Suggest what to tag: Recommending more precise hashtags based on users’ dynamic interests and streaming tweet content

• Design online Twitter-User LDA to capture Twitter users’ dynamic interests.
• Introduce incremental biterm topic model to discover topic distribution of streaming tweet content.
• Combine tweet content and dynamic user interest to build a personalized hashtag recommendation method: User-IBTM.
• To the best of our knowledge, the proposed User-IBTM is the first method which uses online algorithms and topic models for short texts to recommend hashtags in Twitter.

Twitter is an online social networking microblogging service that allows registered users to broadcast 140-character messages called tweets. The service has gained worldwide popularity since it was created in March 2006, with more than 316 million monthly active users in June 2015 who posted 500 million tweets per day. As the number of available tweets grows, the problem of managing tweets becomes extremely difficult, which could lead to information overload. To avoid this problem, people use the hashtag symbol # before a relevant keyword or phrase in their tweets to categorize those tweets and help them show more easily in each Twitter search. Furthermore, hashtags can be used to collect public opinions on events and their ideas at the individual, community or even the world level. Incorporating hashtags to obtain better performance such as sentiment classification and breaking events detection also has attracted considerable research attention in recent years. However, there are very few tweets containing hashtags, which impedes the quality of search results and their further usage in various applications. Therefore, hashtag recommendation has become a particularly important research problem. In this paper, we first propose a novel model, namely online Twitter-User LDA to learn Twitter users’ dynamic interests. Then considering the shortness, sparsity, and high volume of tweets, we introduce an effective method to discover the latent topics of streaming tweet content, which uses recently proposed incremental biterm topic model (IBTM). We finally design an automatic hashtag recommendation method 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 design method based on dynamic user interests and streaming tweet content significantly outperforms several other baseline methods and can suggest more precise hashtags.