Abstract
During the 2011 East Japan Earthquake and Tsunami Disaster, a considerable amount of false information was disseminated on Twitter; for example, after the Cosmo Oil fire, it was rumored that harmful substances will come down with rain. This paper exhaustively extracts pieces of false information from tweets within one week after the earthquake, and analyzes the diffusion of false information and its correction information. By designing a set of linguistic patterns that correct false information, this paper proposes a method for detecting false information. Specifically, the method extracts text passages that match the correction patterns, clusters the passages into topics of false information, and selects, for each topic, a passage explaining the false information most suitably. We report the performance of the proposed method on the data set extracted manually from websites that specialize in collecting false information. In addition, we build a system that visualizes emergences, diffusions, and terminations of a piece of false information and its correction. We also propose a method for discriminating false information from its correction, and discuss the possibility of alerting against false information.
