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Extraction and Analysis of Regionally Specific Behavioral Facilitation Information in the Event of a Large-scale Disaster

During and after large-scale disasters, many people try to obtain disaster-related information from social networking services (SNSs) such as Twitter. This is true because SNSs have much useful information about a disaster. Such information includes prompting disaster-affected people to take action. Therefore, we have proposed a tweet extraction method to encourage disaster-affected people to take action on Twitter. We designate tweets that encourage disaster-affected people to take action as “behavioral facilitation tweets (BF tweets.” After analyzing behavioral facilitation tweets, we found that these tweets include local information that is more useful for disaster-affected people than tweets with no local information. For this study, we call such tweets “Region-specific Behavioral Facilitation tweets (RBF tweets)”: they have more specific disaster content and are more likely to promote action. As described herein, we propose an RBF tweet extraction method for use after a large-scale disaster. Furthermore, after we propose a method of classifying sources from which information is sent and targets to whom it is sent, we analyze them for classified RBF tweets.