Suspicious News Detection Using Micro Blog Text

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Authors Atsuo Fujimura, Tsubasa Tagami, Kazuaki Hanawa, Kentaro Inui, Hiroki Ouchi, Akinori Machino, Hiroki Asano, Ryo Yamashita, Atsushi Komiya, Kaito Suzuki, Kaori Uchiyama, Hitofumi Yanai
Journal/Conference Name Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018
Paper Category
Paper Abstract 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.
Date of publication 2018
Code Programming Language Unspecified

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