TSum4act: A framework for retrieving and summarizing actionable tweets during a disaster for reaction

Abstract

Social networks (e.g. Twitter) have been proved to be an almost real-time mean of information spread, thus they can be exploited as a valuable channel of information for emergencies (e.g. disasters) during which people need updated information for suitable reactions. In this paper, we present TSum4act, a framework designed to tackle the challenges of tweets (e.g. diversity, large volume, and noise) for disaster responses. The objective of the framework is to retrieve actionable tweets (e.g. casualties, cautions, and donations) that were posted during disasters. For this purpose, the framework first identifies informative tweets to remove noise; then assigns informative tweets into topics to preserve the diversity; next summarizes the topics to be compact; and finally ranks the results for user’s faster scan. In order to improve the performance, we proposed to incorporate event extraction for enriching the semantics of tweets. TSum4act has been successfully tested on Joplin tornado dataset of 230.535 tweets and the completeness of 0.58 outperformed 17%, of the retweet baseline’s.