Fake News Detection via Biased User Profiles in Social Networking Sites

The spread of fake news on social networking sites has become a problem. Users who share fake news have strong human needs (such as the desire for approval, belonging, and self-expression) and are likely to have characteristic words in their self-descriptions. In this paper, we propose a method for detecting fake news based on the biased words in those self-descriptions. In the proposed method, feature vectors are first created from words in the self-descriptions of multiple users who post the same news URL on Twitter. Subsequently, they are classified into fake or not fake using machine learning. In experiments conducted using multiple datasets, including real and fake news from Japan and the U.S., the proposed method achieved an average classification accuracy of 90.2%. Furthermore, we show that the proposed method is effective for multi-domain fake news detection and analysis of users targeted by fake news in case studies.