2018/10/27 by Tsubasa Tagami, Tagami, Tsubasa, Hiroki Ouchi +21
Computer Science · Social Sciences · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Misinformation and Its Impacts #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.1810.11663
10 pages; PACLIC 2018
arxiv created 2018/10/27 · openalex publication_date 2018/10/27 · arxiv updated 2018/10/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a new task, suspicious news detection using micro blog text. This task aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the articles. Specifically, in this task, given a set of posts on SNS referring to a news article, the goal is to judge whether the article is to be verified or not. For this task, we create a publicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce the cost of manual fact-checking process.