mlsanityData to 7 Oct 2026

Misinfo Reaction Frames: Reasoning about Readers' Reactions to News Headlines

arXiv 2104.08790 ↗First posted 18 Apr 2021cs.CLcited by 32

Not on the map. Why?

Abstract (arXiv)

Even to a simple and short news headline, readers react in a multitude of ways: cognitively (e.g. inferring the writer's intent), emotionally (e.g. feeling distrust), and behaviorally (e.g. sharing the news with their friends). Such reactions are instantaneous and yet complex, as they rely on factors that go beyond interpreting factual content of news. We propose Misinfo Reaction Frames (MRF), a pragmatic formalism for modeling how readers might react to a news headline. In contrast to categorical schema, our free-text dimensions provide a more nuanced way of understanding intent beyond being benign or malicious. We also introduce a Misinfo Reaction Frames corpus, a crowdsourced dataset of reactions to over 25k news headlines focusing on global crises: the Covid-19 pandemic, climate change, and cancer. Empirical results confirm that it is indeed possible for neural models to predict the prominent patterns of readers' reactions to previously unseen news headlines. Additionally, our user study shows that displaying machine-generated MRF implications alongside news headlines to readers can increase their trust in real news while decreasing their trust in misinformation. Our work demonstrates the feasibility and importance of pragmatic inferences on news headlines to help enhance AI-guided misinformation detection and mitigation.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). Builds on 31 of its 87 references that are in the map. First posted on arXiv 18 Apr 2021.

Builds on

31 of 87 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  6. 2020MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformerscited by 1,414
  7. 2019Defending Against Neural Fake Newscited by 553
  8. 2015A Neural Attention Model for Abstractive Sentence Summarizationcited by 394
  9. 2019Social Bias Frames: Reasoning about Social and Power Implications of Languagecited by 377
  10. 2017"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detectioncited by 361
  11. 2020Social Chemistry 101: Learning to Reason about Social and Moral Normscited by 253
  12. 2020CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claimscited by 195
19 more
  1. 2019Fine-Grained Analysis of Propaganda in News Articlescited by 130
  2. 2020A Distributional Approach to Controlled Text Generationcited by 117
  3. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  4. 2020"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humanscited by 86
  5. 2020CoAID: COVID-19 Healthcare Misinformation Datasetcited by 83
  6. 2018Event2Mind: Commonsense Inference on Events, Intents, and Reactionscited by 82
  7. 2021A Survey on Stance Detection for Mis- and Disinformation Identificationcited by 75
  8. 2019The Limitations of Stylometry for Detecting Machine-Generated Fake Newscited by 63
  9. 2018Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategiescited by 47
  10. 2020Detecting East Asian Prejudice on Social Mediacited by 40
  11. 2019How Decoding Strategies Affect the Verifiability of Generated Textcited by 27
  12. 2019NELA-GT-2018: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articlescited by 26
  13. 2019Towards Automatic Detection of Misinformation in Online Medical Videoscited by 22
  14. 2020NELA-GT-2019: A Large Multi-Labelled News Dataset for The Study of Misinformation in News Articlescited by 12
  15. 2019Fake News Detection as Natural Language Inferencecited by 9
  16. 2021Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Mediacited by 8
  17. 2021VoterFraud2020: a Multi-modal Dataset of Election Fraud Claims on Twittercited by 7
  18. 2017Tabloids in the Era of Social Media? Understanding the Production and Consumption of Clickbaits in Twittercited by 5
  19. 2020Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreakcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 13; 2024: 9; 2025: 6; 2026: 3.

32 papers.

Sort:

By area:

  1. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  2. 2022Generating Literal and Implied Subquestions to Fact-check Complex Claimscited by 66
  3. 2023Natural Language Reasoning, A Surveycited by 61
  4. 2024DELL: Generating Reactions and Explanations for LLM-Based Misinformation Detectioncited by 41
  5. 2024AI as Humanity's Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Textcited by 40
  6. 2023Using Imperfect Surrogates for Downstream Inference: Design-based Supervised Learning for Social Science Applications of Large Language Modelscited by 37
  7. 2023COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statementscited by 34
  8. 2024AI for social science and social science of AI: A Surveycited by 29
  9. 2023EDIS: Entity-Driven Image Search over Multimodal Web Contentcited by 27
  10. 2023Natural Language Decompositions of Implicit Content Enable Better Text Representationscited by 18
  11. 2023Combating Online Misinformation Videos: Characterization, Detection, and Future Directionscited by 14
  12. 2024SoMeLVLM: A Large Vision Language Model for Social Media Processingcited by 11
20 more
  1. 2024LLM Chain Ensembles for Scalable and Accurate Data Annotationcited by 11
  2. 2023CRoW: Benchmarking Commonsense Reasoning in Real-World Taskscited by 8
  3. 2026Evaluating Decision Models for Text Annotation in Computational Social Sciencecited by 8
  4. 2023BiasX: "Thinking Slow" in Toxic Content Moderation with Explanations of Implied Social Biasescited by 7
  5. 2023Exploring news intent and its application: A theory-driven approachcited by 7
  6. 2025Probably Approximately Correct Labelscited by 7
  7. 2024MisinfoEval: Generative AI in the Era of "Alternative Facts"cited by 6
  8. 2023Socratis: Are large multimodal models emotionally aware?cited by 5
  9. 2025Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Modelscited by 5
  10. 2023Interpretation modeling: Social grounding of sentences by reasoning over their implicit moral judgmentscited by 2
  11. 2024RED-CT: A Systems Design Methodology for Using LLM-labeled Data to Train and Deploy Edge Classifiers for Computational Social Sciencecited by 2
  12. 2025Examining Alignment of Large Language Models through Representative Heuristics: The Case of Political Stereotypescited by 2
  13. 2025Simulating Misinformation Vulnerabilities With Agent Personascited by 2
  14. 2023Bike Frames: Understanding the Implicit Portrayal of Cyclists in the Newscited by 1
  15. 2024Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generationcited by 1
  16. 2025GenAI vs. Human Fact-Checkers: Accurate Ratings, Flawed Rationalescited by 1
  17. 2025QRA++: Quantified Reproducibility Assessment for Common Types of Results in Natural Language Processingcited by 1
  18. 2026The Proxy Presumption: From Semantic Embeddings to Valid Social Measurescited by 1
  19. 2024Generator-Guided Crowd Reaction Assessmentcited by 0
  20. 2026RCMN: Understanding Misleadingness in Influential Public Discoursecited by 0