Real-time event search corresponding to place and time using social stream

Abstract

Since the decision was made to hold the Olympic Games in 2020 in Tokyo, the number of foreign tourists visiting the city has been increasing rapidly. Accordingly, tourists have been seeking more sightseeing information. Their interests and visit durations are varied, so they should be provided information that is interesting and timely to each of them. While guidebooks are good for pointing out popular tourist attractions, it is more difficult for tourists to get information on local events and spots that are just becoming popular. We developed a tourist information distribution system that sends information corresponding to a place and time. The system extracts recent major and minor event information from social media streams in a per place and time manner and provides it to tourists. We evaluated the accuracy of its event classification of actual Twitter data with two representative methods: support vector machine (SVM) and random forests. Furthermore, we found that supplementary information from the web can be used to provide more accurate event information.