Social Influence Estimation for Short Texts in Plurk

Social media present a user-friendly way of communication and sharing, which brings new chances to understand users and their social communication patterns. With the popularity of microblogging services, the huge volume of very short texts makes it difficult to track the latest updates or breaking news. In this paper, we propose a novel social influence model for estimating the popularity score for each short text in plurk. First, the degrees of user participation and user propagation are estimated by the number of replies, replurks, likes, and URIs. Then, we measure the influence persistence by the duration of the initial post and the last response, and the influence score can be derived from a linear combination of these simple statistics. Our experimental results on more than 300 thousand plurks collected from 1,750 users showed a good performance in determining popular messages, with the best F-measure of 0.86. From our case studies, top-ranked messages can accurately reflect the popular discussions on important events. This shows the effectiveness of our proposed approach. Further investigation of applying the influence model in event detection is needed.