research analysis news

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts

Latest science news, discoveries and analysis

Lethal dust storms blanket Asia every spring — now AI could help predict them

AI now beats humans at basic tasks — new benchmarks are needed, says major report

Scientists discover first algae that can fix nitrogen — thanks to a tiny cell structure

Peter Higgs obituary: physicist who predicted boson that explains why particles have mass

This fmri technique promised to transform brain research — why can no one replicate it, how to supercharge cancer-fighting cells: give them stem cell skills, is chatgpt corrupting peer review telltale words hint at ai use, the rise of eco-anxiety: scientists wake up to the mental-health toll of climate change, ai-fuelled election campaigns are here — where are the rules rumman chowdhury.

Are women in research being led up the garden path?

A milestone map of mouse-brain connectivity reveals challenging new terrain for scientists

After the genocide: what scientists are learning from Rwanda

Audio long read: Why are so many young people getting cancer? What the data say

Could jwst solve cosmology’s big mystery physicists debate universe-expansion data, revealed: the ten research papers that policy documents cite most, the ‘ghost roads’ driving tropical deforestation, randomness in computation wins computer-science ‘nobel’.

Citizenship privilege harms science

What happens when climate change and the mental-health crisis collide?

Frans de waal (1948–2024), primatologist who questioned the uniqueness of human minds, ai can help to tailor drugs for africa — but africans should lead the way, current issue.

Rwanda 30 years on: understanding the horror of genocide

Nanoscale scythe cuts molecular tethers using mechanical forces, a massive galaxy that formed its stars at z ≈ 11, an optical tweezer array of ultracold polyatomic molecules, research analysis.

Blocking cell death limits lung damage and inflammation from influenza

Scalable, high-quality 2D telluride nanosheets for energy and catalysis applications

Wildlife boost in African forests certified for sustainable logging

Light makes atoms behave like electromagnetic coils, a bitter taste receptor activated in a surprising way, the biologist who built a faraday cage for a crab, a quirky fluid that has robotic capabilities.

Acid test: why the chemistry of this unique crater lake matters

How a young physicist’s job move helped Argentina join the ATLAS collaboration

‘Shrugging off failure is hard’: the $400-million grant setback that shaped the Smithsonian lead scientist’s career

How i harnessed media engagement to supercharge my research career, ready or not, ai is coming to science education — and students have opinions, books & culture.

Survival of the nicest: have we got evolution the wrong way round?

The comings and goings of ants: how are social skills shaped in an ever-changing world?

Three possible muses, wild women and restoring public trust: books in brief, nature podcast.

Latest videos

Nature briefing.

An essential round-up of science news, opinion and analysis, delivered to your inbox every weekday.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

We've detected unusual activity from your computer network

To continue, please click the box below to let us know you're not a robot.

Why did this happen?

Please make sure your browser supports JavaScript and cookies and that you are not blocking them from loading. For more information you can review our Terms of Service and Cookie Policy .

For inquiries related to this message please contact our support team and provide the reference ID below.

• About the Hub
• Announcements
• Faculty Experts Guide
• Subscribe to the newsletter

Explore by Topic

• Arts+Culture
• Politics+Society
• Science+Technology
• Student Life
• University News
• Voices+Opinion
• About Hub at Work
• Gazette Archive
• Benefits+Perks
• Health+Well-Being
• Current Issue
• About the Magazine
• Past Issues
• Support Johns Hopkins Magazine
• Subscribe to the Magazine

You are using an outdated browser. Please upgrade your browser to improve your experience.

Credit: PALESTINIAN NEWS & INFORMATION AGENCY (WAFA) IN CONTRACT WITH APAIMAGES, CC BY-SA 3.0

International team including Johns Hopkins experts makes excess deaths projections in Gaza

Johns hopkins civil and systems engineering professor tak igusa explains how the research team modeled projections of future casualties from trauma, infectious diseases, and other factors in gaza.

By Lisa Ercolano

If fighting continues at a similar pace through August, the conflict in Gaza could cause between 58,260 and 66,720 excess deaths from military action, disease, malnutrition, poor sanitation, a lack of medical care, and more, according to a report issued in February by an international team of experts including researchers from Johns Hopkins University. Excess deaths refer to the number exceeding what would be expected based on Gaza data from the recent past (baseline deaths). If the conflict escalates, the projected toll could reach between 74,290 and 85,750 excess deaths.

The recent report, "Crisis in Gaza: Scenario-Based Health Impact Projections," funded by the UK Humanitarian Innovation Hub and led by Francesco Checchi from the London School of Hygiene and Tropical Medicine and Paul Spiegel , director of the Johns Hopkins Center for Humanitarian Health , provides projections with the goal of informing critical decision-making and policy related to humanitarian efforts in the region.

Image caption: Tak Igusa

Image credit : Will Kirk / Johns Hopkins University

The Hub recently spoke with research team member Tak Igusa , a systems expert and a professor in the Department of Civil and Systems Engineering at JHU's Whiting School of Engineering and a member of the Center for Systems Science and Engineering. Igusa is leading the modeling for trauma-related fatalities and supervises projections for communicable diseases.

What did the team learn from its first report? Did your analysis reveal any unexpected insights?

In my previous modeling work on natural disasters, I examined the impacts of the reduction of essential services and supplies. These models include the emergency response that would typically be brought in to help communities recover. Our Gaza report analyzed what would happen if such assistance was unavailable due to the conflict. We projected multiple health impacts, including non-communicable and infectious diseases, malnutrition, and maternal and newborn mortality. One of our findings is that there is a steadily increasing possibility of cholera, famine, and other humanitarian disasters that may result in higher mortality than the trauma deaths from the conflict. 


How do you model excess mortality rates in Gaza? What data sources do you rely on and how do you address potential data gaps or biases?

Our academic team encompassed a wide range of disciplines including epidemiology, disaster response, nutrition, and maternal and neonatal health. We also used panels of experts to provide estimates for various risk factors, such as the rate of mortality for various types of trauma, and the reproductive number for cholera and other infectious diseases. We used experts who were familiar with or had worked in conflict zones.

I am glad you asked about bias. We discussed the importance of producing an unbiased report in nearly every team meeting. There are some specific steps we took to remove possible sources of bias. First, we carefully looked for possible sources of bias in our data, which came from the academic literature or publicly available sources from the current or past conflicts in the Gaza Strip or other similar settings in the Middle East. Second, we made our data and methods, including the computation code, easily accessible on GitHub to demonstrate transparency and to allow others to reproduce and check our results.


How does your model account for compounding risk factors? For example, how might damage to infrastructure exacerbate the risk of death from food insecurity and lack of access to health care? 


As a systems modeler, I have been working with the team on the interactions between risk factors. We needed more time to complete this complex analysis for our February report, and are currently working on the broad health impacts of sustained malnutrition.


What additional data points could be incorporated into future studies to provide an even more comprehensive understanding of the situation and shed light on where resources are needed most? 


You are correct in noting that complete data is essential for assessing the humanitarian situation. The example I am thinking about now is food. We modelers can compute the total nutritional needs of the Gaza population, and we can also calculate the number of calories entering the Gaza Strip through emergency relief efforts. We also know that it is not possible to distribute these calories evenly and that there is a severe deficit in various regions of Gaza. The additional data we need in this example is related to the flow—or lack thereof—of food to the regions of the Gaza Strip with the most severe cases of malnutrition.


What is the goal or intended impact of making these reports publicly available? While the data itself may be neutral, there was initially some concern that releasing it could elicit strong feelings from various parties. Has that been your experience? 


The U.K. Humanitarian Innovation Hub funded our project to assess the wide health impacts of the conflict in Gaza. Our February report assessed this impact by comparing mortalities due to continuing or escalating conflicts with mortalities in a ceasefire scenario. We presented our numerical findings, pointed out the need for a multi-faceted health response, and highlighted the lifesaving impact an immediate ceasefire would have.



We got quite positive and, in many cases, respectful responses. This may be partly because our report is a comprehensive examination of the impacts of the conflict in Gaza conducted by a team of academics.


Are your research findings currently being used to inform decision-making about resource allocation or other humanitarian efforts?

The principal investigators, professors Paul Spiegel at Johns Hopkins Bloomberg School of Public Health and Francesco Checchi at the London School of Hygiene and Tropical Medicine, have disseminated the report through their networks with policymakers and humanitarian organizations internationally, including in the U.S., U.K., Israel, and Palestine. Key findings in our report have been published in all major news outlets and cited in the U.K. Parliament and the U.S. Senate.



A debriefing session that was most memorable to me was with USAID's Bureau for Humanitarian Assistance. Members of their response team had been studying our report and discussed in detail the parts of our projections that were directly related to their emergency relief plans.


Can your modeling approach and lessons learned be applied to other conflict zones or scenarios—such as Ukraine—or in places experiencing climate-change-induced movement of people?

One motivation for working on this project is that the new modeling approaches we develop can be applied to other contexts in the future. I have done some work on climate-induced migration, and the Gaza project has given me new insights and approaches that I can use to advance this migration research further.


Are there any other important points or considerations you wish to highlight regarding the team, its results, and your role?

This work is an example of the interdisciplinary collaboration that defines the One University concept of Johns Hopkins. There really are no barriers between our schools—and this has enabled a Whiting School faculty member like me to engage in challenging and rewarding humanitarian research.

Posted in Voices+Opinion , Politics+Society

Tagged middle east , international studies , human rights , international health

You might also like

News network.

• Johns Hopkins Magazine
• Get Email Updates
• Submit an Announcement
• Submit an Event
• Privacy Statement
• Accessibility

Discover JHU

• About the University
• Schools & Divisions
• Academic Programs
• Plan a Visit
• my.JohnsHopkins.edu
• © 2024 Johns Hopkins University . All rights reserved.
• University Communications
• 3910 Keswick Rd., Suite N2600, Baltimore, MD
• X Facebook LinkedIn YouTube Instagram

News Analysis: Can both sides declare victory in Iran-Israel clash?

• Show more sharing options
• Copy Link URL Copied!

In a bid to avert wider war in the Middle East, the United States and other world leaders are urging Israel to see victory in its unprecedented military face-off with Iran over the weekend. It’s unclear whether Israel will follow that script, but Iran appears to be doing so, with its government claiming success.

After years of proxy battle, Iran launched an attack from its own territory into Israel for the first time, sending waves of land-attack cruise missiles, more than 100 ballistic missiles and a battery of “killer drones.”

Israel, in turn, mounted a formidable defense and emerged almost unscathed, intercepting or shooting down nearly all of the munitions with its air defense system and help from U.S. and British warplanes as well as Jordan. Saudi Arabia is believed to have let Israel’s allies use its airspace.

The Biden administration praised Israel’s “spectacular defeat” of the Iranian assault.

“This was an incredible success, really proving Israel’s military superiority and just as critically, their diplomatic superiority, that they have friends in the region, that they have around the world that are willing to help them,” said White House national security council spokesman John Kirby.

He noted the coalition of military support from several countries that rallied to help defend Israel despite widespread anger and international isolation over Israel’s conduct of the six-month-old war in Gaza.

World & Nation

Jordanians protest nightly against peace deal with Israel amid anger over Gaza war

Outraged over the war in Gaza, protesters gather every night near the Israeli Embassy in Jordan’s capital to demand an end to peace agreements with Israel.

