Detecting Keyphrases in Micro-blogging with Graph Modeling of Information Diffusion

The rapid increasing popularity of micro-blogging has made it an important information seeking channel. Keyphrase extraction is an effective way for summarizing and analyzing micro-blogging content, which can help users gain insights into internet hotspots. Existing methods for keyphrase extraction usually unilaterally consider phrase frequency or user retweet count as key factors. However, those methods may neglect the relationships between different phrases and the importance of user influence to further information diffusion. Generally, phrases shown in the influential users’ micro-blogs are more likely to attract other users’ interest, making them more likely to be diffused in the near future. Besides, phrases may have relations with each other, and some phrases usually have similar diffusion paths and attract the attention of the same population. In this paper, by comprehensively considering all the above mentioned factors to detect micro-blogging keyphrases, we proposed a novel model. The proposed model first detect high frequency term from abundant micro-blogs as candidate keyphrases, then construct a relation graph about them with user interest and user following web. Finally, we rank those candidates with graph models for realizing keyphrases detection. Experiments show this model is very effective for micro-blogging keyphrase extraction.