Characterizing the interests of social media users: Refinement of a topic model for incorporating heterogeneous media

Recent research has focused on extracting personal interest data from social media. Although many methods have been developed, accurately estimating users’ interests is often difficult because messages on social media are short and are not classified into any predefined categories. We propose a new method to overcome this problem by incorporating heterogeneous media, such as news. In our method, we first extract explicit features and implicit topics of categories using news media, where implicit topics are determined using a refined topic model. Next, we describe social media messages using these features and topics to estimate users’ interests. Compared with several other approaches, our approach provides more accurate estimations of users’ interests. We also demonstrate that the accuracy of friend recommendations is increased using the users’ interests estimated by our method. Thus, we expect that the proposed approach could be helpful for enhancing the personalization of social media services.