2020/07/28 by Taichi Murayama, Murayama, Taichi, Shoko Wakamiya +3
Computer Science · Social Sciences · #Advanced Text Analysis Techniques #Computers and Society (cs.CY) #FOS: Computer and information sciences #Misinformation and Its Impacts #Spam and Phishing Detection #cs.CY
paper · pdf · doi:10.48550/arxiv.2007.14083
5pages, 2 figures
arxiv created 2020/07/28 · openalex publication_date 2020/07/28 · arxiv updated 2020/07/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Large numbers of people use Social Networking Services (SNS) for easy access to various news, but they have more opportunities to obtain and share ``fake news'' carrying false information. Partially to combat fake news, several fact-checking sites such as Snopes and PolitiFact have been founded. Nevertheless, these sites rely on time-consuming and labor-intensive tasks. Moreover, their available languages are not extensive. To address these difficulties, we propose a new fake news collection system based on rule-based (unsupervised) frameworks that can be extended easily for various languages. The system collects news with high probability of being fake by debunking tweets by users and presents event clusters gathering higher attention. Our system currently functions in two languages: English and Japanese. It shows event clusters, 65% of which are actually fake. In future studies, it will be applied to other languages and will be published with a large fake news dataset.