Mining false information on Twitter for a major disaster situation

Keita Nabeshima, Junta Mizuno, Naoaki Okazaki, Kentaro Inui

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


Social networking services (SNS), such as Twitter, disseminate not only useful information, but also false information. Identifying this false information is crucial in order to keep the information on a SNS reliable. The aim of this paper is to develop a method of extracting false information from among a large collection of tweets. We do so by using a set of linguistic patterns formulated to correct false information. More specifically, the proposed method extracts text passages that match specified correction patterns, clusters the passages into topics of false information, and selects a passage that represents each topic of false information. In the experiment we conduct, we build an evaluation set manually, and demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationActive Media Technology - 10th International Conference, AMT 2014, Proceedings
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783319099118
Publication statusPublished - 2014
Event10th International Conference on Active Media Technology, AMT 2014 - Warsaw, Poland
Duration: 2014 Aug 112014 Aug 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8610 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th International Conference on Active Media Technology, AMT 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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