Hashtag Recommendation For Hyperlinked Tweets

Presence of hyperlink in a tweet is a strong indication of tweet being more informative. In this paper, we study the problem of hashtag recommendation for hyperlinked tweets (i.e., tweets containing links to Web pages). By recommending hashtags to hyperlinked tweets, we argue that the functions of hashtags such as providing the right context to interpret the tweets, tweet categorization, and tweet promotion, can be extended to the linked documents. The proposed solution for hashtag recommendation consists of two phases. In the first phase, we select candidate hashtags through five schemes by considering the similar tweets, the similar documents, the named entities contained in the document, and the domain of the link In the second phase, we formulate the hashtag recommendation problem as a learning to rank problem and adopt RankSVM to aggregate and rank the candidate hashtags. Our experiments on a collection of 24 million tweets show that the proposed solution achieves promising results.