Hybrid Feature Selection Framework for Machine Learning-based Bot Detection on Social Media

Nowadays, social media impact all aspects of our lives, making us vulnerable to fraud and scams. Bots are believed to be the most prevalent form of malware that may be found in social media environments. New detection methods are required to keep up with the pace of their continuous advancement. This paper offers an overview of machine learning-based bot detection methods. The study revealed that the effectiveness of machine learning (ML) models can be significantly hindered by redundant and irrelevant features present in the datasets, which can lead to performance degradation. A hybrid feature selection (FS) combining characteristics of the genetic algorithm (GA) and the mutual information (MI) approach is proposed to overcome this challenge. The proposed method is evaluated using the following approaches: random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression (LR). Compared to the state-of-the-art models, the proposed method is capable of efficiently identifying bots using only a small number of features. For the dataset used, we achieved a classification accuracy of 0.99 using 4 features only.