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

arXiv: https://arxiv.org/abs/2301.11850 · HTML: https://mlsanity.com/paper/2301.11850/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 27 Jan 2023.

## Builds on (most cited first, 19 of 19)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Predicting Factuality of Reporting and Bias of News Media Sources](https://mlsanity.com/paper/1810.01765/) (2018)
- [A Survey on Computational Propaganda Detection](https://mlsanity.com/paper/2007.08024/) (2020)
- [In Plain Sight: Media Bias Through the Lens of Factual Reporting](https://mlsanity.com/paper/1909.02670/) (2019)
- [Neural Media Bias Detection Using Distant Supervision With BABE -- Bias Annotations By Experts](https://mlsanity.com/paper/2209.14557/) (2022)
- [POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection](https://mlsanity.com/paper/2205.00619/) (2022)
- [Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media](https://mlsanity.com/paper/2009.09609/) (2020)
- [Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media](https://mlsanity.com/paper/1904.00542/) (2019)
- [Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users Based on Weakly Supervised Learning](https://mlsanity.com/paper/2012.04233/) (2020)
- [Knowledge-Based Trust: Estimating the Trustworthiness of Web Sources](https://mlsanity.com/paper/1502.03519/) (2015)
- [People on Media: Jointly Identifying Credible News and Trustworthy Citizen Journalists in Online Communities](https://mlsanity.com/paper/1705.02667/) (2017)
- [Fact Checking in Community Forums](https://mlsanity.com/paper/1803.03178/) (2018)
- [In Search of Credible News](https://mlsanity.com/paper/1911.08125/) (2019)
- [A Dataset of Fact-Checked Images Shared on WhatsApp During the Brazilian and Indian Elections](https://mlsanity.com/paper/2005.02443/) (2020)
- [Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels](https://mlsanity.com/paper/2108.12519/) (2021)
- [Quotatives Indicate Decline in Objectivity in U.S. Political News](https://mlsanity.com/paper/2210.15476/) (2022)
- [Can online attention signals help fact-checkers fact-check?](https://mlsanity.com/paper/2109.09322/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Corpus for Sentence-level Subjectivity Detection on English News Articles](https://mlsanity.com/paper/2305.18034/) (2023)
- [Sentence-level Media Bias Analysis with Event Relation Graph](https://mlsanity.com/paper/2404.01722/) (2024)
- [Multi-document Summarization through Multi-document Event Relation Graph Reasoning in LLMs: a case study in Framing Bias Mitigation](https://mlsanity.com/paper/2506.12978/) (2025)
- [Structuring the Space of Perspectives](https://mlsanity.com/paper/2608.12113/) (2026)
- [DocNet: Semantic Structure in Inductive Bias Detection Models](https://mlsanity.com/paper/2406.10965/) (2024)
- [Can temporal article-level credibility signals improve domain-level credibility prediction?](https://mlsanity.com/paper/2607.04560/) (2026)

## 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.