mlsanityData to 7 Oct 2026

Predicting Sentence-Level Factuality of News and Bias of Media Outlets

arXiv 2301.11850 ↗First posted 27 Jan 2023cs.CLcited by 6

Not on the map. Why?

Abstract (arXiv)

Automated news credibility and fact-checking at scale require accurately predicting news factuality and media bias. This paper introduces a large sentence-level dataset, titled "FactNews", composed of 6,191 sentences expertly annotated according to factuality and media bias definitions proposed by AllSides. We use FactNews to assess the overall reliability of news sources, by formulating two text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets. Our experiments demonstrate that biased sentences present a higher number of words compared to factual sentences, besides having a predominance of emotions. Hence, the fine-grained analysis of subjectivity and impartiality of news articles provided promising results for predicting the reliability of media outlets. Finally, due to the severity of fake news and political polarization in Brazil, and the lack of research for Portuguese, both dataset and baseline were proposed for Brazilian Portuguese.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 19 of its 50 references that are in the map. First posted on arXiv 27 Jan 2023.

Builds on

19 of 50 references.

References · 19

  1. 2021A Survey on Automated Fact-Checkingcited by 376
  2. 2017Automatic Detection of Fake Newscited by 145
  3. 2020We Can Detect Your Bias: Predicting the Political Ideology of News Articlescited by 92
  4. 2018Predicting Factuality of Reporting and Bias of News Media Sourcescited by 71
  5. 2020A Survey on Computational Propaganda Detectioncited by 69
  6. 2019In Plain Sight: Media Bias Through the Lens of Factual Reportingcited by 68
  7. 2022Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Expertscited by 54
  8. 2022POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detectioncited by 50
  9. 2020Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Mediacited by 27
  10. 2019Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Mediacited by 26
  11. 2020Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learningcited by 23
  12. 2015Knowledge-Based Trust: Estimating the Trustworthiness of Web Sourcescited by 21
7 more
  1. 2017People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communitiescited by 10
  2. 2018Fact Checking in Community Forumscited by 10
  3. 2019In Search of Credible Newscited by 10
  4. 2020A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Electionscited by 9
  5. 2021Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channelscited by 2
  6. 2022Quotatives Indicate Decline in Objectivity in U.S. Political Newscited by 2
  7. 2021Can online attention signals help fact-checkers fact-check?cited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 1; 2026: 2.

6 papers.

  1. 2023A Corpus for Sentence-level Subjectivity Detection on English News Articlescited by 6
  2. 2024Sentence-level Media Bias Analysis with Event Relation Graphcited by 3
  3. 2025Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigationcited by 2
  4. 2026Structuring the Space of Perspectivescited by 1
  5. 2024DocNet: Semantic Structure in Inductive Bias Detection Modelscited by 0
  6. 2026Can temporal article-level credibility signals improve domain-level credibility prediction?cited by 0