In microblogging services such as Twitter, users can choose whose posts they want to read by "following" other user accounts. Twitter users often have large social networks, thus many of them are overwhelmed with managing their network connections and dealing with information overload. We want to address this problem by automatically dividing the social network of a Twitter user into personal cliques, and annotating each clique with keywords to identify the common ground of a clique. Our proposed clique annotation method extracts keywords from the tweet history of the clique members and individually weights the extracted keywords of each clique member according to the relevance of their tweets for the clique. The keyword weight is influenced by two factors. The first factor is calculated based on the number of connections of a user within the clique, and the second factor depends on whether the user mainly publishes personal information or information of general interest. In an experiment, on average 36.25% of the keywords extracted from our proposed method were relevant for the cliques, as opposed to 31.78% for the baseline method, which does not weight keywords but only calculates term frequency. When we annotated only cliques formed around common interests, such as "baseball", our proposed method even extracted 50.67% of relevant keywords, as opposed to 42% for the baseline method. These results clearly indicate that our approach can improve clique annotation in social networks.
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