Identifying Fake News from Twitter Sharing Data: A Large-Scale Study
This addresses the spread of misinformation on social media for consumers, offering a practical solution, though it appears incremental as it builds on existing reputation algorithms.
The study tackled the problem of identifying fake news on Twitter by evaluating various reputation algorithms on a large dataset, finding that simple crowdsourcing-based methods can detect a significant portion of fake or misleading news with low false positive rates for mainstream sites.
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.