Tweethood: Agglomerative Clustering on Fuzzy k-Closest Friends with Variable Depth for Location Mining

According to a recent report by research firm ABI Research, location-based social networks could reach revenues as high as $13.3 billion by 2014. Social Networks like Foursquare and Gowalla are in a dead heat in the Location War. But, having said that it is important to understand for privacy and security reasons, most of the people on social networking sites like Twitter are unwilling to specify their locations explicitly. This creates a need for software that mines the location of the user based on the implicit attributes associated with him. In this paper, we propose the development of a tool TweetHood that predicts the location of the user on the basis of his social network. We show the evolution of the algorithm, highlighting the drawbacks of the different approaches and our methodology to overcome them. We perform extensive experiments to show the validity of our system in terms of both accuracy and running time. The experiments performed demonstrate that our system achieves an accuracy of 72.1% at the city level and 80.1% at the country level. Experimental results show that TweetHood outperforms the gazetteer based geo-tagging approach.