Automatic Online Fake News Detection Combining Content and Social Signals

The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems. In the context of social networks, machine learning (ML) methods can be used for this purpose. Fake news detection strategies are traditionally either based on content analysis...

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Bibliographic Details
Main Authors: Della Vedova, Marco L., Tacchini, Eugenio, Moret, Stefano, Ballarin, Gabriele, DiPierro, Massimo, de Alfaro, Luca
Format: Conference Proceeding
Language:English
Subjects:
automatic hoax detection
Context modeling
Crowdsourcing
Facebook
fake news
Logistics
machine learning
social networks
Training
Twitter
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Summary:The proliferation and rapid diffusion of fake news on the Internet highlight the need of automatic hoax detection systems. In the context of social networks, machine learning (ML) methods can be used for this purpose. Fake news detection strategies are traditionally either based on content analysis (i.e. analyzing the content of the news) or - more recently - on social context models, such as mapping the news' diffusion pattern. In this paper, we first propose a novel ML fake news detection method which, by combining news content and social context features, outperforms existing methods in the literature, increasing their already high accuracy by up to 4.8%. Second, we implement our method within a Facebook Messenger chatbot and validate it with a real-world application, obtaining a fake news detection accuracy of 81.7%.
ISSN:2305-7254
2305-7254
2343-0737
DOI:10.23919/FRUCT.2018.8468301