Abstract: Previous studies on user influence modeling in social networks mostly depend on user friendship network structures and retweeting behaviors. It lacks of the consideration of contents and topics of the tweets, which may also play important roles. In addition, taking the interaction among topics into account would facilitate a more accurate user influence modeling. In this paper, we propose a semi-supervised topic extraction method, which brings in a set of seed words during initialization and assigns these seed words higher weights than other words, to improve the effectiveness of topic extraction. To better model the user influence, we involve the interactions among topics, and combine the similarity of topics together with the similarity of users. Experimental results on real-world datasets demonstrate the effectiveness of our proposed methods.

Key words: social networks, user influence evaluation, feature interaction, topic-specific