A genetic algorithm based tweet clustering technique

Abstract

The Twitter micro blogging site is one of the most popular online social media in today's Web. During an important event, such as a disaster event, hundreds of thousands of tweets are posted rapidly on Twitter. The information is posted too fast for anyone to make sense, hence the information needs to be organized in order to utilize the information effectively. It has been observed that many of the tweets posted during an event are very similar to each other, hence clustering or grouping similar tweets is an effective way to reduce the information load. However, clustering of tweets is challenging because of the small size and noisy nature of tweets. In this work, we propose a novel clustering approach for tweets, which combines two different approaches — a traditional clustering approach K-Means, and an evolutionary approach, Genetic Algorithms. We conduct experiments on a dataset of real tweets collected during a recent disaster event, and show that the proposed methodology performs better than several existing clustering techniques.