Social science studies have acknowledged that the social influence of individuals is not identical. Social networks structure and shared text can reveal immense information about users, their interests, and topic-based influence. Although some studies have considered measuring user influence, less has been on measuring and estimating topic-based user influence. In this paper, we propose an approach that incorporates network structure, usergenerated content for topic-based influence measurement, and user's interactions in the network. We perform experimental analysis on Twitter data and show that our proposed approach can effectively measure topic-based user influence.
Hamzehe et al. REF proposed an approach that incorporates network structure, user generated content for topic-based influence measurement.
Topic-based Social Influence Measurement for Social Networks
Australasian J. of Inf. Systems
Australasian J. of Inf. Systems
Computer Science