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RK_Thesis.pdf (2.08 MB)
ETD Abstract Container
Abstract Header
Knowledge Enabled Location Prediction of Twitter Users
Author Info
Krishnamurthy, Revathy
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=wright1424349831
Abstract Details
Year and Degree
2015, Master of Science (MS), Wright State University, Computer Science.
Abstract
As the popularity of online social networking sites such as Twitter and Facebook continues to rise, the volume of textual content generated on the web is increasing rapidly. The mining of user generated content in social media has proven effective in domains ranging from personalization and recommendation systems to crisis management. These applications stand to be further enhanced by incorporating information about the geo-position of social media users in their analysis. Due to privacy concerns, users are largely reluctant to share their location information. As a consequence of this, researchers have focused on automatic inferencing of location information from the contents of a user’s tweets. Existing approaches are purely data-driven and require large training data sets of geotagged tweets. Furthermore, these approaches rely solely on social media features or probabilistic language models and fail to capture the underlying semantics of the tweets. In this thesis, we propose a novel knowledge based approach that does not require any training data. Our approach uses Wikipedia, a crowd sourced knowledge base, to extract entities that are relevant to a location. We refer to these entities as local entities. Additionally, we score the relevance of each local entity with respect to the city, using the Wikipedia Hyperlink Graph. We predict the most likely location of the user by matching the scored entities of a city and the entities mentioned by users in their tweets. We evaluate our approach on a publicly available data set consisting of 5119 Twitter users across continental United States and show comparable accuracy to the state-of-the-art approaches. Our results demonstrate the ability to pinpoint the location of a Twitter user to a state and a city using Wikipedia, without needing to train a probabilistic model.
Committee
Amit Sheth, Ph.D. (Advisor)
Krishnaprasad Thirunarayan, Ph.D. (Committee Member)
Derek Doran, Ph.D. (Committee Member)
Pages
69 p.
Subject Headings
Computer Science
Keywords
Wikipedia, Twitter, Location Prediction, Semantics
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Citations
Krishnamurthy, R. (2015).
Knowledge Enabled Location Prediction of Twitter Users
[Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1424349831
APA Style (7th edition)
Krishnamurthy, Revathy.
Knowledge Enabled Location Prediction of Twitter Users.
2015. Wright State University, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=wright1424349831.
MLA Style (8th edition)
Krishnamurthy, Revathy. "Knowledge Enabled Location Prediction of Twitter Users." Master's thesis, Wright State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1424349831
Chicago Manual of Style (17th edition)
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Document number:
wright1424349831
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1,759
Copyright Info
© 2015, some rights reserved.
Knowledge Enabled Location Prediction of Twitter Users by Revathy Krishnamurthy is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.