April 11, 2024

The coalition is fragile. Countries such as Jordan, which acknowledged it shot down a number of what it called “flying objects” that trespassed in its airspace, want to remain in good favor with Israel and its main backer, the United States, but also cannot risk the wrath of Iran.

Moreover, U.S. officials do not want an Israeli response now to further increase bloodshed across the region.

Already, the war in Gaza has claimed thousands of Palestinian lives and cost the Biden administration political capital here and abroad.

In a telephone call with Israeli Prime Minister Benjamin Netanyahu late Saturday, President Biden again offered “iron-clad” support in Israel’s defense, but cautioned that U.S. forces will not be part of any offensive action Israel might take against Iran, according to administration officials familiar with the conversation.

“No one wants to run up the escalation ladder,” a senior administration official said, briefing reporters Sunday on condition of anonymity to discuss internal discussions. “Israel has to think carefully what it does next.”

Some Israeli public figures are also urging caution, amid cries for urgent revenge from right-wing members of Netanyahu’s government.

Israel could leverage an improved military and diplomatic standing to find “a more creative and sophisticated way to respond, perhaps at a later stage and in line with previous covert actions it took against Iran, which will better serve its national interest,” said Nimrod Goren, a senior fellow for Israeli affairs at the Middle East Institute in Washington.

In Israel, Netanyahu’s war cabinet on Sunday declared Israel would respond to Iran “in a way and time that suits us,” as member Benny Gantz said. And it will be a surprise, he said.

Iran’s attack was anything but a surprise .

Since the April 1 assassination of a commander of the Islamic Revolutionary Guard Corps and six other officers in an airstrike on a diplomatic compound in Damascus, Syria — an attack attributed to Israel — Iran has steadily promised revenge. In the days leading up to the overnight attack this weekend, Iran had informed some countries in the region that it was planning an attack, and the first drones were launched hours before they reached Israeli airspace, giving U.S. and Western spy satellites plenty of time to detect and track the incoming fusillade.

Several countries who have cordial relations with Iran, such as Turkey and Lebanon, urged “de-escalation” by both Israel and Iran.

Iran said Sunday its retaliation for the April 1 incident was completed. In the telling of officials there, the strike was an awesome display of military might that was also calibrated so as not to tip the region into all-out war. Iran supporters also pointed out that Israel needed the active help of no less than five nations to stop the onslaught and that it could not do it on its own.

They have taken pride in the fact that the strike crossed what has been something of a red line for the last 40 years of hostility between Israel and Tehran: direct fire from Iranian soil to Israeli soil.

Iran had long relied on factions in Iraq, Syria, Lebanon and Yemen to carry out attacks on Israel.

Critics, on the other hand, said that the fact almost all the ordnance was shot down — or never even made it out of Iranian airspace — proved the inferiority of Iran’s arsenal, and that in any case the whole matter was a charade that left Iran looking like the lion that squeaked.

Some analysts said Iran had to know its hundreds of missiles were going to get intercepted and was being careful not to exact too much damage to temper Israeli response. Iran also does not want a broader war, one that would suck in the United States.

But the administration official who briefed reporters Sunday cast doubt on such a calculation. Iran “clearly intended to inflict significant damage and death,” the official said. Israel reported some damage at an air force base and the wounding of a Bedouin child.

Throwing so much of its best materiel at Israel with so little physical impact could be an embarrassing setback for Iran.

On Monday, Iranian Foreign Ministry spokesman Naser Kananí described the attack as “necessary, proportional, and aimed at military targets” to establish “deterrence capability,” according to news agency reports from Tehran. Kanani added that Iran “does not seek to escalate tensions in the region.”

At the same time, Iran has warned that if any country assisted Israel in a counteroffensive, there would be consequences.

On Sunday, an unnamed military source was quoted in Iran state news criticizing Jordan for its “movements during the punitive attacks” and saying that backing Israel would make it “the next target.”

That same day, the Jordanian foreign ministry called in the Iranian ambassador and upbraided him for “getting involved in Jordan’s internal affairs” and for “casting doubt on Jordan’s positions” on Israel, Jordanian Foreign Minister Ayman Safadi said in an interview with Al-Mamlaka TV.

“Iran’s problem is with Israel and not with Jordan,” he said. “Neither Iran nor anyone else can outbid what Jordan is doing, what it is offering, and what it has provided historically for the sake of Palestine.”

Jordan billed the shoot-downs as as a military intervention meant to preserve national sovereignty and the safety of the country’s citizens. Safadi said that Jordan would have done the same if the missiles were fired by Israel toward Iran.

Complicating matters for Jordan’s government, the events of the weekend only added to the anger of many citizens who oppose diplomatic relations with Israel because of its conduct in the war in Gaza. Many Jordanians have Palestinian roots, and activists online have been mocking what they view as their government’s willingness to defend Israelis while letting Palestinians die.

Now the world waits to see whether Israel will be content with having repelled the attack by Iran — or if it will respond militarily.

Jonathan Spyer, research directer at the U.S.-based Middle East Forum, suggested that Israel might be propelled to act because it cannot allow Iran, or the region, to regard retaliatory attacks on Israeli territory as a new normal.

Israel won’t want to “take the win,” as it’s being urged to do by the U.S. and others, he said. “From the Israeli perspective, a ‘win’ is not blocking the blow, but what you do after the blow.”

Wilkinson reported from Washington and Bulos from Beirut.

More to Read

Biden hosts Iraqi leader after Iran’s attack on Israel throws Mideast into greater uncertainty

April 15, 2024

Israel hails ‘success’ in blocking Iran’s unprecedented attack; Biden seeks diplomatic response

April 14, 2024

Iran launches airstrikes toward Israel in retaliatory move, boosting chance of wider conflict

April 13, 2024

Start your day right

Sign up for Essential California for news, features and recommendations from the L.A. Times and beyond in your inbox six days a week.

You may occasionally receive promotional content from the Los Angeles Times.

Tracy Wilkinson covers foreign affairs from the Los Angeles Times’ Washington, D.C., bureau.

Nabih Bulos is the Middle East bureau chief for the Los Angeles Times. Since 2012, he has covered the aftermath of the “Arab Spring” revolution as well as the Islamic State’s resurgence and the campaign to defeat it. His work has taken him to Syria, Iraq, Libya, Turkey, Lebanon, Jordan and Yemen as well as on the migrant trail through the Balkans and northern Europe. A Fulbright scholar, Bulos is also a concert violinist who has performed with Daniel Barenboim, Valeri Gergyev and Bono.

More From the Los Angeles Times

Israeli military renews warnings to Palestinians not to return to war-torn northern Gaza

House Speaker Mike Johnson says he will push for aid to Israel and Ukraine this week

A Pittsburgh congressional race could test Democrats who have criticized Israel’s handling of war

Biden returning to Washington to meet with national security team amid Mideast tensions

Analysis of news sentiments using natural language processing and deep learning

• Open access
• Published: 30 November 2020
• Volume 36 , pages 931–937, ( 2021 )

Cite this article

You have full access to this open access article

• Mattia Vicari 1 &
• Mauro Gaspari 2  

12k Accesses

14 Citations

1 Altmetric

Explore all metrics

This paper investigates if and to what point it is possible to trade on news sentiment and if deep learning (DL), given the current hype on the topic, would be a good tool to do so. DL is built explicitly for dealing with significant amounts of data and performing complex tasks where automatic learning is a necessity. Thanks to its promise to detect complex patterns in a dataset, it may be appealing to those investors that are looking to improve their trading process. Moreover, DL and specifically LSTM seem a good pick from a linguistic perspective too, given its ability to “remember” previous words in a sentence. After having explained how DL models are built, we will use this tool for forecasting the market sentiment using news headlines. The prediction is based on the Dow Jones industrial average by analyzing 25 daily news headlines available between 2008 and 2016, which will then be extended up to 2020. The result will be the indicator used for developing an algorithmic trading strategy. The analysis will be performed on two specific cases that will be pursued over five time-steps and the testing will be developed in real-world scenarios.

Similar content being viewed by others

A survey on sentiment analysis methods, applications, and challenges

Mayur Wankhade, Annavarapu Chandra Sekhara Rao & Chaitanya Kulkarni

A review on sentiment analysis and emotion detection from text

Pansy Nandwani & Rupali Verma

Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis

Salman Bahoo, Marco Cucculelli, … Jasmine Mondolo

Avoid common mistakes on your manuscript.

1 Introduction

Stock forecasting through NLP is at the crossroad between linguistics, machine learning, and behavioral finance (Xing et al. 2018 ). One of the main NLP techniques applied on financial forecasting is sentiment analysis (Cambria 2016 ) which concerns the interpretation and classification of emotions within different sources of text data. It is a research area revived in the last decade due to the rise of social media and cheap computing power availability (Brown 2016 ). Like products and services, market sentiments influence information flow and trading, thus trading firms hope to profit based on forecasts of price trends influenced by sentiments in financial news (Ruiz-Martínez et al. 2012 ). Is it possible to find predictive power in the stock market’s behavior based on them? It seems to be the case in the work “On the importance of text analysis for stock market prediction” by Lee and MacCartney ( 2014 ) that shows, based on text, an improved predictability in the performance of a security. Intuitively, the cause of the stocks’ fluctuation can be the aggregated behavior of the stockholders, who will act based on news (Xing et al. 2018 ). Although the predicting models reported in the literature have not been able to profit in the long run, many theories and meaningful remarks have been made from the financial markets’ data (Xing et al. 2018 ). One of the biggest differences between market sentiment problems and linguistics ones is that the ladder has some guarantee of having some type of structures (Perry 2016 ). There are many models that have been proposed and used in the recent years, each with their positive and negative aspects. Specifically, overly complicated models generally have poor performance, while simpler linear models rely on strong hypotheses, for example, a Gaussian distribution, which does not always apply in real-world cases (Xing et al. 2018 ). Deep learning seems to be the most fit for this purpose since it has the ability to analyze a great amount of data that NLP needs to understand context and grammatical structures. In this paper, we investigate this scenario, exploring DL for forecasting the market sentiment using news headlines. We assume a basic linguistic framework for preprocessing (stopword, lowercasing, removing or numbers and other special characters), and we reject the common assumption that “positive financial sentiment” = “positive words” and vice versa. The reason is that we do not know if that is the case, and we want the model to learn freely by itself. Taking this assumption into consideration, we are going to test two scenarios, both based on the news published today: case A tries to forecast the movement of the Dow Jones Industrial Average (DJIA) in the next four individual days; case B focuses on time intervals from today to the next 4 days. We discuss the obtained results, and we conclude the paper with a road map for the future.

2 Background

2.1 deep learning.

