Abstract:
This research presents a comprehensive exploration of social media bot detection, focusing on
Instagram, Twitter, and TikTok platforms. Social bots are autonomous machines that create for
social media platforms. In this work present supervised machine learning algorithms, including
models Random Forest, XGBoost, KNN, Decision Tree, and Neural Network, I am conducted
an extensive analysis of bot behavior. Collect all public available datasets of labeled human and
bot accounts. And through this study we have analyzed the behavioral activities of different bots
accounts. The study aimed to understand the impact of multiple bot accounts, enhance accuracy
through multi-account analysis, and deploy supervised machine learning technologies for
improved platform security. In this research evaluated algorithms performance using key metrics
such as accuracy, precision, recall, and F1-score, considering platform-specific challenges.
Random Forest and XGBoost three-type accounts are consistently robust performers. Random
Forest Instagram accuracy is 95%, Twitter accuracy is 91%, and TikTok accuracy is 97%, with
the highest prediction accuracy. XGBoost Instagram accuracy is 95%, Twitter accuracy is 90%,
and TikTok accuracy is 97%. All models achieve high accuracy, emphasizing the effectiveness
of bot detection algorithms on the TikTok social media platform. The Neural Network maintains
stable results with a balanced precision-recall trade-off, showing flexibility. Overall, the study
provides valuable knowledge about social media bot detection, highlighting Random Forest and
XGBoost as reliable choices. It gives strong user privacy and data security and opens the way for
future research, inspiring exploration into ensemble approaches and advanced techniques.
