In the recent years, the reliability of information on the Internet has emerged as a crucial issue of modern society. Social network sites (SNSs) have revolutionized the way in which information is spread by allowing users to freely share content. As a consequence, SNSs are also increasingly used as vectors for the diffusion of misinformation and hoaxes. The amount of disseminated information and the rapidity of its diffusion make it practically impossible to assess reliability in a timely manner, highlighting the need for automatic online hoax detection systems. As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who “liked” them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts. We further show that our techniques are robust: they work even when we restrict our attention to the users who like both hoax and non-hoax posts. These results suggest that mapping the diffusion pattern of information can be a useful component of automatic hoax detection systems.

Tacchini, E., Ballarin, G., Della Vedova, M. L., Moret, S., De Alfaro, L., Some like it Hoax: Automated fake news detection in social networks, Paper, in CEUR Workshop Proceedings, (Skopje, 18-18 September 2017), CEUR-WS, AACHEN -- DEU 2017: 1-15 [http://hdl.handle.net/10807/116519]

Some like it Hoax: Automated fake news detection in social networks

Tacchini, Eugenio;Della Vedova, Marco Luigi;
2017

Abstract

In the recent years, the reliability of information on the Internet has emerged as a crucial issue of modern society. Social network sites (SNSs) have revolutionized the way in which information is spread by allowing users to freely share content. As a consequence, SNSs are also increasingly used as vectors for the diffusion of misinformation and hoaxes. The amount of disseminated information and the rapidity of its diffusion make it practically impossible to assess reliability in a timely manner, highlighting the need for automatic online hoax detection systems. As a contribution towards this objective, we show that Facebook posts can be classified with high accuracy as hoaxes or non-hoaxes on the basis of the users who “liked” them. We present two classification techniques, one based on logistic regression, the other on a novel adaptation of boolean crowdsourcing algorithms. On a dataset consisting of 15,500 Facebook posts and 909,236 users, we obtain classification accuracies exceeding 99% even when the training set contains less than 1% of the posts. We further show that our techniques are robust: they work even when we restrict our attention to the users who like both hoax and non-hoax posts. These results suggest that mapping the diffusion pattern of information can be a useful component of automatic hoax detection systems.
2017
Inglese
CEUR Workshop Proceedings
2nd Workshop on Data Science for Social Good, SoGood 2017
Skopje
Paper
18-set-2017
18-set-2017
CEUR-WS
Tacchini, E., Ballarin, G., Della Vedova, M. L., Moret, S., De Alfaro, L., Some like it Hoax: Automated fake news detection in social networks, Paper, in CEUR Workshop Proceedings, (Skopje, 18-18 September 2017), CEUR-WS, AACHEN -- DEU 2017: 1-15 [http://hdl.handle.net/10807/116519]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/116519
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