1. Automatic Online Fake News Detection Combining Content and Social Signals
- Author
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Marco L. Della Vedova, Massimo DiPierro, Eugenio Tacchini, Stefano Moret, Luca de Alfaro, and Gabriele Ballarin
- Subjects
fake news ,social networks ,Facebook ,Computer science ,Twitter ,Context (language use) ,automatic hoax detection ,02 engineering and technology ,Logistics ,computer.software_genre ,Chatbot ,Facebook , Twitter , Context modeling , Training , Logistics , Crowdsourcing ,lcsh:Telecommunication ,020204 information systems ,lcsh:TK5101-6720 ,0202 electrical engineering, electronic engineering, information engineering ,Context modeling ,Training ,Information retrieval ,business.industry ,Crowdsourcing ,Settore ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI ,machine learning ,Content analysis ,Content (measure theory) ,020201 artificial intelligence & image processing ,The Internet ,Fake news ,business ,computer - Abstract
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%.
- Published
- 2018