Social Media Data Analytics Applied to Hurricane Sandy

Social media websites are an integral part of many people´s lives in delivering news and other emergency information. This is especially true during natural disasters. Furthermore, the role of social media websites is becoming more important due to the cost of recent natural disasters. These online platforms are usually the first to deliver emergency news to a wide variety of people due to the significantly large number of users registered. During disasters, extracting useful information from this pool of social media data can be useful in understanding the sentiment of the public, this information can then be used to improve decision making. In this paper, we developed a prototype that automates the process of collecting and analyzing social media data from Twitter. Furthermore, we explore a variety of visualizations that can be generated by the tool in order to understand the public sentiment. We demonstrate an example of utilizing this tool on the Hurricane Sandy disaster between October 26, 2012 to October 30, 2012. Finally, we perform a statistical analysis to explore the causality correlation between an approaching hurricane and the sentiment of the public.