# Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions

arXiv: https://arxiv.org/abs/2410.17655 · HTML: https://mlsanity.com/paper/2410.17655/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://mlsanity.com/paper/2305.06984/) (2023)
- [Predicting Factuality of Reporting and Bias of News Media Sources](https://mlsanity.com/paper/1810.01765/) (2018)
- [What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context](https://mlsanity.com/paper/2005.04518/) (2020)
- [Bias of AI-Generated Content: An Examination of News Produced by Large Language Models](https://mlsanity.com/paper/2309.09825/) (2023)
- [Multi-Task Ordinal Regression for Jointly Predicting the Trustworthiness and the Leading Political Ideology of News Media](https://mlsanity.com/paper/1904.00542/) (2019)
- [Introducing MBIB -- the first Media Bias Identification Benchmark Task and Dataset Collection](https://mlsanity.com/paper/2304.13148/) (2023)
- [Reliability Estimation of News Media Sources: Birds of a Feather Flock Together](https://mlsanity.com/paper/2404.09565/) (2024)
- [GREENER: Graph Neural Networks for News Media Profiling](https://mlsanity.com/paper/2211.05533/) (2022)
- [Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web](https://mlsanity.com/paper/1809.00494/) (2018)
- [Quantifying Political Bias in News Articles](https://mlsanity.com/paper/2210.03404/) (2022)
- [Predicting the Factuality of Reporting of News Media Using Observations About User Attention in Their YouTube Channels](https://mlsanity.com/paper/2108.12519/) (2021)

## Built on by (most cited first, 2 of 2)

- [How Similar Are Grokipedia and Wikipedia? A Multi-Dimensional Textual and Structural Comparison](https://mlsanity.com/paper/2510.26899/) (2025)
- [Can temporal article-level credibility signals improve domain-level credibility prediction?](https://mlsanity.com/paper/2607.04560/) (2026)

## Abstract (arXiv)

Bias assessment of news sources is paramount for professionals, organizations, and researchers who rely on truthful evidence for information gathering and reporting. While certain bias indicators are discernible from content analysis, descriptors like political bias and fake news pose greater challenges. In this paper, we propose an extension to a recently presented news media reliability estimation method that focuses on modeling outlets and their longitudinal web interactions. Concretely, we assess the classification performance of four reinforcement learning strategies on a large news media hyperlink graph. Our experiments, targeting two challenging bias descriptors, factual reporting and political bias, showed a significant performance improvement at the source media level. Additionally, we validate our methods on the CLEF 2023 CheckThat! Lab challenge, outperforming the reported results in both, F1-score and the official MAE metric. Furthermore, we contribute by releasing the largest annotated dataset of news source media, categorized with factual reporting and political bias labels. Our findings suggest that profiling news media sources based on their hyperlink interactions over time is feasible, offering a bird's-eye view of evolving media landscapes.