  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.
