Detecting Fake Users on Social Media with Neo4j and Random Forest Classifier
| dc.contributor.author | Zhao, Yichun | |
| dc.date.accessioned | 2020-06-08T20:40:31Z | |
| dc.date.available | 2020-06-08T20:40:31Z | |
| dc.date.copyright | 2020 | en_US |
| dc.date.issued | 2020-06-08 | |
| dc.description.abstract | Fake 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.reviewstatus | Reviewed | en_US |
| dc.description.scholarlevel | Undergraduate | en_US |
| dc.description.sponsorship | Jamie Cassels Undergraduate Research Award (JCURA) | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/11809 | |
| dc.language.iso | en | en_US |
| dc.subject | Fake User; Fake News; Social Media; Graph Database; Machine Learning | en_US |
| dc.title | Detecting Fake Users on Social Media with Neo4j and Random Forest Classifier | en_US |
| dc.type | Poster | en_US |
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