Fake Social Media Profile Detection using Machine Learning

Information Technology Department

Online spoofing and fraudulent accounts are common on the social network, which is so crucial to our daily lives. Fake accounts that appear to have been created on behalf of businesses or individuals are the ones most likely to use issues related to social media, such as confidentiality, online abuse, misuse, bullying, etc., which can harm a person's reputation and decrease their number of likes and followers. On the other hand, the creation of false accounts is anticipated to harm more people than any other type of cybercrime. This problem inspires us to create a machine learning-based system for identifying fraudulent social media accounts. False profiles are frequently used by intruders in online social networks to engage in harmful activities like harassing individuals, identity theft, and privacy violations. As a result, one of the most challenging tasks on the online social network site is figuring out whether an account is legitimate or fraudulent. In this study, we introduced the Support Vector Machine algorithm and a deep neural network, among other classification methods. Additionally, it contrasts classification strategies using the Spam User dataset.

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