Context (archaeology)

World Wide Web

Biology

The Internet

Hoax

Computer science

Internet privacy

Paleontology

Content (measure theory)

Alternative medicine

Mathematics

Social media

Mathematical analysis

Artificial intelligence

Fake news

Information retrieval

Medicine

User-generated content

Pathology

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%.

Automatic Online Fake News Detection Combining Content and Social Signals