Detecting Public Influence on News Using Topic-Aware Dynamic Granger Test

With the rapid proliferation of Web 2.0, user-generated content (UGC), which is formed by the public to reflect their views and voice, presents rich and timely feedback on news events. Existing research either studies the common and private features between news and UGC, or describes the ability of news media to influence the public opinion. However, in the current highly media-user interactive environment, investigating the public influence on news is of great significance to risk and credible management for government and enterprises. In this paper, we propose a novel topic-aware dynamic Granger test framework to quantify and characterize the public influence on news. In particular, we represent words and documents as distributed low-dimensional vectors which facilitates the subsequent topic extraction. Then, a topic-aware dynamic strategy is proposed to transfer news and UGC streams into topic series, and finally we apply Granger causality test to investigate the public influence on news. Extensive experiments on 45 diverse realworld events demonstrate the effectiveness of the proposed method, and the results show promising prospects on predicting whether an event will be properly handled at its early stage.