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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Propagation From Deceptive News Sources Who Shares, How Much, How Evenly, and How Quickly?

As people rely on social media as their primary sources of news, the spread of misinformation has become a significant concern. In this large-scale study of news in social media we analyze eleven million posts and investigate propagation behavior of users that directly interact with news accounts identified as spreading trusted versus malicious content. Unlike previous work, which looks at specific rumors, topics, or events, we consider all content propagated by various news sources. Moreover, we analyze and contrast population versus sub-population behaviour (by demographics) when spreading misinformation, and distinguish between two types of propagation, i.e. direct retweets and mentions. Our evaluation examines how evenly, how many, how quickly, and which users propagate content from various types of news sources on Twitter. Our analysis has identified several key differences in propagation behavior from trusted versus suspicious news sources. These include high inequity in the diffusion rate based on the source of disinformation, with a small group of highly active users responsible for the majority of disinformation spread overall and within each demographic. Analysis by demographics showed that users with lower annual income and education share more from disinformation sources compared to their counterparts. News content is shared significantly more quickly from trusted, conspiracy, and disinformation sources compared to clickbait and propaganda. Older users propagate news from trusted sources more quickly than younger users, but they share from suspicious sources after longer delays. Finally, users who interact with clickbait and conspiracy sources are likely to share from propaganda accounts, but not the other way around.

social network analysis, information propagation, ↗
NASA NTRS2024

NEWS Integrated Analysis (NEWS-IA) Dataset: Budgets and Input Data

This documentation provides a summary of the underlying physical and mathematical descriptions of the global energy and water cycle budgets used to produce the NASA Energy and Water Cycle Study (NEWS) Integrated Analysis. In addition to discussion of the budget equations, a summary is provided concerning NEWS regions, input data sources, and data fields found in the NEWS-IA input data file.

J Brent Roberts↗
DOE OSTI2021

Prediction of social media postings as trusted news or as types of suspicious news

Disclosed are systems, techniques, and non-transitory storage media for predicting social media postings as being trusted news or a type of suspicious news. The systems, techniques, and non-transitory storage media are based on unique neural network architectures that learn from a combined representation including at least representations of social media posting content and a vector representation of communications among connected users.

Volkova, Svitlana↗
NASA NTRS1972

News conference on Mariner 9: News release

A discussion is presented on Mariner 9 data on Mars. The atmosphere and dust are described, and details are given on the surface features of the planet. Ultraviolet and infrared data are also discussed, as is the question on Martian life. Photographs showing clouds and surface are included.

Source record↗
DOE OSTI2018

Misleading or Falsification? Inferring Deceptive Strategies and Types in Online News and Social Media

Deceptive information in online news and social media has had dramatic effect on our society in recent years. This study is the first to gain deeper insights into writers' intent behind digital misinformation by analyzing psycholinguistic signals: moral foundations and connotations extracted from different types of deceptive news ranging from strategic disinformation to propaganda and hoaxes. To ensure consistency of our findings and generalizability across domains, we experiment with data from: (1) confirmed cases of disinformation in news summaries, (2) {propaganda}, hoax, and disinformation news pages, and (3) social media news. We first contrast lexical markers of biased language, syntactic and stylistic signals, and connotations across deceptive news types including disinformation, propaganda, and hoaxes, and {deceptive} strategies including misleading or falsification. We then incorporate these insights to build machine learning and deep learning predictive models to infer deception strategies and deceptive news types. Our experimental results demonstrate that unlike earlier work on deception detection, content combined with biased language markers, moral foundations, and connotations leads to better predictive performance of deception strategies compared to syntactic and stylistic signals (as reported in earlier work on deceptive reviews). Falsification strategy is easier to identify than misleading strategy. Disinformation is more difficult to predict than to propaganda or hoaxes. Deceptive news types (disinformation, propaganda, and hoaxes), unlike deceptive strategies (falsification and misleading), are more salient, and thus easier to identify in tweets than in news reports. Finally, our novel connotation analysis across deception types provides deeper understanding of writers' perspectives and therefore reveals the intentions behind digital misinformation.

deception↗
DOE OSTI2025

CAMFeND: Credibility-Aware Multimodal Fake News Detection with Rotational Attention

In the evolving digital landscape, fake news is a significant challenge, influencing public perception and decision-making. Traditional detection approaches focus on single-modal data or simple multimodal fusion, often overlooking deeper interactions and news credibility. We propose a novel model addressing these limitations by introducing rotational attention and news domain information as a feature. Unlike static attention mechanisms, our rotational attention dynamically shifts query, key, and value roles across text and image inputs, enabling richer cross-modal interaction. Incorporating news domain information further enhances the model’s reliability by associating news posts with top domains extracted from Google search results, reducing false detections. This approach assesses both the content and the broader web context in which the news is discussed. Our model outperforms existing state-of-the-art methods by providing deeper, layered multimodal integration and domain information analysis, resulting in a more robust and adaptive fake news detection system.

