# A Topic-Agnostic Approach for Identifying Fake News Pages

arXiv: https://arxiv.org/abs/1905.00957 · HTML: https://mlsanity.com/paper/1905.00957/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2019.

## Built on by (most cited first, 17 of 17)

- [Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data](https://mlsanity.com/paper/2102.06314/) (2021)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [FakeFlow: Fake News Detection by Modeling the Flow of Affective Information](https://mlsanity.com/paper/2101.09810/) (2021)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [Adapting Fake News Detection to the Era of Large Language Models](https://mlsanity.com/paper/2311.04917/) (2023)
- ["This is Fake! Shared it by Mistake": Assessing the Intent of Fake News Spreaders](https://mlsanity.com/paper/2202.04752/) (2022)
- [Datavoidant: An AI System for Addressing Political Data Voids on Social Media](https://mlsanity.com/paper/2210.13594/) (2022)
- [GREENER: Graph Neural Networks for News Media Profiling](https://mlsanity.com/paper/2211.05533/) (2022)
- [A Macro- and Micro-Hierarchical Transfer Learning Framework for Cross-Domain Fake News Detection](https://mlsanity.com/paper/2502.14403/) (2025)
- [Bridging Social Media and Search Engines: Dredge Words and the Detection of Unreliable Domains](https://mlsanity.com/paper/2406.11423/) (2024)
- [MGM: Global Understanding of Audience Overlap Graphs for Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2412.10467/) (2024)
- [Consistent and Invariant Generalization Learning for Short-video Misinformation Detection](https://mlsanity.com/paper/2507.04061/) (2025)
- [An exploration of features to improve the generalisability of fake news detection models](https://mlsanity.com/paper/2502.20299/) (2025)
- [Reliable News or Propagandist News? A Neurosymbolic Model Using Genre, Topic, and Persuasion Techniques to Improve Robustness in Classification](https://mlsanity.com/paper/2604.01936/) (2026)
- [Tell Me Who Your Friends Are: Using Content Sharing Behavior for News Source Veracity Detection](https://mlsanity.com/paper/2101.10973/) (2021)
- [Cross-Domain Fake News Detection on Unseen Domains via LLM-Based Domain-Aware User Modeling](https://mlsanity.com/paper/2602.01726/) (2026)
- [A Multi-View Media Profiling Suite: Resources, Evaluation, and Analysis](https://mlsanity.com/paper/2605.01336/) (2026)

## Abstract (arXiv)

Fake news and misinformation have been increasingly used to manipulate popular opinion and influence political processes. To better understand fake news, how they are propagated, and how to counter their effect, it is necessary to first identify them. Recently, approaches have been proposed to automatically classify articles as fake based on their content. An important challenge for these approaches comes from the dynamic nature of news: as new political events are covered, topics and discourse constantly change and thus, a classifier trained using content from articles published at a given time is likely to become ineffective in the future. To address this challenge, we propose a topic-agnostic (TAG) classification strategy that uses linguistic and web-markup features to identify fake news pages. We report experimental results using multiple data sets which show that our approach attains high accuracy in the identification of fake news, even as topics evolve over time.