User-IBTM: An Online Framework for Hashtag Suggestion in Twitter

Abstract

Twitter, the global social networking microblogging service, allows registered users to post 140-character messages known as tweets. People use the hashtag symbol ‘#’ before a relevant keyword or phrase in their tweets to categorize the tweets and help them show more easily in a Twitter search. However, there are very few tweets contain hashtags, which impedes the quality of the search results and their applications. Therefore, how to automatically generate or recommend hashtags has become a particularly important academic research problem. Although many attempts have been made for solving this problem, previous methods mostly do not take the dynamic nature of hashtags into consideration. Furthermore, most previous work focuses on exploiting the similarity between tweets and ignores semantics in tweets.

In this paper, we regard hashtags as the underlying topics of the tweets. We first introduce an effective method for discovering the latent topics of streaming tweets, which uses the recently proposed incremental biterm topic model (IBTM). Then considering the personalized preferences, we propose a novel model, namely online Twitter-User LDA, to learn each Twitter user’s dynamic interests. Finally, we design an online hashtag suggestion framework called User-IBTM by combining the online Twitter-User LDA and IBTM. As shown in the experimental results on real world data from Twitter, our designed framework outperforms several state-of-the-art methods and achieves satisfying performance (Code is available at https://github.com/worldcodingNow/UserIBTM/tree/master).