Challenge: a new study examines the factuality of news media and its biases . social media has democratized content creation and spread information online .
Approach: They propose to characterize entire news media to predict factuality and bias . they experiment with news websites and a set of features derived from their content .
Outcome: The proposed model shows that the features of news websites perform better than baseline . the results show that the feature types are important for fact-checking systems .

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A Survey on Predicting the Factuality and the Bias of News Media (2024.findings-acl)

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Challenge: a growing number of scholars are profiling entire news outlets to profile fake content . political bias detection is also an important topic, but the two problems have been addressed separately .
Approach: They argue that media profiling should be based on factuality and bias together . they argue that it is difficult to fact-check every single suspicious claim or article manually .
Outcome: 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.
Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts (2025.findings-acl)

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Challenge: Important efforts to characterize news media outlets in terms of their political bias and factuality are labor-intensive and prone to human biases.
Approach: They propose a method that emulates criteria used by professional fact-checkers to assess the factuality and political bias of an entire outlet.
Outcome: The proposed method improves on baselines and with multiple LLMs.
In Plain Sight: Media Bias Through the Lens of Factual Reporting (D19-1)

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Challenge: lexical bias stems from content realization, or how things are said, but other forms of bias stem from content selection and organization.
Approach: They use a dataset to analyze news articles annotated with 1,727 bias spans to investigate informational bias.
Outcome: The proposed model shows that informational bias appears more frequently than lexical bias.
What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context (2020.acl-main)

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Challenge: a growing number of fake news reports are published online, causing a trust crisis . a new study aims to predict political bias and factuality of reporting of entire news outlets .
Approach: They propose to profile entire news outlets and look for those that are likely to publish fake content . they also examine what was written about the target medium and who reads it .
Outcome: The proposed method improves on the current state-of-the-art in analyzing social media and what was written about the target medium.
GREENER: Graph Neural Networks for News Media Profiling (2022.emnlp-main)

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Challenge: a new method for profiling news media on the Web addresses the factuality of reporting and bias problem . a recent study has focused on text features but has focused primarily on text .
Approach: They propose a model that models the similarity between media outlets based on their audience overlap . they propose GREENER, which builds a graph of inter-media connections based upon audience overlap.
Outcome: The proposed model improves on state-of-the-art models on two datasets.
Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing (2020.lrec-1)

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Challenge: a lack of publicly available news bias datasets has hindered efforts to detect subtle biases in news articles.
Approach: They propose a news bias dataset which contains sentences with bias labels . they propose to use the dataset to develop and evaluate methods for detecting news bias .
Outcome: The proposed dataset can be used for analyzing news bias and for developing and evaluating methods for news bias detection.
We Can Detect Your Bias: Predicting the Political Ideology of News Articles (2020.emnlp-main)

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Challenge: a new study examines the role of media in predicting political ideology or bias in news articles . systematic exposure to bias in the news can foster intolerance and ideological segregation .
Approach: They propose an adversarial media adaptation and a specially adapted triplet loss for predicting political ideology in news articles.
Outcome: The proposed model improves over state-of-the-art models in this challenging setup.
An Interactive Framework for Profiling News Media Sources (2024.naacl-long)

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Challenge: Existing tools for detecting fake news are difficult for automated systems . e.g., we focus on the source level, and ask: Is this source factual or politically biased?
Approach: They propose an interactive framework for news media profiling that uses graphs and pre-trained large language models to characterize social context on social media.
Outcome: The proposed framework can detect fake and biased news media with as little as 5 human interactions . it can scale better, as often sources publish have same factuality/political bias as source .
SAFARI: Cross-lingual Bias and Factuality Detection in News Media and News Articles (2024.findings-emnlp)

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Challenge: a new corpus of news media and articles is developed to assess political bias and factuality in cross-lingual contexts . integrity and objectivity of news are crucial in an age of information sharing across cultural and language landscapes - a recent study shows .
Approach: They propose a corpus of news media and articles for predicting political bias and factuality . they evaluate the cross-lingual ability of the models; however, they evaluate on English data .
Outcome: The proposed corpus is unprecedented in its collection and evaluates on English data.
MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media (2025.naacl-long)

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Challenge: Existing methods for profiling news media focus on textual features, causing them to overlook complex relationships between entities.
Approach: They propose a framework for profiling news media from the lens of political bias and factuality.
Outcome: The proposed framework improves existing models and improves them by integrating structural information from similar nodes.

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