Supervised ML models create mechanisms that can look at examples and produce generalizations (Goldberg 2017 ). Deep learning (DL) Footnote 1 is a function that imitates the mechanisms of the human brain for finding patterns. Since our case is a binary classification problem (does the DJIA go up or down?), to test our results we used both a binary cross-entropy loss function:

And an accuracy metric: Footnote 2

They are both useful in different ways since the first is used in the training phase, while accuracy is intuitive as long as the classes considered are balanced, like in our case. The basic structure of a neural network (NN) is the neuron: it receives the signal, decides whether to pass the information or not, and sends it to the next neuron. Mathematically, the neuron structure takes some input values × and their relative weights w which are both initialized randomly, and thanks to an “activation function”, Footnote 3 which was the real game-changer (Thanaki 2018 ), the neurons have the ability to spot non-linear behaviors, that is why they are often addressed as being “Universal Function Approximators”. Since searching over the set of all the possible functions is a difficult task, we need to restrict our scope of action using smaller sets, but by doing so, we added an “inductive bias” (b) that must be taken into account. The most commonly used function for this purpose has the form (Goldberg 2017 ):

The parameters Footnote 4 have the purpose to minimize the loss function over the training set and the validation set (Goldberg 2017 ). How can NN learn? Through an optimization method that, thanks to a first-order iterative algorithm called gradient descent, can minimize the error function of our model until a local minimum is reached. Footnote 5 Backpropagation is the central mechanism of the learning process which calculates the gradient of the loss function and, by making extensive use of the chain rule, distributes it back through the layers of the NN and adjusts the weights of the neurons for the next iteration. The learning rate used during backpropagation starts with a value of 0.001 and is based on the adaptive momentum estimation (Adam), a popular learning-rate optimization algorithm. Traditionally, the Softmax function is used for giving probability form to the output vector (Thanaki 2018 ) and that is what we used. We can think of the different neurons as “Lego Bricks” that we can use to create complex architectures (Goldberg 2017 ). In a feed-forward NN, the workflow is simple since the information only goes…forward (Goldberg 2017 ). However, when humans read a book, for example, they comprehend every section, sentence or word taking into account what they saw previously (Olah 2015 ); therefore, a feed-forward NN is not fit for our purposes because it cannot “remember”. Recurrent neural networks (RNN), on the other hand, can catch the sequential nature of the input and can be thought of as multiple copies of the same network, each passing a message to a successor (Olah 2015 ). A well-known drawback of standard RNN is the vanishing gradients’ problem that can be dramatically reduced using, as we did, a gating-based RNN architecture called long short-term memory Footnote 6 (LSTM).

2.2 NLP and vectorization

Natural language processing (NLP) is a field of artificial intelligence (AI) focused on finding interactions between computers and the human language. Linguistics can be a slippery field for humans, and its intrinsic ambiguity is what makes NLP even more problematic for machines (Millstein 2017 ) since complexity can appear on many levels: morphological, lexical, syntactic, or semantic. Data preprocessing is a crucial method used to simplify raw text data. In NLP, words are the features Footnote 7 used to find sentiment, based on their frequency in a database (Velay and Daniel 2018 ). The goal of the language model that is to assign probabilistic sentiment to sentences by trying to capture its context, but to do so, the so-called Markov assumption Footnote 8 is necessary. We use encoding for creating word-embeddings, which are tools used to vectorize words into feature-vectors that a NN can use (Millstein 2017 ). Word-embeddings are representations of documents where vectors with small distances represent words with closely related meanings. These structures allow us to perform math on texts. With the recent advances in DL, word-embeddings are formed with more accuracy, and they make it easier to compute semantic similarities (Xing et al. 2018 ). Unfortunately, distributional methods can fall victim of different corpus biases, which can range from cultural to thematic: a common saying is that “Words are similar if used in similar contexts,” but linguistics is more complicated than it looks. Footnote 9 Each model has its pros and cons (Velay and Daniel 2018 ), the difference stays in the user’s ability to have control over its dataset (Goldberg 2017 ).

3 Learning trading indicators on news

A trading indicator is a call for action to buy/sell an asset given a specific condition. When it comes to short-term market behaviors, we are trying to profit on the investors’ “gut-feeling,” but since this phenomenon is something that cannot be unequivocally defined, we must reduce human judgment as much as possible by letting the algorithm learn directly. As shown in Fig.  1 , we will start by seeing the chosen dataset. Right after, we will analyze which preprocessing operations have been implemented to ease the computational effort for the model. Then we will see all the components of the DL model put in place and ultimately we will present the results with a real-case scenario.

Model structure

When working with neural networks, we encounter some limitations that might affect our results (Thanaki 2018 ): the dimension of the data set, our computing-power availability and the complexity of our model. Just like in the work of Vargas et al. ( 2017 ), the dataset is based on news headlines; specifically from the DJIA Database which comes from Kaggle, which contains 25 daily news with economic content from 2008 to 2016 scraped by the author from the most upvoted by the community on Reddit WorldNews: https://www.kaggle.com/aaron7sun/stocknews/home .

Given the fact that the database stretches over almost a decade and contains, for each day considered, a conspicuous amount of news with inherent economic content, we decided that it represents a plausible research instrument. The database was labeled based on whether the DJIA increased or decreased over each time step considered.

4 The studied model

The focus is on aggregate market indicators and two cases are considered, namely cases A and B as shown, respectively, in Figs.  2 and 3 .

The T0 event, common in both instances, analyzes if, based on the news published today, today’s Adjusted closing price is higher than today’s opening price. While, based on the news published today, case A tries to forecast the movement of the DJIA in individual days, case B focuses on time intervals. After defining these market indicators, the preprocessing phase is crucial to reduce the number of independent variables, namely the word tokens, that the algorithms need to learn. At this stage, the news strings need to be merged to represent the general market indicator, from which stopwords, numbers and special elements (e.g. hashtags, etc.) were removed. In addition, every word has been lowercased and only the 3000 most frequent words have been taken into consideration and vectorized into a sequence of numbers thanks to a tokenizer. Furthermore, the labels are transformed into a categorical matrix with as many columns as there are classes, for our case two. The NN Footnote 10 presented in Fig.  1 starts with an embedding layer, which is the input of the model, whose job is to receive the two-dimensional matrix and output a three-dimensional one, which is randomly initialized with a uniform distribution. Then this 3D-matrix is sent to the hidden layer made of LSTM neurons whose weights are randomly initialized following a Glorot Uniform Initialization, which uses an ELU activation function and dropout. Finally, the output layer is composed of two dense neurons and followed by a softmax activation function. Once the model’s structure has been determined, it needs to be appropriately compiled using the ADAM optimizer for backpropagation, which provides a flexible learning rate to the model.

As shown in Fig.  4 , the database is then divided into training and validation set with an 80/20 split and evaluated by the binary cross-entropy and accuracy metrics that we previously discussed. Moreover, the training set is split into small pieces called batches (which, for instance, have a dimension of 64 for the T0 case) that are given to the computer one by one for 25 iterations in the training set and two epochs to ease the computational effort when updating the weights.

Structure of training, validation and testing sets with DJIA labels

Table 1 shows the level of accuracy obtained in this experiment relative to the validation sets:

Consistent with previous studies (Velay and Daniel 2018 ), we immediately notice that the accuracy is particularly low since, in both cases, our peaks stick around 58%, which is slightly higher than the flip of a coin.

Besides, in both versions of the model, the highest accuracy appears in the T0 case, behavior that suggests that forecasting attempts within shorter time periods should be preferred, confirming existing literature on the topic (Souma et al. 2019 ; Sohangir et al. 2018 ). Nevertheless, we can notice the tendency that the accuracy gradually decreases in case A, while case B shows a less evident decrease that ends with a final increase in T4. Given the low accuracy level, we asked ourselves: How would this model behave in a practical application? Thus, we chose specific news related to major political events that, from our perspective, might have affected the global markets. For this reason, the first testing case looks at major political events that might have caused relevant shifts in the balance of the world:

Start of Trump’s formal impeachment inquiry.

Large crowds of protesters gathered in Hong Kong.

Boris Johnson becomes prime minister.

Protests for George Floyd’s murder explode.

COVID-19 was declared a global pandemic by the WHO.

In the second testing category, we decided to look closer at Trump’s presidency. The events chosen were:

The Summit in Singapore between Trump and Kim Jong-Un.

Trump signs tariffs on steel and aluminum.

Trump formally announced US withdrawal from the Paris Agreements.

Trump signs a big tax cut that was beneficial to big corporations.

Trump starts the government shutdown to build the wall.

To be consistent with the training and validation set, we manually retrieved from the “News” section of Google 25 news focused on the date of every major event listed above. By comparing each prediction with how the DJIA behaved, we notice that the results are not significant because the results obtained did not show any consistent pattern. Therefore, we agree with Arora et al. ( 2019 ) when stating that financial information is extremely unpredictable and that the task of predicting stock movements remains open. For this reason, we thought it could be interesting to keep studying how such a model would behave over longer periods of time by feeding it with more data in the training phase. In fact, for image recognition experiments, DL learning is known for having better performance the more data it sees; therefore, we will follow the same path for financial forecasting. Thus, we decided to extend the original data set until August 2020 and test the same scenario using the same methodology. We retrieved 25 most upvoted daily news from the sub-Reddit/r/worldnews/, just like the author originally did, thanks to the data submissions available in the Pushshift data collections: https://www.files.pushshift.io/reddit/submissions/ .

To keep our results comparable, we kept the same NN structure as in the previous case. The results of the experiment using this extended data set in reported in Table 2 .

On the one hand, for the extended case A, the outcome is mixed and there is no added benefit to our initial model. On the extended case B, on the other hand, we notice an even worse forecasting performance. In addition, as in the previous test for individual news, the results obtained did not show any relevant pattern and are not significant. Unlike in the image-recognition field, which is known for improving its performance whenever a more extensive data-set is fed to the deep learning model, for our financial forecasting case we did not obtain the same performance improvement. Why despite increasing the dataset did we get worse results? We analyzed the datasets for the T0 case and the extended T0 case deeper.

The original dataset T0 gives us 2267 trading dates for the DJIA and the DJIA closed higher than the open on 1212 times and closed lower on 1055 days. This means that an algorithm which would guess “DJIA increase” every single day which would result in

True positives: 1212

True negatives: 0

False positives: 1055

False negatives: 0

At the same time, the extended T0 case shows us that the DJIA went up 1625 days and down 1367 times. Applying again an algorithm which would guess “DJIA increase” every single day would have these characteristics:

True positives: 1625

False positives: 1367

Therefore, we would have an accuracy of around 53% for the original T0 case and of around 54% for the extended T0 case, values that are both extremely close to the results we obtained through an optimized DL model. Thus, we presume that the algorithm did not learn anything other than the bias in the data. Further paths worth exploring would be testing DL models with different databases, that could be:

Using news more focused on a specific security or newspaper.

Testing cases right after the news comes out.

Exploiting a dataset with more news per day.

Exploring a wider variety of NLP methods in the preprocessing phase.

By removing the assumptions “positive financial sentiment” = “positive words”, forecasting sentiment becomes particularly difficult and an elaborated model such as DL the indicator appears weak (Sohangir et al. 2018 ). In fact, the closer we get into situations where human discretion is involved, the more unstable a model becomes (Goldfarb et al. 2018 ).

6 Discussion and future roadmap

Forecasting consists in grabbing data that we have to generate new information: this is a field that economics has been studying for years, and perhaps by combining the old logic of decision theory and DL methods, we may have a better understanding from a different perspective (Goldfarb et al. 2018 ). The benefit of DL is that it does not require lots of human effort to effectively provide outputs that were previously considered impossible and a human exclusive. Indeed, AI did not give us more intelligence, but more prediction capacity.

