Faux Finder: Harnessing the Power of Machine Learning to Root Out Fake Social Media Profiles

Abstract

The development of machine learning (ML) applications for the identification of phony accounts on social media platforms is examined in this project report. Although machine learning (ML) has been around for a while in this field, its broad use for this purpose really took off in the 2010s. The initial breakthrough was shown by a 2009 Carnegie Mellon University study, which showed that machine learning classifiers were more accurate than traditional approaches in differentiating between phony MySpace profiles._x000D_ The development of machine learning algorithms for the identification of bogus accounts continued in the years that followed, integrating methods like categorization, anomaly detection, and graph-based algorithms. Fake accounts may now be found and eliminated on major social media sites like Facebook and Twitter thanks to machine learning (ML), which has significantly improved detection capabilities, Millions of fraudulent accounts were removed annually as a consequence of the use of ML, which was a significant improvement over earlier techniques.