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dc.contributor.authorThompson, Kyle Maurice
dc.date.accessioned2015-08-07T04:30:35Z
dc.date.available2015-08-07T04:30:35Z
dc.date.issued2014-12
dc.identifier.otherthompson_kyle_m_201412_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/thompson_kyle_m_201412_ms
dc.identifier.urihttp://hdl.handle.net/10724/31521
dc.description.abstractIn today’s society, social media has become one of the primary sources of obtaining information. Citizens utilize social media platforms to familiarize themselves with and learn about, current events around the world. There is an increase in individuals that depend on social media to be valid and provide correct information on newsworthy topics. This research provides a unique automated approach that incorporates human intelligence and text mining techniques. The phrase “What vs. Who” is used to separate the past research from this approach. The automated approach includes techniques similar to text mining which uses Python’s Natural Language Toolkit to incorporate natural language processing and search through individual tweets for certain keywords. To provide credible information, news articles, verified Twitter accounts, and criminal and domestic release records, which possess official power, were used as the golden standard and deemed credible. Crowd sourcing was then used to compare results with the automated approach.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectSocial Media
dc.subjectTwitter credibility
dc.titleAssessment of information credibility in Twitter
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorIsmailcem Budak Arpinar
dc.description.committeeIsmailcem Budak Arpinar
dc.description.committeeWalter D. Potter
dc.description.committeeE. Rodney Canfield


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