In fact, the innovation of this technology is not the math behind it that existed for decades, but the combination of this knowledge with the Big Data environments that we can access nowadays (Goldfarb et al. 2018 ). Economics says that by eliminating every obstacle to trading leads to market efficiency. Is it always true? Would the introduction of market agents using trading strategies based on DL and sentiment analysis increase market efficiency? The answer is “it depends”. By introducing a new investing technology we could indeed increase market liquidity and therefore stock markets’ efficiency as a whole. However, the stock market is a complex ecology of interacting players, all with their own strategies and things can go wrong very fast. Let us take a step back and analyze the situation with a few examples from the past that might help us better understand what to expect tomorrow (Buchanan 2012 ):

First, the belief that financial innovations such as derivatives could help us reach more market stability and efficiency dominated the last decades (Buchanan 2012 ). However, those instruments, passed a threshold, profoundly endangered the whole system in 2008 (Buchanan 2012 ).

Second, the innovation of High-Frequency Trading (HFT) has improved the stock market as well by reducing trading costs, enhance liquidity, making markets faster and more reactive in calm times, but they do the exact opposite in troubled ones; exactly when the market would need it the most (Buchanan 2012 ).

Third, hedge funds typically borrow money to attract investors and increase their profits. However, right before the “Quant meltdown” of August 2007 it was clear the strategies used became too similar, causing their margins to decrease. Therefore, to keep being appealing to investors, managers were slowly forced to increase leverage until it was unsustainable and everything collapsed (Buchanan 2012 ).

The belief that the world is becoming more efficient and stable than ever thanks to financial innovation (Buchanan 2012 ) can be misleading. Then why do we always seem surprised when such crises appear? (Buchanan 2012 ) It is not a coincidence that excessive efficiency can compromise stability (Buchanan 2012 ). Efficiency means doing more with less, while stability implies the opposite: some extra room to absorb a hit. Given the current hype for AI, these interconnected markets will have access to new ways to invest at incredible speed. In such a context, trading strategies based on sentiment may be particularly prone to the “herd behavior” assuming that these algorithms must train on datasets containing lots of news that may turn out to be extremely similar (Buchanan 2012 ). In this context, the hypothesis of a “splash crash” ranging across many asset classes does not seem impossible (Buchanan 2012 ). The global financial crisis has revealed the need to drastically rethink how we regulate the financial system to get ready for when the next recession will strike (Buchanan 2012 ). Innovation cannot be stopped, but acknowledging its limitations can help us find the best ways to reduce its weaknesses. The fight is rapidly shifting from humans towards machines, but they are playing with real businesses and people, and we must counteract effectively and rapidly in that regard (Buchanan 2012 ).

7 Conclusion

In conclusion, by removing the assumptions “positive financial sentiment” =  “positive words”, financial forecasting becomes particularly difficult and even a DL model does not show better results than the flip of a coin for any of the cases or time-intervals studied. We acknowledge that there are many other paths that could potentially improve our results, but increasing the database size over a longer period of time, common practice in image-recognition problems, does not look like one of them. Despite that, we believe that deep learning methods could potentially be the cause of a relevant danger, due to its proneness towards the “herd behavior”, for the financial system as a whole and thus must be handled carefully.

Also known as a deep neural network or deep neural learning.

Where TP means’True Positives’, TN means’True Negatives’, FP means’False Positive’ and FN means’False negatives’.

The Activation Function chosen is the Exponential Linear Unit (ELU) since it shows better performance than its Rectified Linear Unit (ReLU) predecessor Hochreiter et al. ( 2016 ) by reducing the vanishing gradient problem and by accepting negative values.

Where x is the input, W are the weights, b is the inductive bias. Right after are explained the dimensions of the vectors that have been enumerated. These are all cases that we will go over later on but, for example: x in our case is a vector whose dimension represents a matrix whose dimensions are represented by the product between the maximum length of the sentences and the random initialization created using the embedding layer for each batch. The notation named ‘ d out ’ represents the dimension of the following layer that will receive the output of the current one.

For the sake of completeness, it does not tell us the minimum error immediately but shows us the proper direction we need to follow Thanaki ( 2018 ).

To avoid overfitting of the LSTM structure, we adopted a common regularization method called "Dropout".

Features in a model based on AI are individual measurable properties used as input to obtain an output Thanaki ( 2018 ); therefore, any attribute can be a feature as long as it is measurable and useful.

Which states that the probability of the next word is calculated by looking at the previous one.

The clearest example is polysemy.

The number of neurons, dropout and epochs named below represent the common T0 case, but have been fine-tuned for each other case using the "babysitting method".

Arora A, Datta A, Ding V (2019) Using news titles and financial features to predict intraday movements of the DJIA. https://www.web.stanford.edu/class/archive/cs/cs224n/cs224n.1194/reports/custom/15744462.pdf . Accessed 30 Sept 2020

Brown ED (2016) Analysis of Twitter messages for sentiment and insight for useinstockmarketdecisionmaking. Amazon Digital Services LLC, Seattle

Google Scholar  

Buchanan M (2012) Forecast: what physics, meteorology and natural sciences can tell us about economics. Bloomsbury, London

Cambria E (2016) Affective computing and sentiment analysis. IEEE Intell Syst 31(2):102–107

Article   Google Scholar  

Goldberg Y (2017) Neural network methods for natural language processing. Morgan Claypool publishers, San Rafael

Book   Google Scholar  

Goldfarb A, Agrawal A, Gans J (2018) Prediction machines: the simple economics of artificial intelligence. Harvard Business Review Press, Cambridge

Hochreiter S, Clevert DA et al (2016) Fast and accurate deep network learning by exponential linear units. https://arxiv.org/abs/1511.07289

Lee S, MacCartney J (2014) On the importance of text analysis for stock price prediction. In: Language resources and evaluation conference (LREC)

Millstein F (2017) Natural language processing with python: natural language processing using NLTK. Packt Publishing, Birmingham

Olah C (2015) Understanding LSTM networks. http://www.colah.github.io/posts/2015-08-Understanding-LSTMs/ . Accessed 30 Sept 2020

Perry T (2016) Deep learning the stock market. https://www.medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02 . Accessed 30 Sept 2020

Ruiz-Martínez JM, Valencia-García R, García-Sánchez F (2012) Semantic-based sentiment analysis in financial news. In: Joint Proceedings of the 1st International Workshop on Finance and Economics on the Semantic Web (FEOSW 2012), 5th International Workshop on REsource Discovery (RED 2012) and 7th International Workshop on Semantic Business Process Management (SBPM 2012) in conjunction with 9th Extended Semantic Web Conference (ESWC 2012). CEUR. CEUR Workshop Proceedings. 862:38–51.  http://ceur-ws.org/Vol-862/FEOSWp4.pdf . Accessed 30 Sept 2020

Sohangir S, Wang D et al (2018) Big data: deep learning for financial sentiment analysis. J Big Data. https://doi.org/10.1186/s40537-017-0111-6

Souma W, Vodenska I, Aoyama H (2019) Enhanced news sentiment analysis using deep learning methods. J Comput Soc Sci 2:33–46

Thanaki J (2018) Python natural language processing: advanced machine learning and deep learning techniques for natural language processing. CreateSpace Independent Publishing Platform, South Carolina

Vargas MR, de Lima BSLP, Evsukoff AG (2017) Deep learning for stock market prediction from financial news articles. In: 2017 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA), pp 60–65

Velay M, Daniel F (2018) Using NLP on news headlines to predict index trends. Lusis https://arxiv.org/abs/1806.09533

Xing FZ, Cambria E, Welsch RE (2018) Natural language based financial forecasting: a survey. Artif Intell Rev 50:49–73. https://doi.org/10.1007/s10462-017-9588-9

Download references

Acknowledgements

This paper has been inspired from my master thesis that can be found here: https://www.github.com/Mattia9494/Sentiment-analysis-with-AI .

Open access funding provided by Alma Mater Studiorum - Università di Bologna within the CRUI-CARE Agreement.

Author information

Authors and affiliations.

University of Bologna, Bologna, Italy

Mattia Vicari

Department of Computer Science and Engineering, University of Bologna, Bologna, Italy

Mauro Gaspari

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Mattia Vicari .

Additional information

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 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/ .

Reprints and permissions

About this article

Vicari, M., Gaspari, M. Analysis of news sentiments using natural language processing and deep learning. AI & Soc 36 , 931–937 (2021). https://doi.org/10.1007/s00146-020-01111-x

Download citation

Received : 29 October 2020

Accepted : 02 November 2020

Published : 30 November 2020

Issue Date : September 2021

DOI : https://doi.org/10.1007/s00146-020-01111-x

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
• Machine learning
• Natural language processing
• Trading signals
• Sentiment analysis
• Trading strategies
• Find a journal
• Publish with us
• Track your research

The Budget Lab at Yale Launches to Provide Novel Analysis for Federal Policy Proposals

The  Budget Lab at Yale , a nonpartisan policy research center, launched on April 12 to provide in-depth analysis for federal policy proposals impacting the American economy. For too long, according to the center’s founders, policy analysis has been narrowly focused on short-term cost estimates, or traditional budget scores, according to the center’s founders. The Budget Lab aims to fill a critical gap in policy evaluation, particularly focusing on the long-term effects of proposed policies on the economy, the income distribution, and recipients. The Budget Lab’s initial analysis , released today, examines both the Tax Cut and Jobs Act (TCJA) and the Child Tax Credit (CTC) through this broader lens.  

The Budget Lab is co-founded by leading economic advisors and academics whose goal is to bring fresh ideas and new methods to policy making. 

• Natasha Sarin, Co-founder and President, is a Professor of Law at Yale Law School with a secondary appointment at the Yale School of Management in the Finance Department. She served as Deputy Assistant Secretary for Economic Policy and later as a Counselor to the U.S. Treasury Secretary Janet Yellen. 
• Danny Yagan, Co-founder and Chief Economist, is an Associate Professor of Economics at UC Berkeley and a Research Associate of the National Bureau of Economic Research. He was the Chief Economist of the White House Office of Management and Budget.
• Martha Gimbel, Co-founder and Executive Director, is a former Senior Advisor at the White House Council of Economic Advisers, Senior Policy Advisor to the U.S Secretary of Labor, and Senior Economist and Research Director at Congress’s Joint Economic Committee. 

“For many of the greatest policy challenges of our time — investing in children, combating climate change — their most important impact is not on short-run GDP. We need to understand the effects on poverty, on emissions reduction, on the income distribution,” said Sarin. “We are excited to share the tools we have built to analyze the fiscal and social impacts of government policies so policymakers can make better choices.”

The Budget Lab’s work will look at issues not included in current budget policy assessment methods, particularly in evaluating the full scope of costs and returns related to policies including the child tax credit, tax cuts, paid family leave, deficit reduction, and universal pre-K. The Lab’s innovative approach bridges this gap by offering a combination of existing open-source models and our microsimulation tax model to provide fast, transparent, and innovative estimates that unlock deeper insights.

“Our approach implements a new lens to improve existing conventions for distributional impacts by showing how policies affect families over time,” added Yagan. 

One key aspect of the Budget Lab’s commitment to transparency is its open-access model code. The code used to produce analysis is publicly available, fostering trust and allowing policymakers to understand how the Budget Lab arrives at its results. It also allows for the infrastructure of the budget model the team is developing to be leveraged by others interested in similar analysis. 

