CROWDSOURCING APPROACH FOR DISASTER RESPONSE ASSESSMENT

Philippines are a country attuned to social media and a disaster prone country and recent research focused on the interesting use of Twitter. This work is motivated to provide information through crowdsourcing, which uses humans as sensors to observe and report events in the physical world. In this paper we propose that, Twitter feeds which consist of short messages to extract information as a tool in needs assessment for a disaster hit community. This information will serve as situation awareness through crowd sensing, in order to deliver the relevant basic needs to the disaster stricken community and humanitarian disaster response. The data were obtain using the Twitters open search API, preliminary experiment is carried out, Naïve Bayes algorithm was used to classify disaster related tweets. The geo location feature in the tweets were extracted and translated into map for visualization and the information related to disaster. This study will be helpful in identifying, analyzing, monitoring and evaluating basic needs of the affected communities, in order for the decision makers to take necessary actions and respond to the needs of the people.