Detecting popular topics in micro-blogging based on a user interest-based model

The rapid increasing popularity of micro-blogging has made it an important information seeking channel. By detecting recent popular topics from micro-blogging, we have opportunities to gain insights into internet hotspots. Generally, a topic's popularity is determined by two primary factors. One is how frequently a topic is discussed by users, and the other is how much influence those users have, since topics shown in the influential users' posts are more likely to attract others' attention. However, existing approaches interpret a topic's popularity with only the number of keywords related to it, which neglect the importance of the user influence to information diffusion in micro-blogging. In this paper, drawing upon the Cognitive Authority Theory and Social Network Theory, we propose a novel model that detects the most popular topics in micro-blogging with a user interest-based method. The proposed model first constructs a topic graph according to users' interests and their following relationship, and then calculates the topics' popularity with a link-based ranking algorithm. The popular topics detected by the method can reflect the relationship among users' interests, and the topics in the posts of influential users can be highlighted. Experimental results on the data of Twitter, a well-known and feature-rich micro-blogging service, show that the proposed method is effective in popular topic discovery.