With the ever expanding usage of social media websites such as Twitter, it is possible to use statistical inquires to form a geographic location of a person using solely the content of their tweets. According to a study done in 2010, Zhiyuan Cheng, was able to detect a location of a Twitter user within 100 miles of their actual location 51% of the time. While this may seem like an already significant find, this study was done while Twitter was still finding its ground to stand on. In 2010, Twitter had 75 million unique users registered, as of March 2013, Twitter has around 500 million unique users. In this thesis, my own dataset was collected and using Excel macros, a comparison of my results to that of Cheng’s will see if the results have changed over the three years since his study. If found to be that Cheng’s 51% can be shown more efficiently using a simpler methodology, this could have a significant impact on Homeland Security and cyber security measures.
","abstract_html":"<p>With the ever expanding usage of social media websites such as Twitter, it is possible to use statistical inquires to form a geographic location of a person using solely the content of their tweets. According to a study done in 2010, Zhiyuan Cheng, was able to detect a location of a Twitter user within 100 miles of their actual location 51% of the time. While this may seem like an already significant find, this study was done while Twitter was still finding its ground to stand on. In 2010, Twitter had 75 million unique users registered, as of March 2013, Twitter has around 500 million unique users. In this thesis, my own dataset was collected and using Excel macros, a comparison of my results to that of Cheng’s will see if the results have changed over the three years since his study. If found to be that Cheng’s 51% can be shown more efficiently using a simpler methodology, this could have a significant impact on Homeland Security and cyber security measures.</p>","abstract_has_math":false,"creators":["Wright, Christopher M."],"institution":null,"degree_name":"Master of Science","degree_level":null,"degree_discipline":"Department of Physics and Astronomy","degree_department":null,"school":null,"contributors":["Phillip Womble (Director), Keith Andrew, Lance Hahn"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-05-01T07:00:00Z","date_published":"2014-05-01T07:00:00Z","updated_at":"2026-07-24T06:08:39Z","subjects":["Large Data Set","Linguistics","Big Data Analysis","Human Intelligence","Twitter","Facebook","Social Networking","Software Engineering","Internet","Communication Technology and New Media","OS and Networks","Theory and Algorithms"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wku.edu/theses/1372","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Phillip Womble (Director), Keith Andrew, Lance Hahn"]},{"key":"dc:creator","label":"Author","values":["Wright, Christopher M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Physics and Astronomy"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Large Data Set","Linguistics","Big Data Analysis","Human Intelligence","Twitter","Facebook","Social Networking","Software Engineering","Internet","Communication Technology and New Media","OS and Networks","Theory and Algorithms"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wku.edu/theses/1372"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["With the ever expanding usage of social media websites such as Twitter, it is possible to use statistical inquires to form a geographic location of a person using solely the content of their tweets. According to a study done in 2010, Zhiyuan Cheng, was able to detect a location of a Twitter user within 100 miles of their actual location 51% of the time. While this may seem like an already significant find, this study was done while Twitter was still finding its ground to stand on. In 2010, Twitter had 75 million unique users registered, as of March 2013, Twitter has around 500 million unique users. In this thesis, my own dataset was collected and using Excel macros, a comparison of my results to that of Cheng’s will see if the results have changed over the three years since his study. If found to be that Cheng’s 51% can be shown more efficiently using a simpler methodology, this could have a significant impact on Homeland Security and cyber security measures.
"]},{"key":"dc:title","label":"Title","values":["Using Statistical Methods to Determine Geolocation Via Twitter"]}]}],"canonical_facts":{"dc:contributor":["Phillip Womble (Director), Keith Andrew, Lance Hahn"],"dc:creator":["Wright, Christopher M."],"dc:description.abstract":["With the ever expanding usage of social media websites such as Twitter, it is possible to use statistical inquires to form a geographic location of a person using solely the content of their tweets. According to a study done in 2010, Zhiyuan Cheng, was able to detect a location of a Twitter user within 100 miles of their actual location 51% of the time. While this may seem like an already significant find, this study was done while Twitter was still finding its ground to stand on. In 2010, Twitter had 75 million unique users registered, as of March 2013, Twitter has around 500 million unique users. In this thesis, my own dataset was collected and using Excel macros, a comparison of my results to that of Cheng’s will see if the results have changed over the three years since his study. If found to be that Cheng’s 51% can be shown more efficiently using a simpler methodology, this could have a significant impact on Homeland Security and cyber security measures.
"],"dc:identifier":["https://digitalcommons.wku.edu/theses/1372"],"dc:subject":["Large Data Set","Linguistics","Big Data Analysis","Human Intelligence","Twitter","Facebook","Social Networking","Software Engineering","Internet","Communication Technology and New Media","OS and Networks","Theory and Algorithms"],"dc:title":["Using Statistical Methods to Determine Geolocation Via Twitter"],"dc:type":["Thesis"],"thesis:degree_discipline":["Department of Physics and Astronomy"],"thesis:degree_name":["Master of Science"]},"updated_at":"2026-07-24T06:08:39Z"}