1,2Department of CSE, KL University, Vaddeswaram, Andhra Pradesh, India
1Email: tkrishnan.mtech@kluniversity.in
Social networking, such as X (formerly Twitter), is the most effective way of having a large human interaction, but it is currently being overwhelmed with automated accounts that simulate human behavior and spread misinformation and manipulate opinions. The detection of such spambots is crucial in ensuring the integrity of information but many of the conventional techniques are based on opaque, black-box models that cause one to contemplate what is happening. Data used in this study is Cresci-15 and Cresci-17 to analyze interpretable ML techniques towards the identification of spambots and fraudulent followers. Both text and feature-based data are utilised and the preprocessing methods of normalisation, tokenization and removal of extraneous information are applied. RFE is a feature dimensionality reduction method that utilizes recursive selection, and resampling algorithms such as SMOTE and SMOTEENN are used to correct the issue of class imbalance. DT, RF, SVM, NB, XGBoost, AdaBoost, Stacking Classifier, and Voting Classifier are some of the various ML techniques evaluated. The findings show that the Stacking Classifier is highly accurate, achieving 99.9% on the Cresci-15 dataset and 99.5% on the Cresci-17 dataset. Moreover, explainable AI models such as LIME and SHAP allow one to visualize the importance of each feature, thus enhancing model transparency and making it easier to make decisions. These results emphasize the effectiveness of incorporating the feature selection method, advanced resampling as well as ensemble learning techniques along with the interpretable means to the dependable determination of the automated accounts in the social networks.
Keywords: Interpretable AI, social network, bot detection, fake followers, spambots.
How to cite this article: Krishnan T, Kumar JP. Identification of Spambots and Fake Followers on Social Network via Interpretable AI-Based Machine Learning. Int J Drug Deliv Technol. 2026;16(10s): 53-60; DOI: 10.25258/ijddt.16.10s.7
Source of support: Nil.
Conflict of interest: None