Suspicious News Detection Using Micro Blog Text
Computation and Language
2018-10-30 v1
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.
Cite
@article{arxiv.1810.11663,
title = {Suspicious News Detection Using Micro Blog Text},
author = {Tsubasa Tagami and Hiroki Ouchi and Hiroki Asano and Kazuaki Hanawa and Kaori Uchiyama and Kaito Suzuki and Kentaro Inui and Atsushi Komiya and Atsuo Fujimura and Hitofumi Yanai and Ryo Yamashita and Akinori Machino},
journal= {arXiv preprint arXiv:1810.11663},
year = {2018}
}
Comments
10 pages; PACLIC 2018