“Our aim is to provide rapid responses to important policy questions with the ability to think not only about the costs of policies but also about benefits and the return on investments,” said Martha Gimbel.  “Our tax microsimulation model, budget estimates, and interactives will paint a broader and more realistic picture of how Americans will benefit from proposed government initiatives.”  

The Budget Lab is hosting a launch event at the National Press Club on April 12 where the leadership team will share new research on budget scoring for TCJA and CTC. The event will include remarks by Shalanda Young, Director of the Office of Management and Budget and a panel discussion with Joshua Bolten, former Director of the Office of Management and Budget and White House Chief of Staff for President George W. Bush; Doug Holtz-Eakin, former Director of Congressional Budget Office and economic policy advisor to Sen. John McCain; and will be moderated by Greg Ip of The Wall Street Journal .   

Budget Lab Team

In addition to the Budget Lab co-founders, the team includes leading economists who have extensive experience in the public sector. 

Ernie Tedeschi, Director of Economics, was most recently the chief economist at the White House Council of Economic Advisors. Rich Prisinzano, is the Director of Policy Analysis, previously served at the Penn Wharton Budget Model and for over a decade as an economist in the Office of Tax Analysis in the U.S. Department of Treasury. John Ricco, Associate Director of Policy Analysis, is an economic researcher with a decade of experience building microsimulation models to inform public policy debates and was formerly with the Penn Wharton Budget Model and also a research analyst at the International Monetary Fund. Harris Eppsteiner, Associate Director of Policy Analysis, was a Special Assistant to the Chairman and research economist at the White House Council on Economic Advisors. 

In the Press

Ct artists and scholars win prestigious guggenheim awards, who cares if supreme court justices get along — a commentary by linda greenhouse ’78 msl, silencing trump with a gag order is hopeless — a commentary by stephen l. carter ’79, former biden administration economists launch new budget lab, related news.

“Vital Places”: Yale Law School’s Centers Enhance Intellectual Life

Clinic Lawsuit Challenges VA Denial of Gender-Affirming Surgery

Joint Conference with NYU Law Celebrates Professor Robert Post’s Book on the Taft Court

• Community and Engagement
• Honors and Awards
• Give Now 

Goodnight Distinguished Professor in Educational Equity Maria Coady Receives Leadership Through Research Award from AERA Second Language Research SIG

NC State College of Education Goodnight Distinguished Professor in Educational Equity Maria Coady has received the Leadership Through Research Award from the American Educational Research Association (AERA) Second Language Research Special Interest Group (SIG). 

The award honors scholars who have played an active and long-standing role in the field of second language education; are nationally and internationally recognized as a leader in the field; have engaged in original and innovative research that is recognized as having a major impact on the understanding of second language education; support other scholars in furthering their research and teaching in the field; and engaged in work that has had an impact on equity and access for second language learners and their teachers. 

“It is a special honor for me to receive this award because the Second Language Research SIG has exceptional scholars and leaders in the field and their work is a hallmark of quality research, educational equity and advocacy for multilingual students,” Coady said. “A recognition by these scholars is truly humbling.” 

For more than two decades, Coady’s research has focused on English language and multilingual learners in rural settings with a particular focus on students’ literacy and language development, educational practices for rural multilingual students and ways educators and schools can best engage multilingual learners and their families.

Coady said this year marks the first time that the award committee for the Leadership Through Research Award recognized rural multilingual learners as a specific subgroup of students who are often under-resourced. 

“Little research has taken place in rural communities for our multilingual students. I believe this recognition is significant because it recognizes rural multilingual students, families and educators,” she said. “I am also grateful for the rural communities and educators who have let me into their spaces to learn about their work and the unique context of rural education.” 

Since joining the College of Education in 2022 as the inaugural Goodnight Distinguished Professor in Educational Equity, Coady has met with school leaders in multiple rural school districts in North Carolina to discuss their needs and offer support related to teaching multilingual learners. She has also initiated research studies in rural schools and provided professional development activities for K-12 teachers in the state. 

With funding from the Spencer Foundation, Coady last year hosted the first-ever Conference on Rural English Learner Education and Research (CREER) , a gathering that brought together scholars and educators from across the country to identify and discuss the state of educational programs, policies and practices for multilingual learners and families as well as create a collaborative plan and a national research agenda for this population.

In 2023, she also released Educating Multilingual Students in Rural Schools , an edited book that features place-based research studies and professional development projects that address localized challenges for working with rural multilingual students and families, and was elected to the National Association for Bilingual Education Executive Board .

“Working in rural multilingual education has been a tremendous journey,” Coady said. “Very little research has captured or sought to understand the unique context of rural education and how multilingual students and families have contributed to thriving rural communities today. During COVID, these were our unsung essential worker-heroes. I believe the research has blazed a trail to illuminate these students and families and their strengths.”

• Research and Impact
• homepage-news
• honors and awards
• Maria Coady
• multilingual learners

More From College of Education News

Assistant Professor of Educational Evaluation and Policy Analysis Lam Pham Receives NC State’s 2023-24 Outstanding Teacher Award 

College of Education Awarded $4 Million in Grant Funding From October 2023 Through March 2024 

Distinguished University Professor of Mathematics and Statistics Education Hollylynne Lee Leads Team Named Finalist in 2023-24 Tools Competition 

NBC News Graphics

Explaining the news through visualizations and data analysis from the NBC News Digital Data/Graphics team.

• The Data Point

Local officials increasingly targeted for threats and harassment, new data shows

Data Graphics

Inflation in america: where are prices rising and falling.

Israel-Hamas war

Class destroyed: a look at what has become of gaza's revered universities, covid by the numbers.

Coronavirus

Tracking covid hospitalizations.

Tracking Covid deaths

More graphics, map: see the states where marijuana is legal.

Does your state have abortion restrictions? See our breakdown of each state's rules

Map: 29 million Americans live under new voter ID laws put in place since 2020

Graphic: Tracking Biden’s topics at the State of the Union

Can you tell which piece of audio was made by AI?

Map: See which states have introduced or passed anti-DEI bills

Map: Where the pills for medication abortions are legal

Map: See how the largest Texas wildfire compares to where you live

How the Israel-Hamas conflict has spread beyond the Mideast

Presidential approval tracker: How popular is Joe Biden?

‘This is not a Lahaina problem’: Once unthinkable, frequent fires are Hawaii’s new normal.

Together we are beating cancer

• Cancer types
• Breast cancer
• Bowel cancer
• Lung cancer
• Prostate cancer
• Cancers in general
• Clinical trials
• Causes of cancer
• Coping with cancer
• Managing symptoms and side effects
• Mental health and cancer
• Money and travel
• Death and dying
• Cancer Chat forum
• Health Professionals
• Cancer Statistics
• Cancer Screening
• Learning and Support
• NICE suspected cancer referral guidelines
• Make a donation
• By cancer type
• Leave a legacy gift
• Donate in Memory
• Find an event
• Race for Life
• Charity runs
• Charity walks
• Search events
• Relay for Life
• Volunteer in our shops
• Help at an event
• Help us raise money
• Campaign for us
• Do your own fundraising
• Fundraising ideas
• Get a fundraising pack
• Return fundraising money
• Fundraise by cancer type
• Set up a Cancer Research UK Giving Page
• Find a shop or superstore
• Become a partner
• Cancer Research UK for Children & Young People
• Our Play Your Part campaign
• Brain tumours
• Skin cancer
• All cancer types
• By cancer topic
• New treatments
• Cancer biology
• Cancer drugs
• All cancer subjects
• All locations
• By Researcher
• Professor Duncan Baird
• Professor Fran Balkwill
• Professor Andrew Biankin
• See all researchers
• Our achievements timeline
• Our research strategy
• Involving animals in research
• Research opportunities
• For discovery researchers
• For clinical researchers
• For population researchers
• In drug discovery & development
• In early detection & diagnosis
• For students & postdocs
• Our funding schemes
• Career Development Fellowship
• Discovery Programme Awards
• Clinical Trial Award
• Biology to Prevention Award
• View all schemes and deadlines
• Applying for funding
• Start your application online
• How to make a successful applicant
• Funding committees
• Successful applicant case studies
• How we deliver research
• Our research infrastructure
• Events and conferences
• Our research partnerships
• Facts & figures about our funding
• Develop your research career
• Recently funded awards
• Manage your research grant
• Notify us of new publications
• Find a shop
• Volunteer in a shop
• Donate goods to a shop
• Our superstores
• Shop online
• Wedding favours
• Cancer Care
• Flower Shop
• Our eBay store
• Shoes and boots
• Bags and purses
• We beat cancer
• We fundraise
• We develop policy
• Our global role
• Our organisation
• Our strategy
• Our Trustees
• CEO and Executive Board
• How we spend your money
• Early careers
• Your development

Cancer News

• For Researchers
• For Supporters
• Press office
• Publications
• Update your contact preferences
• About cancer
• Get involved
• Our research
• Funding for researchers

The latest news, analysis and opinion from Cancer Research UK

• Science & Technology
• Health & Medicine
• Personal Stories
• Charity News
• Health & Medicine

Cancer waiting times: Latest updates and analysis

11 April 2024

This article provides information on the latest performance against cancer waiting times targets. We have another piece explaining the recent changes to cancer waiting times in England .

Over the past few years, pressure on NHS cancer services has been mounting.

Cancer waiting times, which show whether the health system is meeting its targets for quickly diagnosing and treating cancer, help show us the extent of this pressure.

Testing for cancer, diagnosing it and starting treatment quickly saves people from stress and anxiety. Not only this, but cancer that’s diagnosed and treated at an early stage, when it isn’t too large and hasn’t spread, is more likely to be treated successfully. Prompt diagnosis and treatment underpin this.

December 2023 was the first month that the reported data on cancer waiting times is reflecting the new updated NHSE targets , as explained in our previous article . 

The standards have been streamlined into 3 key cancer waiting time standards with associated targets that   indicate how well cancer services are doing .  

Here are the latest results in England for February 2024:

The Faster Diagnosis Standard:  Target Met

• 78.1% of people were diagnosed, or had cancer ruled out, within 28 days of an urgent referral in February 2024. The target is 75% and this is the first time the target has been met since its introduction in October 2021.

The 62-day referral to treatment standard: Target Missed

• Only 63.9% of people in England received their diagnosis and started their first treatment within 2 months (or 62 days) of an urgent referral* in February 2024. The target is 85%.

The 31-day decision to treat standard: Target Missed

• 91.1% of people started treatment** within 31 days of doctors deciding a treatment plan in February 2024. The target is 96% . 

The  above  data are specific to England. Scotland,  Wales and Northern Ireland also have their own cancer waiting times targets. 

While it's promising that more people are finding out if they have cancer or not faster, thousands of people in England are still waiting longer than they should to begin treatment every single month. Behind missed targets are patients - friends, family and loved ones who are facing unacceptably long and anxious waits to find out if they have cancer and when they can begin treatment. NHS staff are doing their best, but our health service simply does not have enough equipment or staff to see, test and treat everyone in time. With a general election on the horizon, there's a real opportunity for political parties to turn things around. We urgently need more staff and equipment for the NHS, alongside reform to cancer services. Without this, cancer patients will continue to face even more fear and anxiety, during what is already a stressful time in their lives.

