Name
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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Social and Information Networks | Journal |
Volume | Citations | PageRank |
abs/1902.07207 | 0 | 0.34 |
References | Authors | |
12 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rakshit Agrawal | 1 | 5 | 7.44 |
| Luca de Alfaro | 2 | 2693 | 176.95 |
| Gabriele Ballarin | 3 | 20 | 1.74 |
| Stefano Moret | 4 | 20 | 1.74 |
| Massimo Di Pierro | 5 | 1 | 1.02 |
| Eugenio Tacchini | 6 | 20 | 1.74 |
| Marco L. Della Vedova | 7 | 66 | 8.61 |