Text mining on social media's data during disasters response phase - Bougouffa, Safa (Kulliyyah of Information and Communication Technology, International Islamic University Malaysia, 2015)
The pervasive use of social media has engendered extraordinary amounts of social data. Social media provides easily an accessible platform for users to share extensively information about situational updates during emergency states and disasters. In this study, we apply a text mining technique on Twitter's data throughout the New York snowstorm and the Hollister earthquake, together with a content analysis of an Online Discussion Forums collection. We explore the role of text mining in evaluating information during disasters in order to understand the use of social media as well to the issues that emerged by the online users during the preparedness, mitigation, response and recovery phases of a disaster. Moreover, the investigation is born out of the fact that Information and Communication Technologies can serve to help in the different phases of a disaster. An informatics focus on matters of disasters is essential for the social good, as well for the increase of the attention from all sectors on disasters. The results of this study show that the information on Twitter were not the original content, but instead it came from traditional media and other sources, which were subject to journalistic standards and the social media played the role of a mediator between the different types of media.