#ChennaiFloods: Leveraging Human and Machine Learning for Crisis Mapping during Disasters Using Social Media

The recent emergence of ubiquitous smart communication devices accelerate people to post the current trending topics in real time as micro blogs, tweets, posts and multimedia content on social media sites along with geographical location tags (geo-tags). Specifically, during recent floods in Tamilnadu 2015, the early warnings about flooded areas emerged to get posted in popular social media with geo-parsed hash tags continuously. In the humanitarian view, the real-time crisis sparked great interest in designing an innovative methodology using big social media data analysis along with supervised machine learning techniques to actuate immediate disaster response and rescue efforts in near future. The proposed system performs disaster tweet collection based on trending disaster hash tags. Our system performs Naive-Bayesian (multinomial) and SSVM classification on collected tweets to identify the severity of the disaster. Based on location-to-interpolation cluster proximity, disaster geographic map is generated for the affected area. Our approach detects the tweets fitted into correct classifier label, and generate an output with detection rate of 79% to 91% of the time. The predicted disaster mapping results are highly accurate up to 89% for real time geo-parsed tweets that matched with actual location at-risk during the flood.