Gupta, Nidhi↗
DOE OSTI2019

Explaining Multimodal Deceptive News Prediction Models

Social media plays a valuable role in rapid news dissemination, but it also serves as a vehicle to propagate unverified information. For example, news shared on Facebook or Twitter may actually contain disinformation, propaganda, hoaxes, conspiracies, clickbait or satire. This paper presents an in-depth analysis of the behavior of suspicious news classification models including error analysis and prediction confidence. We consider five deep learning architectures that leverage combinations of text, linguistic and image input signals from tweets. The behavior of these models is analyzed across four suspicious news prediction tasks. Our findings include that models leveraging only the text of tweets outperform those leveraging only the image (by 3-13% absolute in F-measure), and that models that combine image and text signals with linguistic cues e.g., biased and subjective language markers can, but do not always, perform even better. Finally, our main contribution is a series of analyses, in which we characterize text and image traits of our classes of suspicious news and analyze patterns of errors made by the various models to inform the design of future deceptive news prediction models.

deep learning, social media, deception↗
DOE OSTI2019

Nuclear Crisis Communication in an Era of Fake News and Media Overload - 19414

Over the past several decades, the nuclear industry has worked diligently to improve the security and safety aspects of their technology, but it has failed to meet the challenge of the public's negative perception. Studies have shown that nuclear energy and nuclear waste evoke fear among the general populace because people see risks as immediate, unknown, uncontrollable, and potentially catastrophic. To some extent, negative public perception challenges for the nuclear industry are primarily a consequence of its own inability to communicate the benefits of nuclear science effectively with the lay public. While effective and trustworthy communication cannot guarantee success, it helps shape public opinion, and for a routinely misperceived project like a nuclear facility, that is paramount. Fukushima was the first nuclear accident to occur in the world where citizens have access to 24/7 news channels and the ability to respond in a real-time manner through social media outlets. Following the Fukushima accident, social media sites were filled with information regarding nuclear energy that were based more on mass rumor and even paranoia than facts. More locally, we have seen this trend following what was a minor external radiation release event at the WIPP in February 14, 2014. Many online news sources were flooded with anti-nuke stories aimed at pushing a fear of radiation, such as inferring an increase in cancer rates among the public from this minor incident; which was exactly the opposite of what was true. Today, when the public gets its news from social media and online sources, people are faced with rapidly changing and contrasting interpretations of the same story, depending on when they accessed the latest news. They are also increasingly vulnerable to fake news, including disinformation deliberately planted and cultivated by the anti-nuclear movement. Public reactions following these nuclear events reinforced the value of effective communication in maintaining the high level of transparency necessary for long-term, local acceptance and support for nuclear facilities. A lack of trustworthy communication leaves a vacuum that can be quickly filled by rumors and fake information. When dealing with a nuclear crisis, failure to recognize the rapidly changing and contrasting media framing, or believing fake news could have very real and catastrophic consequences. Trusted organizations must swiftly take charge and guide the way in which the crisis is framed to keep the public adequately and accurately informed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2018

Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources

In the age of social news, it is important to understand the types of reactions that are evoked from news sources with various levels of credibility. In the present work we seek to better understand how users react to trusted and deceptive news sources across two popular, and very different, social media platforms. To that end, (1) we develop a model to classify user reactions into one of nine types, such as answer, elaboration, and question, etc, and (2) we measure the speed and the type of reaction for trusted and deceptive news sources for 10.8M Twitter posts and 6.2M Reddit comments. We show that there are significant differences in the speed and the type of reactions between trusted and deceptive news sources on Twitter, but far smaller differences on Reddit.

machine learning, social media, natural language p↗
DOE OSTI2025

Towards Content Authenticity: Multimodal Fake News Detection and AI-Generated Text Identification

