International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Identification of Spambots and Fake Followers on Social Network Via Interpretable AI-Based Machine Learning |
|---|---|
| ABSTRACT | The rapid growth of social networking platforms has increased communication and information sharing while also creating opportunities for malicious activities such as spambots and fake followers. These automated accounts spread spam, misinformation, and fraudulent content, reducing the credibility and security of online communities. Traditional rule-based detection techniques often fail to identify sophisticated spambots due to their continuously evolving behavior and limited adaptability. This paper proposes an intelligent machine learning-based framework for identifying spambots and fake followers on social networks. The proposed system performs data preprocessing, feature engineering, and feature selection before applying multiple classification algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). In addition, Explainable Artificial Intelligence (XAI) techniques such as SHAP and LIME are incorporated to improve the transparency and interpretability of the classification results. Experimental evaluation demonstrates that the CNN model outperforms the other classifiers, achieving an overall accuracy of approximately 94% with high precision, recall, and F1-score. The proposed framework provides an accurate, scalable, and interpretable solution for automated spambot detection, helping social media platforms identify malicious accounts and improve the security and reliability of online social networks. |
| AUTHOR | DR.T.V.S SRIRAM, G.KAVYA Sr. Associate Processor, Dept. of CSE, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 186 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1407065 |
| pdf/65_Identification of Spambots and Fake Followers on Social Network Via Interpretable AI-Based Machine Learning.pdf | |
| KEYWORDS | |
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