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Quantifying Information Propogation Rate and Geographical Location Extraction During Disasters Using Online Social Networks
Abstract
The pervasive popularity of social networking facilitates the propagation of trending information and the online exchange of diverse opinions among socially connected individuals. In order to identify events from the density ratio of real-time tweets, the authors suggest a new underlying quantification model, and morphological time-series analysis is performed using information entropy to ascertain the rate of news coverage of crisis situations. To further get insightful patterns in events, the event-link ratio is evaluated. In this study, the authors utilize data collected from Twitter to evaluate how far news of these events has spread. The study concludes by demonstrating the effectiveness of the proposed framework in a case study on the disasters events where it successfully captured critical information and provided insights into the dissemination of information during the disaster. The suggested approach detects events faster and with 94% accuracy than state-of-the-art methods. Comparing all location references, unambiguous location extraction has 96% accuracy.
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