In today’s digital world, the spread of fake news and the rise of AI-generated text have become major threats to content authenticity and public trust. This thesis addresses both challenges through two complementary research directions: detecting fake news using multimodal features, and identifying AI-generated text using semantic and structural reasoning. The first part of the work focuses on fake news detection by introducing a novel model that combines text and image features through a unique rotational attention mechanism. Unlike traditional attention methods, this approach rotates the roles of query, key, and value across modalities to capture deeper interactions. Additionally, the model incorporates external domain information by linking news posts to top-ranked websites from Google search results, which helps assess the credibility of content based on its broader web context. This results in a more reliable and accurate fake news detection system that outperforms existing state-of-the-art methods. The second part presents SGG-ATD, a new framework for detecting AI-generated text. It uses masked language modeling to measure sentence coherence, followed by constructing a graph where keywords—both original and predicted—are connected based on semantic and contextual similarity. A Graph Convolutional Network (GCN) is then used to learn structural relationships within the text for final classification. Experimental results demonstrate that SGG-ATD achieves high F1-scores and consistently outperforms strong baselines. This method contributes to robust AI text detection, supporting accountability and resilience against AI-driven misinformation.

Gupta, Nidhi↗
DOE OSTI2024

News Media Framing of Suicide Circumstances and Gender: Mixed Methods Analysis

Background: Suicide is a leading cause of death worldwide. Journalistic reporting guidelines were created to curb the impact of unsafe reporting; however, how suicide is framed in news reports may differ by important characteristics such as the circumstances and the decedent’s gender. Objective: This study aimed to examine the degree to which news media reports of suicides are framed using stigmatized or glorified language and differences in such framing by gender and circumstance of suicide. Methods: We analyzed 200 news articles regarding suicides and applied the validated Stigma of Suicide Scale to identify stigmatized and glorified language. We assessed linguistic similarity with 2 widely used metrics, cosine similarity and mutual information scores, using a machine learning–based large language model. Results: News reports of male suicides were framed more similarly to stigmatizing (P<.001) and glorifying (P=.005) language than reports of female suicides. Considering the circumstances of suicide, mutual information scores indicated that differences in the use of stigmatizing or glorifying language by gender were most pronounced for articles attributing legal (0.155), relationship (0.268), or mental health problems (0.251) as the cause.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1987

Index to NASA news releases and speeches, 1986

This issue of the Index to NASA News Releases and Speeches contains a listing of news releases distributed by the Office of Public Affairs, NASA Headquarters, and a selected listing of speeches presented by members of the Headquarters staff during 1986. The index is arranged in six sections: Subject Index, Personal Names Index, News Release Number Index, Accession Number Index, and Speeches and News Releases.

Source record↗
NASA NTRS1988

Index to NASA news releases and speeches, 1987

This issue of the Index to NASA News Releases and Speeches contains a listing of news releases distributed by the Office of Public Affairs, NASA Headquarters, and a selected listing of speeches presented by members of the Headquarters staff during 1987. The index is arranged in six sections: Subject Index, Personal Names Index, News Release Number Index, Accession Number Index, and Speeches and News Releases.

Source record↗
NASA NTRS1989

Index to NASA news releases and speeches, 1988

This issue of the Index to NASA News Releases and Speeches contains a listing of news releases distributed by the Office of Public Affairs, NASA Headquarters, and a selected listing of speeches presented by members of the Headquarters staff during 1988. The index is arranged in six sections: Subject Index, Personal Names Index, News Release Number Index, Accession Number Index, and Speeches and News Releases.

Source record↗
NASA NTRS1990

Index to NASA news releases and speeches, 1989

This issue of the Index to NASA News Releases and Speeches contains a listing of news releases distributed by the Office of Public Affairs, NASA Headquarters, and a selected listing of speeches presented by members of the Headquarters staff during 1989. The index is arranged in six sections: Subject Index, Personal Names Index, News Release Number Index, Accession Number Index, and Speeches and News Releases.

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NASA NTRS1993

Index to NASA news releases and speeches, 1992

This issue of the Index to NASA News Releases and Speeches contains a listing of news releases distributed by the Office of Public Affairs, NASA Headquarters, and a selected listing of speeches presented by members of the Headquarters staff during 1992. The index is arranged in six sections: subject index, personal names index, news release number index, accession number index, speeches, and news releases.

Source record↗