# Some Like it Hoax: Automated Fake News Detection in Social Networks

arXiv: https://arxiv.org/abs/1704.07506 · HTML: https://mlsanity.com/paper/1704.07506/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2017.

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

- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [Domain Adaptive Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/2202.08159/) (2022)
- [Multimodal Emergent Fake News Detection via Meta Neural Process Networks](https://mlsanity.com/paper/2106.13711/) (2021)
- [Weakly Supervised Veracity Classification with LLM-Predicted Credibility Signals](https://mlsanity.com/paper/2309.07601/) (2023)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [A Revisit of Fake News Dataset with Augmented Fact-checking by ChatGPT](https://mlsanity.com/paper/2312.11870/) (2023)
- [Fake News Detection: It's All in the Data!](https://mlsanity.com/paper/2407.02122/) (2024)
- [Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection](https://mlsanity.com/paper/2201.02048/) (2022)
- [SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection](https://mlsanity.com/paper/2205.08159/) (2022)
- [Insta-VAX: A Multimodal Benchmark for Anti-Vaccine and Misinformation Posts Detection on Social Media](https://mlsanity.com/paper/2112.08470/) (2021)
- [Fake News Detection Tools and Methods -- A Review](https://mlsanity.com/paper/2112.11185/) (2021)
- [SOK: Fake News Outbreak 2021: Can We Stop the Viral Spread?](https://mlsanity.com/paper/2105.10671/) (2021)
- [Clickbait Detection in YouTube Videos](https://mlsanity.com/paper/2107.12791/) (2021)
- [MINT -- Mainstream and Independent News Text Corpus](https://mlsanity.com/paper/2108.06249/) (2021)
- [Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?](https://mlsanity.com/paper/2111.05139/) (2021)
- [FALSE: Fake News Automatic and Lightweight Solution](https://mlsanity.com/paper/2208.07686/) (2022)
- [Fake News Detection and Behavioral Analysis: Case of COVID-19](https://mlsanity.com/paper/2305.16057/) (2023)
- [DisTrack: a new Tool for Semi-automatic Misinformation Tracking in Online Social Networks](https://mlsanity.com/paper/2408.00633/) (2024)
- [Truth in Text: A Meta-Analysis of ML-Based Cyber Information Influence Detection Approaches](https://mlsanity.com/paper/2503.22686/) (2025)
- [CrediBench: Building Web-Scale Network Datasets for Information Integrity](https://mlsanity.com/paper/2509.23340/) (2025)

## Abstract (arXiv)

In recent years, the reliability of information on the Internet has emerged as a crucial issue of modern society. Social network sites (SNSs) have revolutionized the way in which information is spread by allowing users to freely share content. As a consequence, SNSs are also increasingly used as vectors for the diffusion of misinformation and hoaxes. The amount of disseminated information and the rapidity of its diffusion make it practically impossible to assess reliability in a timely manner, highlighting the need for automatic hoax detection systems. As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who "liked" them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts. We further show that our techniques are robust: they work even when we restrict our attention to the users who like both hoax and non-hoax posts. These results suggest that mapping the diffusion pattern of information can be a useful component of automatic hoax detection systems.