Automatic Online Fake News Detection Combining Content and Social Signals
| Autor: | Marco L. Della Vedova, Eugenio Tacchini, Stefano Moret, Gabriele Ballarin, Massimo DiPierro, Luca de Alfaro |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: | |
| Zdroj: | Proceedings of the XXth Conference of Open Innovations Association FRUCT, Vol 426, Iss 22, Pp 272-279 (2018) |
| Druh dokumentu: | article |
| ISSN: | 2305-7254 2343-0737 |
| DOI: | 10.23919/FRUCT.2018.8468301 |
| Popis: | 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%. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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