Predicting User Influence in Social Media

Understanding influence plays a vital role in enhancing businesses operation and improving effect of information propagation. Therefore the user influence in social media, such as Twitter, is widely studied based on different standards, such as the number of followers, retweets and so on. However, little work considers the accurate click number of short URLs as the measurement of influence. In Twitter short URLs are frequently included in tweets because of the limitation of characters. And some users may focus more on click number of the URLs instead of the number of followers or retweets. Thus, it is necessary to analyze the factors that impact the click number received by URLs of users. In this paper, we conduct the predictive analyses about the user influence which is measured by the click number of short URLs. We first exploit a wide range of possible features consisting of the sets of user properties, behavior and topics. And then we employ the logistic regression analysis to identify the significant features for predicting the user influence, and find most of features we proposed have a significant predictive power to the user influence. Finally based on the large scale Twitter data, four models are used for the prediction and the Bagging model achieves the best result, an overall accuracy of more than