학술논문
Automatic Online Fake News Detection Combining Content and Social Signals
Document Type
Conference
Author
Source
2018 22nd Conference of Open Innovations Association (FRUCT) Open Innovations Association (FRUCT), 2018 22nd Conference of. :272-279 May, 2018
Subject
Language
ISSN
2305-7254
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%.