What does this mean for people affected by cancer?

It can be easy to forget that behind these numbers are real people going through an incredibly anxious time.

Quantifying the impact of missing targets and longer waits on patient outcomes is difficult as the research is limited.

The picture is different for different cancer types – some progress quicker than others – but we know the overall impact is likely to be negative. One study estimated that a 4-week delay to cancer surgery  led to a 6-8% increased risk of dying .

People with more aggressive cancers are prioritised for early treatment where possible, but there can be good reasons why someone might experience a long wait for treatment.

For example, it can take longer to plan treatments intending to cure someone’s cancer, and sometimes patients need prehabilitation before starting treatment to give them the best chance of recovering well.

But increases in missed targets mean people who need potentially lifesaving cancer treatments are waiting, and worrying, for longer – and that is a big concern.

Despite delays, people shouldn’t put off coming forward if they are worried about symptoms. It’s always better to be on the waiting list than not at all, and if doctors are concerned, they will push things through as quickly as possible.

Getting back on track

Once again, the cancer waiting times published today represent unacceptable waits for cancer patients. Behind every one of these missed targets are patients, friends, family and loved ones who are facing unacceptably long and anxious waits to find out if they have cancer and when they can begin treatment.  

Whilst it is welcome that the Faster Diagnostic Standard (FDS) of 75% has been met, which is testament to the hard work of NHS staff in responding to growing demands for diagnostic tests, the 75% target is set well below the originally recommended target of 95%. We have not seen all cancer waiting times met since 2015 which represents a long-term failure to plan and invest in the NHS workforce and key facilities and equipment.  

Longer, Better Lives sets out the 5 missions that we want the next Government to adopt and implement to help reduce cancer mortality rates by 15% and prevent 20,000 cancer deaths a year by 2040. It provides the ‘cancer blueprint’ for political parties ahead of a 2024 general election, setting out actions which can be taken to ensure that services are diagnosing more cancers at early stages and so that those who go on to receive a diagnosis have access to optimal treatments.

To build momentum behind this, the next UK Government should commit to consistently meeting all cancer waiting time targets for England by the end of the next Parliament, and address NHS resource gaps by developing a 10-year cancer-specific workforce plan, eliminating the wider NHS maintenance backlog by 2030 and committing to rolling ringfenced capital investment for cancer. This will only be possible through significant investment in NHS staff and equipment, coupled with reform to cancer services.  

Progress in efforts to tackle cancer is possible. Thanks to lifesaving research into cancer prevention, diagnosis and treatment, survival for all cancers combined in the UK has doubled since the early 1970s.  But with cancer cases on the rise and improvements in survival showing signs of slowing , cancer is still a defining health issue in the UK. It’s vital that the Government takes bold action to address these growing challenges. This includes introducing the Tobacco and Vapes Bill, which continues it legislative journey in Parliament next week. With thousands of lives lost to tobacco related cancers each year , the Bill is one of the biggest opportunities we have had to prevent cancer in a generation.  

With a relentless focus, and a long term plan on preventing cancer, diagnosing cancers earlier and reducing inequalities, bringing tests, treatment and innovations to patients more quickly and building a national movement to beat cancer, together – we can elevate UK cancer survival to amongst the best in the world and help everyone lead longer, better lives free from the fear of cancer.  

* Urgent referrals include urgent referrals from a GP for cancer symptoms or breast symptoms, urgent referrals from a cancer screening programme, and referrals upgraded by a consultant.  

It’s important to note that the update to cancer waiting times standards in October 2023 means that more types of referral are now included in the 62-day standard. This means that that the 62-day standard now applies to more people than before.  

** This standa rd i ncludes people starting their first treatment for canc er and a lso people starting a ny subsequ ent treatments. B e fore October 2023, t h e 31 – day standard included first treatments only.  

Husband diagnosed with kidney cancer after a MRI for another reason. Dr referred for urgent review to urology after seeing the MRI he called for another reason. Was told by the urologist that a CT would be needed. This is what has happened next CT scan 20 Feb Results kidney cancer possibly Adrenal as primary and lung nodules so was referred to Endocrine.

Was told all above on the 27 Feb by phone call. Also being referred for a lung biopsy which we have heard nothing yet. March saw Endocrine team was told bloods were needed and urine test all done and results were back on the 20 th March.

21 March was told not Adrenal as the primary as previously thought it is kidney cancer still. Was told we would get a biopsy in two weeks time . I phoned up they said biopsy would be 24 April which is nearly 6/7 weeks after and well over the two week biopsy wait was told it would be. I am still chasing this up. Lung biopsy was told 4 week wait on the 27 Feb still no news. We have been passed from Kidney to Endcrine back to urology again. No treatment Not even seen an oncologist yet. Only had CT MRI Full blood Urine test One Doctor seen at one appointment Shocking really we was told on the the letter its stage 4 not even by a doctor.

I had a fall at work of about 9 feet. Sustained Multiple fractures, my Wrist, Collar bone. etc,. Had C.T Scan followed by M.I.R while in A&E. 5 days later got phone call to say they had found a growth. At Adrenal Gland was told it was on it.(So assumed on outside ) Then on the 6th day since fall got another phone call to go in for what the caller said was for emergency M.I.R and Bloods..In mean time received letter saying growth was inside the Adrenal Gland.? So do not know if on outside or inside and not told size of it..Just told When results of last M.I.R. are in system i would be getting appointment to Endocrinology. That was 3 weeks ago..I have phoned Endocrinology twice once it got to over 2 weeks. Keep being told my results are not yet in the System. And in any case waiting list for Endocrine appt,. is one huge long waiting list. Today weirdly i was told my results are still not in system but then was told She would get my Consultant i have not met yet to phone me..Obviously i cannot work until fractures heal and plaster comes off. So i have too much time to worry and feel in Limbo as to what will,… is to happen next..

Fourteen years of incompetent government.

too informative and thanks for sharing this much knowledge with us.

Husband diagnosed with bladder cancer in August 2023. Awaiting a bladder removal. Aggressive cancer & waiting list is 4-5 months. Due according to the surgeon to strikes. I will be taking legal advice & action. NOT good enough.

I had a PSA test in January that scored 19. The follow up test two weeks later scored 21. I then had an MRI scan followed buy a CT scan & prostate gland biopsy. On 19th May a consultant Urologist at Leicester General Hospital told me I had stage three cancer with a high Gleeson score. He prescribed hormone treatment and referred me to Oncology. On the 22nd Aug I saw an Oncology consultant & was told I needed seven & half weeks of radiotherapy. Owing to the “backlog” treatment wouldn’t start for two & half months and if I hadn’t heard anything by then to “Give them a call” I was advised by Prostaid to “chase this up” Today I called Radiotherapy at Leicester Royal Infirmary and was told I’m number eighty in the queue and the list is being cleared at four per week. Unfortunately, it looks as if I have another five months to wait before any futher treatment will start. I’ll continue with hormone injections.

My daughter was diagnosed with grade 4 bladder cancer on 14th July 2023. She has still not started treatment. Is this because they know she’s going to die so they see no urgency in treating her. They can’t operate and she only saw the oncologist 10 days ago. It’s disgraceful. She is very distressed at the lack of treatment and this can’t be doing her any good physically either.

I am not happy I have not had chemo for 6mths it is very stressful

From my own experience, I cannot fault the care and treatment I have received from Oxford University NHS Trust. I was referred by my GP for tests on 14/02/23, received a phone call from my local hospital, The Horton General in Banbury, on the 15th inviting me for a CT Scan on the 16th, on the 16th I received another phone call this time from the endoscopy unit offering me an appointment on the 18th,…. bad news! On the 1st March I was sat in front of my consultant at the Churchill Hospital in Oxford getting the really bad news. I started palliative chemotherapy and immunotherapy on 29th March and have just completed my sixth and final cycle of chemo with immuno to continue. Perhaps I am lucky (well only sort of, because the outcome is now per-ordained) because of where I live and OUHNHSTrust includes the Churchill Hospital, an acknowledged cancer care unit. Finally, a big shout out to ALL the wonderful and caring staff, from Professor Ramon De Melo, Dr. (Consultant) Paul Miller, all the Macmillan nurses, all the nurses and staff at the Horton GH in Banbury and particularly those at the Brodey Center who administer the chemo/immuno therapies.

I waited 10 weeks for results of my two yearly scan! Consultant said well if it was good news I would have rang you within a couple of weeks! I started palliative chemo 16 weeks after my scan! The stress this has caused for me and my family is unimaginable. My cancer is not curable but it is treatable. At the time of the scan my cancer spread was small but 16 weeks down the line who knows!

These figures showing the many missed targets are absolutely shocking but don’t come as a surprise. As a former experienced RadiationOncologist in the north of England, I kept making awareness of delays in cancer diagnosis and treatment, particularly radiotherapy, in the public domain 30 years ago. The current dreadful missed target figures are a direct result of long term significant underfunding of cancer services by many governments and are extremely worrying.

Tell us what you think

Leave a reply cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Read our comment policy .

Highlighted content

Making cancer screening work for you, that cancer conversation podcast – longer, better lives: ep.2 why did a doctor have to wait for her cancer treatment, what's it like to be diagnosed with cancer as a teenager, can vaping cause changes in our cells, flame of hope awards 2024: spotlight on our inspiring volunteers, a message to mps: don’t miss the opportunity to make smoking history, from lemonade stand to grand challenge: changing how we treat cancer in children and young people, cancer deaths plummet in middle-aged people, related topics.

• Health service policy
• Treatments policy
• Cancer in the news

Design and Analysis of Experiments Conference comes to Virginia Tech

The statistical conference attracts researchers from around world.

• Melissa McKeown

11 Apr 2024

• Share on Facebook
• Share on Twitter
• Copy address link to clipboard

The Virginia Tech Department of Statistics will host the 12th event in the Design and Analysis of Experiments conference series, slated to take place May 15-17. The conference will focus on emerging areas of research in experimental design as well as novel innovations in traditional areas.

This year’s event, which will be held primarily in the Data and Decision Sciences Building on the Blacksburg campus, will feature invited sessions, poster sessions, mentoring sessions, roundtable sessions, and a panel discussion.

What is the conference series?

The Design and Analysis of Experiments conference series began in 2000 with a regional event hosted by the Ohio State University and has since expanded to be international in scope, attracting leading researchers from across North America and the world.

Among the goals of the conference series is the provision of support and encouragement to junior researchers in the statistical field of design and analysis of experiments as well as stimulating interest in topics of practical relevance to science and industry. Notably, this year's events will pair junior and senior researchers for mentoring purposes.

Invited sessions

The agenda includes 10 invited sessions featuring national and global experts in the field of design and analysis of experiments:

• Advancements in Screening Design, Nonregular Design, and Space-Filling Design
• Causal Inference and Experimental Design
• Covering Arrays and Combinatorial Testing
• Design Issues in Uncertainty Quantification
• Experimental Design for Transportation Studies
• Factorial/Multi-level Design
• Online Experimentation
• Optimal Experimental Design
• Orthogonal Arrays and Related Designs
• Sequential Design, Active Learning, and Bayesian Optimization

Registration and information

Registration for this year’s event is currently open. Early bird registration closes April 15.

For more information, visit the event website or contact organizers Xinwei Deng , Anne Driscoll , or J.P. Morgan .

