1):e9. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Karnoe A, Furstrand D, Christensen KB, Norgaard O, Kayser L. Assessing competencies needed to engage with digital health services: development of the ehealth literacy assessment toolkit. J Med Internet Res. May 10, 2018;20(5):e178. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Nielsen AS, Hanna L, Larsen BF, Appel CW, Osborne RH, Kayser L. Readiness, acceptance and use of digital patient reported outcome in an outpatient clinic. Health Informatics J. Jun 03, 2022;28(2):14604582221106000. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Murray E, Hekler EB, Andersson G, Collins LM, Doherty A, Hollis C, et al. Evaluating digital health interventions: key questions and approaches. Am J Prev Med. Nov 2016;51(5):843-851. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Chesser A, Burke A, Reyes J, Rohrberg T. Navigating the digital divide: a systematic review of eHealth literacy in underserved populations in the United States. Inform Health Soc Care. Feb 24, 2016;41(1):1-19. [ CrossRef ] [ Medline ]
• Chesser AK, Keene Woods N, Smothers K, Rogers N. Health literacy and older adults: a systematic review. Gerontol Geriatr Med. Mar 15, 2016;2:2333721416630492. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mitra S, Singh A, Rajendran Deepam S, Asthana MK. Information and communication technology adoption among the older people: a qualitative approach. Health Soc Care Community. Nov 21, 2022;30(6):e6428-e6437. [ CrossRef ] [ Medline ]
• Ahmed S. How not to do things with words. Wagadu. 2016. URL: https://sites.cortland.edu/wagadu/wp-content/uploads/sites/3/2017/02/v16-how-not-to-do-ahmed.pdf [accessed 2024-04-02]
• Monkman H, Kushniruk AW. eHealth literacy issues, constructs, models, and methods for health information technology design and evaluation. Knowl Manag E Learn. 2015;7(4). [ CrossRef ]
• Brørs G, Norman CD, Norekvål TM. Accelerated importance of eHealth literacy in the COVID-19 outbreak and beyond. Eur J Cardiovasc Nurs. Aug 15, 2020;19(6):458-461. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by A Mavragani; submitted 14.03.23; peer-reviewed by G Myreteg, J Eriksen, M Siermann; comments to author 18.09.23; revised version received 09.10.23; accepted 27.02.24; published 11.04.24.

©Christian Gybel Jensen, Frederik Gybel Jensen, Mia Ingerslev Loft. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

Title: Understanding social enterprise performance - a business model approach

Authors : Giorgi Jamburia; Jean-Marie Courrent

Addresses : University of Montpellier, Rue Vendémiaire, CS 19519, 34960 Montpellier Cedex 2, France ' University of Montpellier, Montpellier Management Institute, Rue Vendémiaire, CS 19519, 34960 Montpellier Cedex 2, France

Abstract : This paper aims to provide a better understanding of the performance of social enterprises within a sustainability approach. The conceptual model proposes that the major aspect of sustainability lies in the implementation of business models that create a balance between social and economic value creation. The hypotheses were tested on a sample of 115 Swedish work integration social enterprises (WISEs), and an analysis of the results suggests that social imprinting and partner network strength are positively related to social performance, whereas social bricolage has a positive effect on both social and economic performance. The social and economic performances of social enterprises are also positively associated with each other, suggesting the possibility that WISEs can be simultaneously successful in both dimensions.

Keywords : social entrepreneurship; social enterprises; social enterprise performance; social performance; economic performance; business models; partner network; social imprinting; social bricolage; work integration social enterprises.

DOI : 10.1504/IJESB.2024.137754

International Journal of Entrepreneurship and Small Business, 2024 Vol.52 No.1, pp.1 - 27

Received: 16 Dec 2020 Accepted: 15 Jul 2021 Published online: 05 Apr 2024 *

Keep up-to-date

• Our Newsletter ( subscribe for free )
• New issue alerts
• Inderscience is a member of publishing organisations including:

ORIGINAL RESEARCH article

Analysis of professional competency awareness based on visible network graphs a provisionally accepted.

• 1 Yunnan University of Finance And Economics, China
• 2 Kunming No.1 High School, China

The final, formatted version of the article will be published soon.

With the development of society and technological advancement, the focus on professional competency has gradually increased. This paper conducts an in-depth analysis of the Baidu search index for "professional competency" from July 3, 2017, to October 6, 2023, using visible network graphs, covering 34 provincial-level administrative regions in China. Firstly, a literature review is conducted to provide a theoretical foundation for subsequent analysis. Then, the paper elaborates on the basic concepts of complex networks, parameters, and the principles of visible network graph algorithms. On this basis, the application of system clustering and community detection algorithms is explored. The paper also conducts a detailed empirical analysis of the collected data, starting with an analysis of data for various provinces in China, including the construction of visible network graphs, the study of scale-free networks, and discussions on regional system clustering. Subsequently, a nationwide analysis of data is performed, with a focus on the construction of visible network graphs for the visibility of professional competency awareness and the study of community structures. Through this study, a deeper understanding of the awareness of professional competency in different regions can be obtained, providing valuable references for policy formulation and corporate decision-making.

Keywords: Visible Network Graphs, Professional competency, Awareness, Baidu search index, complex networks

Received: 20 Nov 2023; Accepted: 11 Apr 2024.

Copyright: © 2024 Meng, Mou and Han. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Lidan Han, Kunming No.1 High School, Kunming, Yunnan Province, China

People also looked at