Social media feeds are rapidly emerging as a novel avenue for the contribution and dissemination of geographic information. Among which Twitter, a popular micro-blogging service, has recently gained tremendous attention for its real-time nature. For instance, during floods, people usually tweet which enable detection of flood events by observing the twitter feeds promptly. In this paper, we propose a framework to investigate the real-time interplay between catastrophic event and peo-ples’ reaction such as flood and tweets to identify disaster zones. We have demonstrated our approach using the tweets following a flood in the state of Bihar in India during year 2017 as a case study. We construct a classifier for semantic analysis of the tweets in order to classify them into flood and non-flood categories. Subsequently, we apply natural language processing methods to extract information on flood affected areas and use elevation maps to identify potential disaster zones.