document: Discovering geographical topics in the twitter stream Micro-blogging services have become indispensable communication tools for online users for disseminating breaking news , eyewitness accounts , individual expression , and protest groups . Recently , Twitter , along with other online social networking services such as Foursquare , Gowalla , Facebook and Yelp , have started supporting location services in their messages , either explicitly , by letting users choose their places , or implicitly , by enabling geo-tagging , which is to associate messages with latitudes and longitudes . This functionality allows researchers to address an exciting set of questions : 1 ) How is information created and shared across geographical locations , 2 ) How do spatial and linguistic characteristics of people vary across regions , and 3 ) How to model human mobility . Although many attempts have been made for tackling these problems , previous methods are either complicated to be implemented or oversimplified that can not yield reasonable performance . It is a challenge task to discover topics and identify users ' interests from these geo-tagged messages due to the sheer amount of data and diversity of language variations used on these location sharing services . In this paper we focus on Twitter and present an algorithm by modeling diversity in tweets based on topical diversity , geographical diversity , and an interest distribution of the user . Furthermore , we take the Markovian nature of a user 's location into account . Our model exploits sparse factorial coding of the attributes , thus allowing us to deal with a large and diverse set of covariates efficiently . Our approach is vital for applications such as user profiling , content recommendation and topic tracking . We show high accuracy in location estimation based on our model . Moreover , the algorithm identifies interesting topics based on location and language .
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