Social Network Analytics: Natural Disaster Analysis Through Twitter

Today, we are in the era of social networks producing a huge quantity of data accentuated by mobile technologies. This evolution of the actors and the behavior of social networks users leads to massive data on social web. People share their experiences and opinions on miscellaneous topics in social networks, such as politics, health issues, and natural disasters. This makes publicly available data an inestimable resource for different addressed topics. In this paper, we investigate natural disasters including avalanche, drought, earthquake, flood, hurricane, landslide, tornado, tsunami, volcano, and wildfire. So, we extract data from Twitter Network, we classified relevant tweets to positive, very positive, negative, very negative, or neutral. Then, we define the most dominant disasters information through Word Cloud and we analyze this information using the pie chart. Furthermore, we plot the data on the map based on the coordinates (longitude and latitude). Finally, we deduce the most frequent disaster and which region is the most influenced.