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.