Suspicious News Detection Using Micro Blog Text
This work addresses the costly process of manual fact-checking for news verification, though it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of detecting suspicious news articles from microblog text to reduce manual fact-checking costs, showing that basic machine learning models can effectively support this task.
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.