Propagator or Influencer? A Data-driven Approach for Evaluating Emotional Effect in Online Information Diffusion

摘要

Reposting is the basic and key behavior for information diffusion in online social networks. It would be beneficial to understand the influence factors of reposting behavior and predict future reposting status, which could be practically applied in breaking news detection, marketing, social media researches and so on. Existing reposting analytics and prediction approaches mainly focus on factors related to the original information content and the social influence of the information publishers. However, online information diffuses by viral cascades instead of single-source broadcast in social network, which means some reposting behavior actually occurs in information propagators rather than the original publishers. In some social networks, users are allowed to comment when they repost, which represents their views and attitudes to the information they propagate. In this paper, we evaluate how emotional tendencies of information propagators influence future reposting. We first propose a modified sentiment analysis method and present emotional analysis on the user-generated content in online diffusion. Experiments are conducted with a real-world dataset and the results indicate the effectiveness of our finegrained emotional features in reposting prediction.