A Fact-checking Assistant System for Textual Documents*

This paper proposes a system for identifying which parts of textual documents the editors should do fact-checking. Using our system, we can reduce editors’ time and efforts by identifying descriptions that need fact-checking. To accomplish this purpose, we construct a machine-learning-based classifier of sentences, which classifies a part of documents into four classes: according to the necessity of fact-checking. We assume that there are typical descriptions which contain misinformation. Therefore, if we collect the documents and their revised documents, and labels whether their revisions are corrections or not, we can construct the classifier by learning the dataset. To construct this classifier, we build a dataset that includes a set of sentences which are revised more than once, from Wikipedia edit history. The labels indicate the degree of sentence corrections by editors. We develop a Web-based system for demonstrating our proposed approach. When we input texts, the system predicts which parts of the texts the editors should re-confirm the facts.