# Automatically Identifying Fake News in Popular Twitter Threads

arXiv: https://arxiv.org/abs/1705.01613 · HTML: https://mlsanity.com/paper/1705.01613/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2017.

## Built on by (most cited first, 18 of 18)

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- [The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence](https://mlsanity.com/paper/2601.05478/) (2026)
- [A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma](https://mlsanity.com/paper/2101.00480/) (2021)
- [Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twitter](https://mlsanity.com/paper/2101.05626/) (2021)
- [Automated Fake News Detection using cross-checking with reliable sources](https://mlsanity.com/paper/2201.00083/) (2022)
- [Advanced Text Analytics -- Graph Neural Network for Fake News Detection in Social Media](https://mlsanity.com/paper/2502.16157/) (2025)

## Abstract (arXiv)

Information quality in social media is an increasingly important issue, but web-scale data hinders experts' ability to assess and correct much of the inaccurate content, or `fake news,' present in these platforms. This paper develops a method for automating fake news detection on Twitter by learning to predict accuracy assessments in two credibility-focused Twitter datasets: CREDBANK, a crowdsourced dataset of accuracy assessments for events in Twitter, and PHEME, a dataset of potential rumors in Twitter and journalistic assessments of their accuracies. We apply this method to Twitter content sourced from BuzzFeed's fake news dataset and show models trained against crowdsourced workers outperform models based on journalists' assessment and models trained on a pooled dataset of both crowdsourced workers and journalists. All three datasets, aligned into a uniform format, are also publicly available. A feature analysis then identifies features that are most predictive for crowdsourced and journalistic accuracy assessments, results of which are consistent with prior work. We close with a discussion contrasting accuracy and credibility and why models of non-experts outperform models of journalists for fake news detection in Twitter.