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.