Mining False Information on Twitter for a Major Disaster Situation

  • Keita Nabeshima
  • Junta Mizuno
  • Naoaki Okazaki
  • Kentaro Inui
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8610)


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.


Noun Phrase Social Networking Service False Information Correction Word Great East Japan Earthquake 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684 (2011)Google Scholar
  2. 2.
    Ennals, R., Trushkowsky, B., Agosta, J.M.: Highlighting disputed claims on the web. In: Proceedings of the 19th International Conference on World Wide Web, pp. 341–350 (2010)Google Scholar
  3. 3.
    Giampiccolo, D., Magnini, B., Dagan, I., Dolan, B.: The third pascal recognizing textual entailment challenge. In: Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing, pp. 1–9 (2007)Google Scholar
  4. 4.
    Harabagiu, S., Hickl, A., Lacatusu, F.: Negation, contrast and contradiction in text processing. In: Proceedings of the 21st National Conference on Artificial Intelligence, pp. 755–762 (2006)Google Scholar
  5. 5.
    Hsu, C.F., Khabiri, E., Caverlee, J.: Ranking comments on the social web. In: International Conference on Computational Science and Engineering, CSE 2009, vol. 4, pp. 90–97 (2009)Google Scholar
  6. 6.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46(5), 604–632 (1999)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Lex, E., Voelske, M., Errecalde, M., Ferretti, E., Cagnina, L., Horn, C., Stein, B., Granitzer, M.: Measuring the quality of web content using factual information. In: Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality, pp. 7–10 (2012)Google Scholar
  8. 8.
    MacCartney, B., Galley, M., Manning, C.D.: A phrase-based alignment model for natural language inference. In: Proceedings of 2008 Conference on Empirical Methods in Natural Language Processing, pp. 802–811 (2008)Google Scholar
  9. 9.
    de Marneffe, M.C., Rafferty, A.N., Manning, C.D.: Finding contradictions in text. In: Proceedings of ACL 2008: HLT, pp. 1039–1047 (2008)Google Scholar
  10. 10.
    de Marneffe, M.C., Rafferty, A.R., Manning, C.D.: Identifying Conflicting Information in Texts. In: Handbook of Natural Language Processing and Machine Translation: DARPA Global Autonomous Language Exploitation (2011)Google Scholar
  11. 11.
    Pasternack, J., Roth, D.: Making better informed trust decisions with generalized fact-finding. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, pp. 2324–2329 (2011)Google Scholar
  12. 12.
    Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: Identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599 (2011)Google Scholar
  13. 13.
    Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Patil, S., Flammini, A., Menczer, F.: Truthy: Mapping the spread of astroturf in microblog streams. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 249–252 (2011)Google Scholar
  14. 14.
    Ritter, A., Soderland, S., Downey, D., Etzioni, O.: It’s a contradiction – no, it’s not: A case study using functional relations. In: Proceedings of 2008 Conference on Empirical Methods in Natural Language Processing, pp. 11–20 (2008)Google Scholar
  15. 15.
    Watanabe, Y., Miyao, Y., Mizuno, J., Shibata, T., Kanayama, H., Lee, C.W., Lin, C.J., Shi, S., Mitamura, T., Kando, N., Shima, H., Takeda, K.: Overview of the recognizing inference in text (RITE-2) at NTCIR-10. In: Proceedings of the NTCIR-10 Conference, pp. 385–404 (2013)Google Scholar
  16. 16.
    Watanabe, Y., Mizuno, J., Inui, K.: THK’s natural logic-based compositional textual entailment model at NTCIR-10 RITE-2. In: Proceedings of the NTCIR-10 Conference, pp. 531–536 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Keita Nabeshima
    • 1
  • Junta Mizuno
    • 2
  • Naoaki Okazaki
    • 1
    • 3
  • Kentaro Inui
    • 1
  1. 1.Graduate School of Information SciencesTohoku University / MiyagiJapan
  2. 2.Resilient ICT Research CenterNICT / MiyagiJapan
  3. 3.Japan Science and Technology Agency (JST) / TokyoJapan

Personalised recommendations