FAKE PROFILE IDENTIFICATION IN SOCIAL NETWORK USING MACHINE LEARNING AND NLP

Abstract

Abstract:Platforms for social media like Facebook, Twitter, Instagram, and othershave a big impact on our lives. All across the world, people are actively engaged in it. But, it also needs to address theproblem of false profiles. Fake accounts are regularly made by people,software, or machines. Theyare employed in the spread of rumours and illegal actions like phishing andidentity theft. This project uses several machine learning techniques todiscriminate between fake and authentic Twitter profiles based oncharacteristics such as follower and friend counts, status changes, andmore. Twitter profile dataset, classifying genuine accounts as TFP and E13and fake accounts as INT, TWT, and FSF. In this section, the author talks about neural networks, LSTM, XG Boost, and Random Forest. Theimportant traits are picked to judge the veracity of a social media page. The architecture and hyperparameters are also discussed. Lastly, after themodels have been trained, results are generated. As a result, the output is0 for true profiles and 1 for fake profiles. It is possible to disable or deletea fake profile when it is found, preventing cyber security issues.