Application of multi-criterion decision-making methods for bot classification in social networks
DOI:
https://doi.org/10.17721/2706-9699.2025.2.03Keywords:
social networks, bots, multi-criteria decision-making, TOPSISAbstract
The aim of the article is to develop a methodology for classifying social network accounts into «bot», «non-bot», and «suspicious» categories using Multi-Criteria Decision-Making methods (MCDM).
Research methodology. The study employs a hybrid MCDM approach, combining the Analytic Hierarchy Process (AHP) and entropy method to determine feature weights, and the TOPSIS method for final classification. The criteria integrate behavioral, structural, attributive, and content-based features.
Results of the research. The proposed model was tested on a synthetic dataset of 100 accounts. It demonstrated high effectiveness, achieving 90% classification accuracy, with a precision of 0.85 and a recall of 0.89. The results confirm the model’s ability to reliably detect bots while minimizing false classifications of genuine users.
Practical significance. The developed methodology provides a transparent, explainable, and adaptable tool for bot detection that can be integrated into social network monitoring systems, digital security tools, and information analytics platforms without the need for complete model retraining.
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