# Identifying Fake News from Twitter Sharing Data: A Large-Scale Study

## TL;DR

This study evaluates reputation algorithms on Twitter data, showing that simple crowdsourcing methods effectively identify fake news with low false positives, supporting large-scale misinformation detection.

## Contribution

It demonstrates that straightforward crowdsourcing algorithms can reliably detect fake news on Twitter with minimal false positives, offering a practical solution.

## Key findings

- Crowdsourcing algorithms identify a large portion of fake news.
- Low false positive rates for mainstream websites.
- Potential for large-scale fake news detection systems.

## Abstract

Social networks offer a ready channel for fake and misleading news to spread and exert influence. This paper examines the performance of different reputation algorithms when applied to a large and statistically significant portion of the news that are spread via Twitter. Our main result is that simple crowdsourcing-based algorithms are able to identify a large portion of fake or misleading news, while incurring only very low false positive rates for mainstream websites. We believe that these algorithms can be used as the basis of practical, large-scale systems for indicating to consumers which news sites deserve careful scrutiny and skepticism.