Classification Method for Shared Information on Twitter Without Text Data

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 di cult 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 in uential 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 classi cation 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 veri ed the validity of our proposed classi cation method. Our experiment veri ed that the ratio of the clusters whose tweets are mutually similar in the cluster to all Copyright is held by the International World Wide Web Conference Committee (IW3C2). IW3C2 reserves the right to provide a hyperlink to the author’s site if the Material is used in electronic media. WWW 2015 Companion, May 18–22, 2015, Florence, Italy. ACM 978-1-4503-3473-0/15/05. http://dx.doi.org/10.1145/2740908.2741726. 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 classi ed topic-similar tweets, even if they are not linguistically similar.