Automatic Online Fake News Detection Combining Content and Social Signals

Marco Luigi Della Vedova, Eugenio Tacchini, Stefano Moret, Gabriele Ballarin, Massimo Dipierro, Luca De Alfaro

Risultato della ricerca: Contributo in libroContributo a convegno

38 Citazioni (Scopus)

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%.
Lingua originaleEnglish
Titolo della pubblicazione ospite2018 22nd Conference of Open Innovations Association (FRUCT)
Pagine272-279
Numero di pagine8
DOI
Stato di pubblicazionePubblicato - 2018
Evento2018 22nd Conference of Open Innovations Association (FRUCT) - Jyvaskyla, Finland
Durata: 15 mag 201818 mag 2018

Convegno

Convegno2018 22nd Conference of Open Innovations Association (FRUCT)
CittàJyvaskyla, Finland
Periodo15/5/1818/5/18

Keywords

  • Facebook , Twitter , Context modeling , Training , Logistics , Crowdsourcing

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