Detecting Abusive Arabic Language Twitter Accounts Using a Multidimensional Analysis Model

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dc.contributor.advisorJones, James H
dc.contributor.authorAbozinadah, Ehab
dc.creatorAbozinadah, Ehab
dc.date.accessioned2018-10-22T01:21:21Z
dc.date.available2018-10-22T01:21:21Z
dc.date.issued2017
dc.description.abstractTwitter is one of the most popular social media sources for disseminating news and propaganda in the Middle East. The increased use of social media has motivated spammers to post malicious content on social media sites. Some of these Arabic language spammers use adult content to further the distribution of their malicious activities. However, the extensive number of users posting adult content in social media degrades the experience for other users for whom the adult content is not desired or appropriate. These accounts would be suspended or terminated from Twitter whenever reported by Twitter’s users as Twitter prohibits adult content in an image, a video, or a text. Moreover, some countries have attempted to detect these accounts, but have failed as these accounts use informal Arabic language and misspelled words that cannot be detected using blacklisted keywords.
dc.format.extent127 pages
dc.identifier.urihttps://hdl.handle.net/1920/11326
dc.language.isoen
dc.rightsCopyright 2017 Ehab Abozinadah
dc.subjectInformation technology
dc.subjectComputer science
dc.subjectBig Data
dc.subjectClassification
dc.subjectCyber Security
dc.subjectData Analysis
dc.subjectMachine learning
dc.subjectSocial media Analysis
dc.titleDetecting Abusive Arabic Language Twitter Accounts Using a Multidimensional Analysis Model
dc.typeDissertation
thesis.degree.disciplineComputer Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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