# A Survey on Predicting the Factuality and the Bias of News Media

arXiv: https://arxiv.org/abs/2103.12506 · HTML: https://mlsanity.com/paper/2103.12506/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 105 references that are in the map.
- First posted on arXiv 16 Mar 2021.

## Builds on (most cited first, 30 of 33)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Media Forensics and DeepFakes: an overview](https://mlsanity.com/paper/2001.06564/) (2020)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [Automated Fact Checking: Task formulations, methods and future directions](https://mlsanity.com/paper/1806.07687/) (2018)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Explainable Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2011.03870/) (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)
- [This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News](https://mlsanity.com/paper/1703.09398/) (2017)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Fact-Checking Meets Fauxtography: Verifying Claims About Images](https://mlsanity.com/paper/1908.11722/) (2019)
- [Exploiting Multi-domain Visual Information for Fake News Detection](https://mlsanity.com/paper/1908.04472/) (2019)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Unsupervised User Stance Detection on Twitter](https://mlsanity.com/paper/1904.02000/) (2019)
- [What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context](https://mlsanity.com/paper/2005.04518/) (2020)
- [Fully Automated Fact Checking Using External Sources](https://mlsanity.com/paper/1710.00341/) (2017)
- [Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media](https://mlsanity.com/paper/1904.00542/) (2019)
- [A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017](https://mlsanity.com/paper/2005.01803/) (2020)
- [Sampling the News Producers: A Large News and Feature Data Set for the Study of the Complex Media Landscape](https://mlsanity.com/paper/1803.10124/) (2018)
- [A Topic-Agnostic Approach for Identifying Fake News Pages](https://mlsanity.com/paper/1905.00957/) (2019)
- [Tanbih: Get To Know What You Are Reading](https://mlsanity.com/paper/1910.02028/) (2019)
- [A Bayesian approach for predicting the popularity of tweets](https://mlsanity.com/paper/1304.6777/) (2013)
- [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)
- [Efficient video integrity analysis through container characterization](https://mlsanity.com/paper/2101.10795/) (2021)
- [Seminar Users in the Arabic Twitter Sphere](https://mlsanity.com/paper/1707.07276/) (2017)
- [Camera-based Image Forgery Localization using Convolutional Neural Networks](https://mlsanity.com/paper/1808.09714/) (2018)

## Built on by (most cited first, 20 of 20)

- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems](https://mlsanity.com/paper/2401.05778/) (2024)
- [Detecting and Understanding Harmful Memes: A Survey](https://mlsanity.com/paper/2205.04274/) (2022)
- [Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection](https://mlsanity.com/paper/2109.12986/) (2021)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [QCRI at SemEval-2023 Task 3: News Genre, Framing and Persuasion Techniques Detection using Multilingual Models](https://mlsanity.com/paper/2305.03336/) (2023)
- [WhatTheWikiFact: Fact-Checking Claims Against Wikipedia](https://mlsanity.com/paper/2105.00826/) (2021)
- [Yesterday's News: Benchmarking Multi-Dimensional Out-of-Distribution Generalization of Misinformation Detection Models](https://mlsanity.com/paper/2410.18122/) (2024)
- [Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts](https://mlsanity.com/paper/2506.12552/) (2025)
- [MALicious INTent Dataset and Inoculating LLMs for Enhanced Disinformation Detection](https://mlsanity.com/paper/2603.14525/) (2026)
- [Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations](https://mlsanity.com/paper/2205.04402/) (2022)
- [MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2412.10467/) (2024)
- [The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection](https://mlsanity.com/paper/2509.15896/) (2025)
- [AuthorityBench: Benchmarking LLM Authority Perception for Reliable Retrieval-Augmented Generation](https://mlsanity.com/paper/2603.25092/) (2026)
- [No News is Good News: A Critique of the One Billion Word Benchmark](https://mlsanity.com/paper/2110.12609/) (2021)
- [From Veracity to Diffusion: Adressing Operational Challenges in Moving From Fake-News Detection to Information Disorders](https://mlsanity.com/paper/2512.02552/) (2025)
- [The Tag is the Signal: URL-Agnostic Credibility Scoring for Messages on Telegram](https://mlsanity.com/paper/2601.13294/) (2026)
- [A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis](https://mlsanity.com/paper/2605.01336/) (2026)
- [MEDIAREF: A Public Knowledge Store for Media Background Checks](https://mlsanity.com/paper/2607.02383/) (2026)
- [From Articles to Publishers: Aggregating Language Model Predictions for News Source Reliability Inference](https://mlsanity.com/paper/2609.24219/) (2026)

## Abstract (arXiv)

The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically. Thus, many researchers are shifting their attention to higher granularity, aiming to profile entire news outlets, which makes it possible to detect likely "fake news" the moment it is published, by simply checking the reliability of its source. Source factuality is also an important element of systems for automatic fact-checking and "fake news" detection, as they need to assess the reliability of the evidence they retrieve online. Political bias detection, which in the Western political landscape is about predicting left-center-right bias, is an equally important topic, which has experienced a similar shift towards profiling entire news outlets. Moreover, there is a clear connection between the two, as highly biased media are less likely to be factual; yet, the two problems have been addressed separately. In this survey, we review the state of the art on media profiling for factuality and bias, arguing for the need to model them jointly. We further discuss interesting recent advances in using different information sources and modalities, which go beyond the text of the articles the target news outlet has published. Finally, we discuss current challenges and outline future research directions.