Social Media Bot Detection Using Machine Learning Approach

Nowadays, social media platforms are thronged with social bots spreading misinformation. Twitter has become the hotspot for social bots. These bots are either automated or semi-automated, spreading misinformation purposefully or not purposefully is influencing society's perspective on different aspects of life. This tremendous increase in social bots has aroused huge interest in researchers. In this paper, we have proposed a social bot detection model using Random Forest Classifier, we also used Extreme Gradient Boost Classifier, Artificial Neural Network, and Decision Tree Classifier on the top 8 attributes, which are staunch. The attribute is selected after analyzing the preprocessed data set taken from Kaggle which contains 37446 Twitter accounts having both human and bots. The overall accuracy of the proposed model is above 83%. The result demonstrated that the model is feasible for high-accuracy social bot detection.