T2s Based Social Media Bot Detection Using Machine Learning

Abstract

Recently, Due To The Rapid Development Of Online Social Networks (OSNs) Such As Facebook, Twitter, And Weibo, The Number Of Calculators/social Bots That Imitate Human Users Has Increased. As Artificial Intelligence (AI) Improves, Social Bots Are Getting Smarter And Better At Manipulating People’s Calculation Behaviors. Building A Reliable And Efficient Search Engine Is Crucial To Keeping OSNs Clean And Users Safe. Despite The Rapid Development Of Social Bot Search Platforms, State-of-the-art Systems Still Face Challenges Related To Model Generalization (and Whether It Can Be Adapted To Different Types Of OSNs) And Good Networks. Bots Spread Misinformation And Are Difficult To Detect Based On A Single Piece Of Content, But Advanced Techniques Can Detect Bots With High Accuracy. Social Media Bot Detection Can Use Negative Comments Or Other Scripts To Detect Bots. Social Media Bots Can Target Different Audiences By Creating Fake Models. The Proposed Model For Bot Operated Account Detection Vs. Human Operated Account Detection Method Is Based On Past Tweeting History Of The User. Certain Attributes Such As Friends, Followers Count, And Favorites Were Considered As Features For Designing A Classifier To Detect Bots. In The Proposed Model, The Historical Behavior Based On User Posted Tweets Are The Main Concerned For Detecting All The Accounts.

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