Exploiting Social Networks As A Live Mass Media Channel During Disasters For Reactions

Social networks, e.g. Twitter, have been proved to be almost real-time systems for spreading information, that provide a valuable information channel in emergencies, e.g. disasters. This paper presents a framework designed to distill actionable tweets. The framework tackles the diversity, large volume, and noise of tweets for providing users live information for quick responses. To do that, our framework first retrieves a large number of tweets to ensure the diversity. It next removes irrelevant and indirect tweets for reducing the volume, divides informative tweets into predefined classes for quick navigation, and groups tweets in a class into topics to preserve the diversity. Finally, it ranks tweets in each topic to extract important tweets for the user's quick scan. For ranking, the framework utilizes event extraction to enrich the semantics and reduce the noise of tweets. After that, the framework builds event graphs for ranking to find out important tweets. To validate the efficiency of our framework, we took Twitter as a case study. Experimental results on five disaster datasets show that our framework achieves promising results compared to strong methods in disaster scenarios.