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 difficult 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.
- Mendoza, B. Poblete, and C. Castillo. Twitter under crisis: can we trust what we RT? In Proceedings of the First Workshop on Social Media Analytics -SOMA '10, pages 71--79. ACM Press, July 2010. Google ScholarDigital Library
- Miyabe, E. Aramaki, and A. Miura.Use trend analysis of twitter after the great east japan earthquake. In Proceedings of SIG-DPS/GN 2011-DPS-148/2011-GN-81/2011-EIP-53, 2011.Google Scholar
- Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th internationalconference on World wide web, WWW '10, pages 851--860. ACM, 2010. Google ScholarDigital Library
- jio Toriumi, Takeshi Sakaki, Kosuke Shinoda, Kazuhiro Kazama, Satoshi Kurihara, and Itsuki Noda.Information Sharing on Twitter During the 2011 Catastrophic Earthquake. 2nd International Workshop on Social Web for Disaster Management (swdm2013) WWW 2013 Companion Publication pp.1025--1028 Google ScholarDigital Library
- rcía-Silva, A., Kang, J. H., Lerman, K., and Corcho, O. Characterising emergent semantics in twitter lists. In The Semantic Web: Research and Applications (pp. 530--544). Springer Berlin Heidelberg, 2012. Google ScholarDigital Library
- O. Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith. From Tweets to Polls : Linking Text Sentiment to Public Opinion Time Series. Most, pages 122--129, 2010.Google Scholar
- O. Connor, Krieger, M. , Ahn, D. Tweetmotif: Exploratory search and topic summarization for twitter, Proceedings of ICWSM, pp. 2--3 (2010)Google Scholar
- Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting Elections with Twitter : What 140 Characters Reveal about Political Sentiment. Word Journal Of The International Linguistic Association, pages 178--185, 2010.Google Scholar
- sa, K. D. , Shah, R. , Lin, B. , Gershman, A. , and Frederking, R. Topical clustering of tweets, in Pro-ceedings of SIGIR Workshop on Social Web Search and Mining (2011)Google Scholar
- Wasserman and K. Faust.Social network analysis: Methods and applications. In Structural Analysis in the Social Sciences, volume 8, pages 299--302. Cambridge University Press, 1994.Google Scholar
- all HENRY. Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for information Science, 24(4), 265--269, 1973.Google Scholar
- B. Frakes and R. Baeza-Yates.Information Retrieval: Data Structures and Algorithms. Prentice Hall PTR, 1992. Google ScholarDigital Library
- auset, A., Newman, M. E., and Moore, C. Finding community structure in very large networks, Physical review E, Vol. 70, No. 6, p. 066111 (2004)Google Scholar
- Salton, A.Wong, C.S.Yang. A Vector Space Model for Automatic Indexing(1975)Google Scholar
- an Ramos: Using TF-IDF to Determine Word Relevance in Document Queries(2003)Google Scholar
Index Terms
- Classification Method for Shared Information on Twitter Without Text Data
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