• TOMOYA NORO Department of Computer Science, Tokyo Institute of Technology Meguro, Tokyo, 152-8552, Japan
  • TAKEHIRO TOKUDA Department of Computer Science, Tokyo Institute of Technology Meguro, Tokyo, 152-8552, Japan


Social media, Twitter, social network analysis, search, graph-based approach


Twitter is one of the largest social media. Although it can be used to get information on a topic of interest, it is not easy for us to find tweets relevant to the topic due to a massive amount of tweets and the small size of each tweet. Some relevant tweets may not include any terms explicitly related to the topic, and general content-based keyword search techniques and query expansion techniques are not effective for finding such relevant tweets. To solve this problem, we present a method for finding tweets on a topic of interest based on the Twitter user activities related to the topic such as tweet, retweet, and reply. The method consists of two phases: the preparation phase and the main phase. In the preparation phase, we create a user-tweet reference graph representing the relation between users and tweets based on the past user activities related to the topic, calculate the influence of each user and tweet in the topic, then define two types of each user’s power, called “Voice” and “Impact”, indicating “how much voice the user has on the topic” and “how much impact the user has on the other users’ tweets on the topic”. In the main phase, we calculate the relevance of newly-arrived tweets to the topic according to the Voice and the Impact score of the users who posted, retweeted, or replied to each of the tweets, then rank the tweets by the relevance score. The two phases are processed independently. Once the preparation phase is completed, the main phase can return the final result any time. Experimental results show that “who retweeted or replied to the tweet” is more effective for judging the relevance of each tweet to the topic than “who posted the tweet”, and our method can find relevant tweets which do not include any terms explicitly related to the topic. We compare our method with an indegree-based method and a PageRank-based method, and show that our method outperforms the methods compared.



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