Location-specific tweet detection and topic summarization in Twitter

Abstract

Automatic detection of tweets that provide Location-specific information will be extremely useful in conveying geo-location based knowledge to the users. However, there is a significant challenge in retrieving such tweets due to the sparsity of geo-tag information, the short textual nature of tweets, and the lack of pre-defined set of topics. In this paper, we develop a novel framework to identify and summarize tweets that are specific to a location. First, we propose a weighting scheme called Location Centric Word Co-occurrence (LCWC) that uses the content of the tweets and the network information of the twitterers to identify tweets that are location-specific. We evaluate the proposed model using a set of annotated tweets and compare the performance with other weighting schemes studied in the literature. This paper reports three key findings: (a) top trending tweets from a location are poor descriptors of location-specific tweets, (b) ranking tweets purely based on users' geo-location cannot ascertain the location specificity of tweets, and (c) users' network information plays an important role in determining the location-specific characteristics of the tweets. Finally, we train a topic model based on Latent Dirichlet Allocation (LDA) using a large collection of local news database and tweet-based Urls to predict the topics from the location-specific tweets and present them using an interactive web-based interface.