Comprehensive Event Storyline Generation from Microblogs

Abstract

Microblogging data contains a wealth of information of trending events and has gained increased attention among users, organizations, and research scholars for social media mining in different disciplines. Event storyline generation is one typical task of social media mining, whose goal is to extract the development stages with associated description of events. Existing storyline generation methods either generate storyline with less integrity or fail to guarantee the coherence between the discovered stages. Secondly, there are no scientific method to evaluate the quality of the storyline. In this paper, we propose a comprehensive storyline generation framework to address the above disadvantages. Given Microblogging data related to the specified event, we first propose Hot-Word-Based stage detection algorithm to identify the potential stages of event, which can effectively avoid ignoring important stages and preventing inconsistent sequence between stages. Community detection algorithm is applied then to select representative data for each stage. Finally, we conduct graph optimization algorithm to generate the logically coherent storylines of the event. We also introduce a new evaluation metric, SLEU, to emphasize the importance of the integrity and coherence of the generated storyline. Extensive experiments on real-world Chinese microblogging data demonstrate the effectiveness of the proposed methods in each module and the overall framework.