Knowledge bases have been used to improve performance in applications ranging from web search and event detection to entity recognition and disambiguation. More recently, knowledge bases have been used to analyze social data. A key challenge in social data analysis has been the identification of the geographic location of online users in a social network such as Twitter. Existing approaches to predict the location of users, based on their tweets, rely solely on social media features or probabilistic language models. These approaches are supervised and require large training dataset of geo-tagged tweets to build their models. As most Twitter users are reluctant to publish their location, the collection of geo-tagged tweets is a time intensive process. To address this issue, we present an alternative, knowledge-based approach to predict a Twitter user's location at the city level. Our approach utilizes Wikipedia as a source of knowledge base by exploiting its hyperlink structure. Our experiments, on a publicly available dataset demonstrate an improvement of 3% in the accuracy of prediction and a 16% reduction in the average error distance, over the state of the art supervised techniques.
In REF , Krishnamurthy et al. exploited Twitter (https://twitter.com) tweets to predict the location of its users.
Knowledge Enabled Approach to Predict the Location of Twitter Users
ESWC
Computer Science