Learning from the crowd: An evolutionary mutual reinforcement model for analyzing events

Social media is inarguably a powerful medium for mobilizing support for various real-life events be it for social, political, or economic transformation. Further, in contrast to the generic information obtained from the mainstream media, novel and specific information available at social media sites makes them valuable sources for event analysis. However, due to the power law distribution of the Internet, these overwhelmingly large number of sources are buried in the Long Tail making it extremely challenging to identify the quality sources among them. In this research, we propose an evolutionary mutual reinforcement model to confront these challenges. Due to absence of ground truth, a novel evaluation strategy is introduced. The results indicate tremendous potential. 25% to 130% information gain is obtained with the proposed approach when compared against the state-of-the-art baselines, viz. Google blog search and Icerocket blog search. Further, our ranking methodology is capable of identifying the highly informative sources much earlier than the aforementioned baselines. The proposed model affords an apparatus for micro and macro event analysis.