Detecting Fake Users on Social Media with Neo4j and Random Forest Classifier

dc.contributor.authorZhao, Yichun
dc.date.accessioned2020-06-08T20:40:31Z
dc.date.available2020-06-08T20:40:31Z
dc.date.copyright2020en_US
dc.date.issued2020-06-08
dc.description.abstractFake news is defined by the Ethical Journalism Network as “deliberately fabricated and published” information intended “to deceive and mislead others.” It manipulates the ignorant into false beliefs and causes negative societal impacts. Fake social media users are perceived as popular, and they spread fake news by making it look real. The objective of this research project is to improve the accuracy of detecting fake users in the previous study by A. Mehrotra, M. Sarreddy and S. Singh, by using different centrality measures supported by the Neo4j graph database and two more datasets. The machine learning algorithm - random forest classifier, which uses the centrality measures as its features, detects fake users on social media with reasonable results.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelUndergraduateen_US
dc.description.sponsorshipJamie Cassels Undergraduate Research Award (JCURA)en_US
dc.identifier.urihttp://hdl.handle.net/1828/11809
dc.language.isoenen_US
dc.subjectFake User; Fake News; Social Media; Graph Database; Machine Learningen_US
dc.titleDetecting Fake Users on Social Media with Neo4j and Random Forest Classifieren_US
dc.typePosteren_US

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