While  sponsorship opportunities  are still available, the following organizations are currently supporting the success of this year’s event:

• National Science Foundation
• National Security Agency
• Virginia Tech Academy of Data Science
• Virginia Tech National Security Institute

540-231-6468

• Academy of Data Science
• College of Science

Related Content

All MRS websites use cookies to help us improve our services. Any data collected is anonymised. If you continue using this site without accepting cookies you may experience some performance issues. Read about our cookies here .

Sort by: Newest Oldest Relevance

• You do not need to register again if you already have an MRS web account, just sign in.
• About Research Live
• Contributor guidelines
• A to Z Contributors
• Publications Index
• Terms & Conditions
• Research Buyers Guide
• Geodemographics
• MRS Website

• Forgot Password?
• Research Live
• Leisure & Arts
• Public Sector

Bethan Blakeley: Learning on the job

Bethan Blakeley

• 2024 elections
• Annual Conference 2024
• Behavioural science
• Cost of Living
• Data analytics
• Innovations
• Social media
• Sustainability
• Advertisement Features
• IMPACT Reports
• New business
• Research Heroes 2021
• Research Heroes 2023
• Asia Pacific
• Latin America
• Middle East and Africa
• North America
• Beam Suntory
• British Gas
• British Museum
• Chapel Down
• Compare the Market
• Jaguar Land Rover
• Marks & Spencer
• Rugby League
• Warner Leisure Hotels

Impact magazine is a quarterly publication for MRS members. You can access Impact content on this website.

• Terms & Conditions
• Research Live Live
• Toggle navigation Toggle navigation -->
• Sign In Forgot Password? Sign In

Read of the day 6 min read

Q&A: How a job share turned into a business for Wren Insight

Research Live sits down with Wren Insight founders Jude Terry and Hannah Beech to discuss job sharing and whether the sector could benefit from more flexibility.

Katie McQuater

How research companies should respond to the AI revolution

Unlimited sold to Accenture

Whatever happened to ED&I?

Mark Thorpe

Tracking generative AI: How are insights suppliers using it?

Norstat to change ownership

Liam Kay-McClean

Advertisement feature

The True Role of Insight Teams in Customer-Led Transformation

Haleon hires head of insights and analytics

How brands can support consumers in the cost-of-living crisis

Kim Lambert

News Round Up

Fantix agrees two first-party data partnerships

MPs call on government to publish more evidence used in policy decisions

New hires at Context+

Opinium names head of brand and product research

Sago launches Sago Health

The Round Up

Hootsuite agrees Talkwalker acquisition

The world's leading job site for research and insight

Resources Group Research Manager – Varied projects including behavioural science! £35,000 – £45,000 + strong benefits

Resources Group Insight Manager (Client Side) – Not-For-Profit/Sustainability To £55,000

Spalding Goobey Associates Insight Manager – Quant or Mixed Methods – Bespoke Strategy Consultancy £36 – 48,000 + Bens

Sign up for the latest news and opinion. You will be asked to create an account which also gives you free access to premium Impact content.

  Daily   Weekly   Both

Most Popular

Accenture buys Mindcurv

Strat7 Incite expands senior team

Room 101: What market research terms should be binned?

Rise in consumer insight maturity, finds GRBN study

Rainmakers integrated into Strat7 Incite

DIY research is reshaping agency-client dynamics

Daniel Kahneman dies aged 90

How research helped in the battle to save Europe’s peatlands

@RESEARCH LIVE

Media evaluation firm Comscore has increased its revenue in the second quarter but has made a net loss of $44.9m, a… https://t.co/rAHZYxiapz

RT @ImpactMRS : Marginalised groups are asserting themselves in Latin America, with diverse creative energy and an embrace of indigenous cul…

There is no evidence that Facebook’s worldwide popularity is linked to widespread psychological harm, according to… https://t.co/wS1Um3JRS5

Featured company

Town/Country: London

Tel: +44 (0)20 7431 4366

We are an independent participant recruitment company. We recruit for all online and face to face methodologies by using traditional, modern and emerging techniques. We work with recruiters . . .

The Research Buyers Guide

Find your next agency.

Powered by the Research Buyers Guide

Advanced Search

Brought to you by:

©2024 The Market Research Society, 15 Northburgh Street, London EC1V 0JR Tel: +44 (0)20 7490 4911 [email protected]

• Advertisers
• Accessibility statement
• Publication index
• Research Buyer's Guide
• MRS WEBSITE

The post-demographic consumerism trend means segments such age are often outdated, from @trendwatching #TrendSemLON

Are you human?

• Election 2024
• Entertainment
• Newsletters
• Photography
• Personal Finance
• AP Investigations
• AP Buyline Personal Finance
• Press Releases
• Israel-Hamas War
• Russia-Ukraine War
• Global elections
• Asia Pacific
• Latin America
• Middle East
• Election Results
• Delegate Tracker
• AP & Elections
• March Madness
• AP Top 25 Poll
• Movie reviews
• Book reviews
• Personal finance
• Financial Markets
• Business Highlights
• Financial wellness
• Artificial Intelligence
• Social Media

Analysis: Iran upends decades of shadow warfare in direct attack on Israel as tensions mount at home

A woman walks past an anti-Israeli banner on a building at the Felestin (Palestine) Square in downtown Tehran, Iran, Sunday, April 14, 2024. Israel on Sunday hailed its air defenses in the face of an unprecedented attack by Iran, saying the systems thwarted 99% of the more than 300 drones and missiles launched toward its territory. The sign in Hebrew reads: “Your next mistake will be the end of your fake country.” The sign in Farsi reads: “The next slap will be harder.” (AP Photo/Vahid Salemi)

Motorbikes cross an intersection in downtown Tehran, Iran, Sunday, April 14, 2024. Israel on Sunday hailed its air defenses in the face of an unprecedented attack by Iran, saying the systems thwarted 99% of the more than 300 drones and missiles launched toward its territory. (AP Photo/Vahid Salemi)

People cross an intersection in downtown Tehran, Iran, Sunday, April 14, 2024. Israel on Sunday hailed its air defenses in the face of an unprecedented attack by Iran, saying the systems thwarted 99% of the more than 300 drones and missiles launched toward its territory. (AP Photo/Vahid Salemi)

Passengers use a BRT bus in downtown Tehran, Iran, Sunday, April 14, 2024. Israel on Sunday hailed its air defenses in the face of an unprecedented attack by Iran, saying the systems thwarted 99% of the more than 300 drones and missiles launched toward its territory. (AP Photo/Vahid Salemi)

• Copy Link copied

DUBAI, United Arab Emirates (AP) — Iran’s direct attack on Israel over the weekend upended decades of its shadowy warfare by proxy, something Tehran has used to manage international repercussions for its actions. But with both economic and political tensions at home boiling, the country’s Shiite theocracy chose a new path as changes loom for the Islamic Republic.

Supreme Leader Ayatollah Ali Khamenei will mark his 85th birthday Friday, with no clear successor in sight and still serving as the final arbiter of every decision Iran makes. Coming to power in the wake of Iran’s devastating eight-year war with Iraq in the 1980s, Khamenei preached for years about “strategic patience” in confronting his government’s main rivals, Israel and the United States, to avoid open combat.

That saw Iran invest more deeply in regional militia forces to harass Israel — such as Hamas in the Gaza Strip or Lebanon’s Hezbollah militia — and contain the U.S., like with the militias that planted devastating improvised explosives that killed American troops during the Iraq war. That’s extended even into impoverished Yemen, where Iran’s arming of the Houthi rebels empowered their takeover of the capital and checkmated a Saudi-led coalition still trapped in a yearslong war there.

That strategy changed Saturday. After days of warnings, Iran launched 170 bomb-carrying drones, more than 30 cruise missiles and more than 120 ballistic missiles toward Israel, according to an Israeli count. Those weapons included the same bomb-carrying drones Iran supplied to Russia for its grinding war on Ukraine.

Despite Israel and the U.S. describing 99% of those projectiles being shot down , Iran has called the attack a success. Iranian Foreign Minister Hossein Amirabdollahian said Monday the attack was “to deter, punish and warn the Zionist regime.” Khamenei himself had called for Iran to “punish” Israel as well.

The trigger for the attack came April 1, when a suspected Israeli strike hit a consular annex building by Iran’s Embassy in Damascus, Syria , killing at least 12, including a top commander of Iran’s paramilitary Revolutionary Guard’s expeditionary Quds Forces.

However, for years, Iran and Israel have been targeting each other’s interests across the Middle East.

Israel is suspected of assassinating Iranian nuclear scientists and sabotaging atomic sites in the Islamic Republic. In Syria, Israel has repeatedly bombed airports likely to interrupt Iranian weapons shipments, as well as killed other Guard officers. Meanwhile, Iran is suspected of carrying out a host of bombings and gun attacks targeting Jews and Israeli interests over the decades.

But the embassy attack struck a nerve with the Iranian government.

“Attacking our consulate is like attacking our soil,” Khamenei said April 10.

It also comes amid a moment filled with uncertainty for Iran. As Khamenei grows older, power has become ever-more consolidated in the country.

Hard-liners control every lever of power within both security services and political bodies, with none of the relative moderates who once shepherded Iran’s nuclear deal with world powers into existence.

That includes former President Hassan Rouhani, who led the effort. Authorities barred Rouhani earlier this year from running again to hold his seat on the Assembly of Experts, the 88-cleric body that will pick Iran’s next supreme leader.

The hard-liners’ grip on power has seen voter turnout drop to its lowest level since the 1979 Islamic Revolution. Their stranglehold also leaves them as the only political faction to blame as the public remains incensed by Iran’s collapsing economy.

The nuclear deal’s demise, after former President Donald Trump unilaterally withdrew America from the accord in 2018, has seen Iran’s rial currency tumble. The rial now seesaws near record lows, trading Monday at 658,000 to the dollar — down from 32,000 at the time the agreement was reached nearly a decade ago.

Already, prosecutors in Tehran have begun a criminal investigation into the Jahan-e Sanaat newspaper and a journalist over a story on the possible economic impact of Iran’s attack on Israel. The judiciary’s Mizan news agency described the report as “disturbing the psychological security of society and making the country’s economic atmosphere turbulent.”

His case comes as other journalists and activists report being summoned by authorities, portending a new crackdown on any sign of dissent in the country.

There are also signs that authorities appear to be preparing for a new push at enforcing the country’s mandatory headscarf, or hijab, laws for women.

“The Tehran police — as in all other provinces — will start to confront all lawbreaking with regard to the hijab,” said Tehran police chief Brig. Gen. Abbas Ali Mohammadian, according to the semiofficial ISNA news agency.

Some women in Tehran still walk through the streets with their hair uncovered, a continued protest since the nationwide 2022 demonstrations over the death of Mahsa Amini , arrested by police for not wearing a hijab to their liking. United Nations investigators say Iran was responsible for Amini’s death and violently put down largely peaceful protests in a monthslong security crackdown that killed more than 500 people and saw over 22,000 detained.

A new push for hijab enforcement may reignite that anger, particularly in Tehran. Meanwhile, rumors persist that the government may soon raise the country’s heavily subsidized gasoline prices. A price increase in 2019 grew into nationwide antigovernment protests that reportedly saw over 300 people killed and thousands arrested.

