Suspicious news detection using micro blog text

Tsubasa Tagami, Hiroki Ouchi, Hiroki Asano, Kazuaki Hanawa, Kaori Uchiyama, Kaito Suzuki, Kentaro Inui, Atsushi Komiya, Atsuo Fujimura, Hitofumi Yanai, Ryo Yamashita, Akinori MacHino

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

We present a new task, suspicious news detection using micro blog text. This task aimsto support human experts to detect suspiciousnews articles to be verified, which is costly buta crucial step before verifying the truthfulnessof the articles. Specifically, in this task, givena set of posts on SNS referring to a news article, the goal is to judge whether the articleis suspicious or not. For this task, we create apublicly available dataset in Japanese and provide benchmark results by using several basic machine learning techniques. Experimental results show that our models can reduce thecost of manual fact-checking process.

Original languageEnglish
Pages648-657
Number of pages10
Publication statusPublished - 2018
Event32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018 - Hong Kong, Hong Kong
Duration: 2018 Dec 12018 Dec 3

Conference

Conference32nd Pacific Asia Conference on Language, Information and Computation, PACLIC 2018
CountryHong Kong
CityHong Kong
Period18/12/118/12/3

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)

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