# Check-It: A Plugin for Detecting and Reducing the Spread of Fake News and Misinformation on the Web

arXiv: https://arxiv.org/abs/1905.04260 · HTML: https://mlsanity.com/paper/1905.04260/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

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

- [Adapting Fake News Detection to the Era of Large Language Models](https://mlsanity.com/paper/2311.04917/) (2023)
- [The Rise and Fall of Fake News sites: A Traffic Analysis](https://mlsanity.com/paper/2103.09258/) (2021)
- [FNDaaS: Content-agnostic Detection of Fake News sites](https://mlsanity.com/paper/2212.06492/) (2022)
- [An exploration of features to improve the generalisability of fake news detection models](https://mlsanity.com/paper/2502.20299/) (2025)
- [FakeZero: Real-Time, Privacy-Preserving Misinformation Detection for Facebook and X](https://mlsanity.com/paper/2510.25932/) (2025)
- [A Systematic Review on the Detection of Fake News Articles](https://mlsanity.com/paper/2110.11240/) (2021)

## Abstract (arXiv)

Over the past few years, we have been witnessing the rise of misinformation on the Web. People fall victims of fake news during their daily lives and assist their further propagation knowingly and inadvertently. There have been many initiatives that are trying to mitigate the damage caused by fake news, focusing on signals from either domain flag-lists, online social networks or artificial intelligence. In this work, we present Check-It, a system that combines, in an intelligent way, a variety of signals into a pipeline for fake news identification. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection, respecting the user's privacy. Experimental results show that Check-It is able to outperform the state-of-the-art methods. On a dataset, consisting of 9 millions of articles labeled as fake and real, Check-It obtains classification accuracies that exceed 99%.