– The purpose of this paper is to propose a method to detect local events in real time using Twitter, an online microblogging platform. The authors especially aim at detecting local events regardless of the type and scale.
– The method is based on the observation that relevant tweets (Twitter posts) are simultaneously posted from the place where a local event is happening. Specifically, the method first extracts the place where and the time when multiple tweets are posted using a hierarchical clustering technique. It next detects the co-occurrences of key terms in each spatiotemporal cluster to find local events. To determine key terms, it computes the term frequency-inverse document frequency (TFIDF) scores based on the spatiotemporal locality of tweets.
– From the experimental results using geotagged tweet data between 9 a.m. and 3 p.m. on October 9, 2011, the method significantly improved the precision of between 50 and 100 per cent at the same recall compared to a baseline method.
– In contrast to existing work, the method described in this paper can detect various types of small-scale local events as well as large-scale ones by incorporating the spatiotemporal feature of tweet postings and the text relevance of tweets. The findings will be useful to researchers who are interested in real-time event detection using microblogs.
