Classification method for shared information on twitter without text data

Seigo Baba, Fujio Toriumi, Takeshi Sakaki, Kosuke Shinoda, Satoshi Kurihara, Kazuhiro Kazama, Itsuki Noda

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

8 Citations (Scopus)

Abstract

During a disaster, appropriate information must be collected. For example, victims and survivors require information about shelter locations and dangerous points or advice about protecting themselves. Rescuers need information about the details of volunteer activities and supplies, especially potential shortages. However, collecting such localized information is dificult from such mass media as TV and newspapers because they generally focus on information aimed at the general public. On the other hand, social media can attract more attention than mass media under these circumstances since they can provide such localized information. In this paper, we focus on Twitter, one of the most influential social media, as a source of local information. By assuming that users who retweet the same tweet are interested in the same topic, we can classify tweets that are required by users with similar interests based on retweets. Thus, we propose a novel tweet classification method that focuses on retweets without text mining. We linked tweets based on retweets to make a retweet network that connects similar tweets and extracted clusters that contain similar tweets from the constructed network by our clustering method. We also subjectively verified the validity of our proposed classification method. Our experiment verified that the ratio of the clusters whose tweets are mutually similar in the cluster to all clusters is very high and the similarities in each cluster are obvious. Finally, we calculated the linguistic similarities of the results to clarify our proposed method's features. Our method classified topic-similar tweets, even if they are not linguistically similar.

Original languageEnglish
Title of host publicationWWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages1173-1178
Number of pages6
ISBN (Electronic)9781450334730
DOIs
Publication statusPublished - 2015 May 18
Externally publishedYes
Event24th International Conference on World Wide Web, WWW 2015 - Florence, Italy
Duration: 2015 May 182015 May 22

Other

Other24th International Conference on World Wide Web, WWW 2015
CountryItaly
CityFlorence
Period15/5/1815/5/22

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Keywords

  • Clustering
  • Data mining
  • Network
  • Social media

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Baba, S., Toriumi, F., Sakaki, T., Shinoda, K., Kurihara, S., Kazama, K., & Noda, I. (2015). Classification method for shared information on twitter without text data. In WWW 2015 Companion - Proceedings of the 24th International Conference on World Wide Web (pp. 1173-1178). Association for Computing Machinery, Inc. https://doi.org/10.1145/2740908.2741726