Those tensions, coupled with hard-liners’ grip on power and Khamenei’s age, signal more changes loom for the country. And while Iran said of its attack Saturday that “the matter can be deemed concluded” even before missiles reached Israel, that doesn’t mean there won’t be further retaliation from the country.

EDITOR’S NOTE — Jon Gambrell , the news director for the Gulf and Iran for The Associated Press, has reported from each of the Gulf Cooperation Council countries, Iran and other locations across the Mideast and wider world since joining the AP in 2006.

Read our research on: Gun Policy | International Conflict | Election 2024

Regions & Countries

News platform fact sheet.

The transition of the news industry away from print, television and radio into digital spaces has caused huge disruptions in the traditional news industry , especially the print news industry . It is also reflected in the ways individual Americans say they are getting their news. Today, an overwhelming majority of Americans get news at least sometimes from digital devices. Explore the patterns and trends that shape the platforms Americans turn to for news below.

News consumption across platforms

A large majority of U.S. adults (86%) say they often or sometimes get news from a smartphone, computer or tablet, including 56% who say they do so often. This is more than the 49% who said they often got news from digital devices in 2022 and the 51% of those who said the same in 2021. The portion that gets news from digital devices continues to outpace those who get news from television. The portion of Americans who often get news from television has stayed fairly consistent, at 31% in 2022 and 32% in 2023. Americans turn to radio and print publications for news far less frequently than to digital devices and television.

When asked which of these platforms they  prefer  to get news on, nearly six-in-ten Americans say they prefer a digital device (58%), more than say they prefer TV (27%). Even fewer Americans prefer radio (6%) or print (5%).

News across digital platforms

Though digital devices are by far the most common way Americans access their news, where they get that news  on  their devices is divided among a number of different pathways. Today, news websites, apps and search engines are the digital pathways most Americans get news from at least sometimes. Half of Americans at least sometimes get news from social media, and three-in-ten say the same of podcasts.

Among digital platforms, news websites or apps are also the most preferred source for news: A quarter of U.S. adults prefer to get their news this way, compared with 15% who prefer search, 12% who prefer social media and 6% who say they prefer podcasts.

Who uses each news platform

News consumption across platforms varies by age, gender, race, ethnicity, educational attainment and political leaning. Americans ages 50 and older are more likely than younger adults to turn to and prefer television and print publications.

• News platform use
• Digital platform use

% of U.S. adults in each demographic group who get news at least sometimes from …

*Estimates for Asian adults are representative of English speakers only. Note: White, Black and Asian adults include those who report being only one race and are not Hispanic; Hispanic adults are of any race. Source: Survey of U.S. adults conducted Sept. 25-Oct. 1, 2023.

• News platform preference
• Digital platform preference

% of U.S. adults in each demographic group who say they prefer ___ for getting news

Find out more

This fact sheet was compiled by Research Analyst Jacob Liedke and Research Associate Luxuan Wang .

Read the  methodology and the topline .

Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. This is the latest report in Pew Research Center’s ongoing investigation of the state of news, information and journalism in the digital age, a research program funded by The Pew Charitable Trusts, with generous support from the John S. and James L. Knight Foundation.

Follow these links for more in-depth analysis of news consumption:

Social Media and News Fact Sheet , Nov. 15, 2023

Americans are following the news less closely than they used to , Oct. 24, 2023

Black Americans’ Experiences With News , Sept. 26, 2023

For National Radio Day, key facts about radio listeners and the radio industry , Aug. 17, 2023

Podcasts as a Source of News and Information , April 18, 2023

The Role of Alternative Social Media in the News and Information Environment , Oct. 6, 2022

News on Twitter: Consumed by Most Users and Trusted by Many , Nov. 15, 2021

About four-in-ten Americans say social media is an important way of following COVID-19 vaccine news , Aug. 24, 2021

Large Majorities of Newsmax and OAN News Consumers Also Go to Fox News , March 23, 2021

More than eight-in-ten Americans get news from digital devices , Jan. 12, 2021

Measuring News Consumption in a Digital Era , Dec. 8, 2020

Many Americans Get News on YouTube, Where News Organizations and Independent Producers Thrive Side by Side , Sept. 28, 2020

Americans Who Mainly Get Their News on Social Media Are Less Engaged, Less Knowledgeable , July 30, 2020

Read all reports and short reads related to news platforms and sources .

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

#protect2024 Secure Our World Shields Up Report A Cyber Issue

CISA Announces Malware Next-Gen Analysis

Updated analysis system enhances scalability, streamlines workflow and empowers threat hunts 

WASHINGTON – The Cybersecurity and Infrastructure Security Agency (CISA) announces today a new release of our malware analysis system, called Malware Next-Gen, which allows any organization to submit malware samples and other suspicious artifacts for analysis. Malware Next-Gen allows CISA to more effectively support our partners by automating analysis of newly identified malware and enhancing the cyber defense efforts.

Timely, actionable intelligence on malware, such as how it works and what it is designed to do, is crucial to network defenders conducting potential cyber incident response and/or threat hunts.  Malware Next-Gen provides advanced and reliable malware analysis on a scalable platform, capable of meeting the increasing demands of future workloads. The integrated system provides CISA analysts and operations community members with multilevel containment capabilities for the automatic analysis of potentially malicious files or uniform resource locators (URLs).

“Effective and efficient malware analysis helps security professionals detect and prevent malicious software from enabling adversary access to persistence within an organization. Malware Next-Gen is a significant leap forward in CISA's commitment to enhancing national cybersecurity,” said CISA Executive Assistant Director for Cybersecurity Eric Goldstein . “Our new automated system enables CISA’s cybersecurity threat hunting analysts to better analyze, correlate, enrich data, and share cyber threat insights with partners. It facilitates and supports rapid and effective response to evolving cyber threats, ultimately safeguarding critical systems and infrastructure.”

Since November, Malware Next-Gen has been available to .gov and .mil organizations. Nearly 400 registered users have submitted more than 1,600 files resulting in the identification of approximately 200 suspicious or malicious files and URLs, which were quickly shared with partners. While members of the public may submit a malware sample; only authorized, registered users are able to receive analytical results from submissions.

All organizations, security researchers and individuals are encouraged to register and submit suspected malware into this new automated system for CISA analysis. For more information, visit: Malware Next-Generation Analysis .

About CISA  

As the nation’s cyber defense agency and national coordinator for critical infrastructure security, the Cybersecurity and Infrastructure Security Agency leads the national effort to understand, manage, and reduce risk to the digital and physical infrastructure Americans rely on every hour of every day.

Visit CISA.gov for more information and follow us on  Twitter ,  Facebook ,  LinkedIn , Instagram . 

Related Articles

Cisa directs federal agencies to immediately mitigate significant risk from russian state-sponsored cyber threat, cisa publishes high-risk communities webpage, april is emergency communications month, cisa marks important milestone in addressing cyber incidents; seeks input on circia notice of proposed rulemaking.

Articles on Research Brief

Displaying 1 - 20 of 409 articles.

Loneliness can kill, and new research shows middle-aged Americans are particularly vulnerable

Frank J. Infurna , Arizona State University

Rural students’ access to Wi-Fi is in jeopardy as pandemic-era resources recede

Gabriel E. Hales , Michigan State University and Keith N. Hampton , Michigan State University

Could sharing a bedroom with your pets be keeping you from getting a good night’s sleep?

Brian N. Chin , Trinity College

‘Fake news’ legislation risks doing more harm than good amid a record number of elections in 2024

Samuel Jens , Stony Brook University (The State University of New York)

Competitive workplaces don’t work for gender equality

Amalia Rebecca Miller , University of Virginia and Carmit Segal , University of Zurich

Many travel nurses opt for temporary assignments because of the autonomy and opportunities − not just the big boost in pay

Ivan Gan , University of Houston-Downtown

Tweaking US trade policy could hold the key to reducing migration from Central America

Raymond Robertson , Texas A&M University and Kaleb Girma Abreha , Texas A&M University

Not having job flexibility or security can leave workers feeling depressed, anxious and hopeless

Monica Wang , Boston University

Trump-era tax cuts contributed to a decline in higher ed giving, with fewer Americans donating to colleges and universities

Jin Lee , University of Louisiana at Lafayette

New studies suggest millions with mild cognitive impairment go undiagnosed, often until it’s too late

Soeren Mattke , University of Southern California and Ying Liu , USC Dornsife College of Letters, Arts and Sciences

Chilling out rather than blowing off steam is a better way to manage anger − new review of 154 studies reveals what works

Sophie L. Kjaervik , Virginia Commonwealth University and Brad Bushman , The Ohio State University

Pacemaker powered by light eliminates need for batteries and allows the heart to function more naturally − new research

Pengju Li , University of Chicago Pritzker School of Molecular Engineering

The hostility Black women face in higher education carries dire consequences

Ebony Aya , Macalester College

Teenagers often know when their parents are having money problems − and that knowledge is linked to mental health challenges, new research finds

Jamie Hanson , University of Pittsburgh

Sharks, turtles and other sea creatures face greater risk from industrial fishing than previously thought − we estimated added pressure from ‘dark’ fishing vessels

Heather Welch , University of California, Santa Cruz

Omega-3 fatty acids are linked to better lung health, particularly in patients with pulmonary fibrosis

John Kim , University of Virginia

‘Swarm of one’ robot is a single machine made up of independent modules

Yu Gu , West Virginia University and Trevor Smith , West Virginia University

Making it personal: Considering an issue’s relevance to your own life could help reduce political polarization

Rebecca Dyer , Hamilton College and Keelah Williams , Hamilton College

Flowers grown floating on polluted waterways can help clean up nutrient runoff and turn a profit

Jazmin Locke-Rodriguez , Florida International University and Krishnaswamy Jayachandran , Florida International University

Self-extinguishing batteries could reduce the risk of deadly and costly battery fires

Apparao Rao , Clemson University and Bingan Lu , Hunan University

Related Topics

• Coronavirus
• Higher ed attainment
• K-12 education
• New research
• Philanthropy and nonprofit research
• Philanthropy and nonprofits
• Quick reads
• US higher education

Top contributors

Director, Institute for Social and Health Equity; Social and Healty Equity Endowed Chair, Department of Health Policy, Management, and Behavior, School of Public Health, University at Albany, State University of New York

Distinguished Professor of Pharmacy Practice, University of Connecticut

Assistant Professor of Psychology, University of Pittsburgh

Associate Professor of Nonprofit Studies, George Mason University

Associate Professor of Education, University of Southern California

Ben L. Fryrear Chair in Marketing and Associate Professor of Business Administration, University of Pittsburgh

Associate Professor of International Business, University of South Carolina

Professor of Communication, The Ohio State University

Clinical Professor of Gerontology and Co-Director LeadingAge LTSS Center, UMass Boston

Postdoctoral Fellow at The Injury and Violence Prevention Program, Virginia Commonwealth University

Professor of Economics and Education Policy, New York University

Senior Research Fellow and Lecturer of Gerontology, LeadingAge LTSS Center @UMass Boston, UMass Boston

Associate Professor of Sociology, USC Dornsife College of Letters, Arts and Sciences

Professor of Public Policy, Education and Economics, Vanderbilt University

Associate Professor, Sociology, University of California, Santa Barbara

• X (Twitter)
• Unfollow topic Follow topic