Document Type
Conference Proceeding
Publication Date
2015
Abstract
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 comparable performance to the state of the art techniques.
Repository Citation
Krishnamurthy, R.,
Kapanipathi, P.,
Sheth, A. P.,
& Thirunarayan, K.
(2015). Knowledge Enabled Approach to Predict the Location of Twitter Users. Lecture Notes in Computer Science, 9088, 187-201.
https://corescholar.libraries.wright.edu/knoesis/1067
DOI
10.1007/978-3-319-18818-8_12
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
Presented at the 12th European Semantic Web Conference, Portoroz, Slovenia, May 31-June 4, 2015.
Available for download is the author's version of the proceeding. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-18818-8_12.