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The unprecedented use of social media through smartphones and otherweb-enabled mobile devices has enabled the rapid adoption of platforms likeTwitter. Event detection has found many applications on the web, includingbreaking news identification and summarization. The recent increase in theusage of Twitter during crises has attracted researchers to focus on detectingevents in tweets. However, current solutions have focused on static Twitterdata. The necessity to detect events in a streaming environment during fastpaced events such as a crisis presents new opportunities and challenges. Inthis paper, we investigate event detection in the context of real-time Twitterstreams as observed in real-world crises. We highlight the key challenges inthis problem: the informal nature of text, and the high volume and highvelocity characteristics of Twitter streams. We present a novel approach toaddress these challenges using single-pass clustering and the compressiondistance to efficiently detect events in Twitter streams. Through experimentson large Twitter datasets, we demonstrate that the proposed framework is ableto detect events in near real-time and can scale to large and noisy Twitterstreams.

From Tweets to Events: Exploring a Scalable Solution for Twitter Streams