Twitter Analysis based on Damage Detection and Geoparsing for Event Mapping Management

Background: Early event detection, monitor, and response can significantly decrease the impact of disasters. Lately, the usage of social media for detecting events has displayed hopeful results. Objectives: for event detection and mapping; the tweets will locate and monitor them on a map. This new approach uses grouped geoparsing then scoring for each tweet based on three spatial indicators. Method/Approach: Our approach uses a geoparsing technique to match a location in tweets to geographic locations of multiple-events tweets in Egypt country, administrative subdivision. Thus, additional geographic information acquired from the tweet itself to detect the actual locations that the user mentioned in the tweet. Results: The approach was developed from a large pool of tweets related to various crisis events over one year. Only all (very specific) tweets that were plotted on a crisis map to monitor these events. The tweets were analyzed through predefined geo-graphical displays, message content filters (damage, casualties). Conclusion: A method was implemented to predict the effective start of any crisis event and an inequity condition is applied to determine the end of the event. Results indicate that our automated filtering of information provides valuable information for operational response and crisis communication