Work

Suspicious News Detection Using Micro Blog Text

Year: 2018

Type: conference-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.

Source: Institutional Repositories DataBase (IRDB)

DOI: 10.48550/arxiv.1810.11663

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

Institutions: Tohoku University, RIKEN

Language: English

Topic: Misinformation and Its Impacts

Subfield: Sociology and Political Science

Field: Social Sciences

Domain: Social Sciences

Open Access status: green

Funder: Japan Society for the Promotion of Science

OpenAlex ID: W2898241237

FWCI: 2.28

Cites: 28

Cited by: 3

Related to: 10