BUTE: bursty users tagging method estimated by time series data

Many Twitter users post tweets that are related to their particular interests. Users can also collect information by following other users. One approach clarifies user interests by tagging labels based on the users. A user tagging method is important to discover candidate users with similar interests. Typical approaches estimate user interests with terms in tweets and by applying graph theory such as following networks. In contrast, we propose a new user tagging method using the posting time series data of the number of tweets and developed the following hypothesis: Since users have interests, they will post more tweets at the time occurring the events compared with general times. Based on this hypothesis, we extract interests as burst levels from the user and hashtag time series data with Kleinberg's burst enumerating algorithm. We manage the burst levels of users as the term frequency in documents and calculate the hashtag scores for each user by three typical score calculation methods: cosine similarity, Naive Bayes, and TF-IDF. Thus, the proposed method needs no linguistic analysis which requires heavy computational resources. With our sophisticated experimental evaluations with actually active users, we demonstrate the high efficiency of our tagging methods, evaluate them using such information retrieval system evaluation metrics as expected reciprocal rank (ERR) and Q-measure, and clarify the strengths and limitations of each one. Naive Bayes and cosine similarity are especially suitable for user tagging and tag score calculation tasks.