Identifying Fake News from Twitter Sharing Data: A Large-Scale Study
Social and Information Networks
2019-02-20 v1 Machine Learning
Machine Learning
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.
Cite
@article{arxiv.1902.07207,
title = {Identifying Fake News from Twitter Sharing Data: A Large-Scale Study},
author = {Rakshit Agrawal and Luca de Alfaro and Gabriele Ballarin and Stefano Moret and Massimo Di Pierro and Eugenio Tacchini and Marco L. Della Vedova},
journal= {arXiv preprint arXiv:1902.07207},
year = {2019}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1802.08066