Behavioural analysis and machine learning for social media bot detection: a comparative study of Random Forest, SVC, and Decision Trees

Subaramaniam, Kasthuri and Baker, Oras and Palaniapan, Sellappan and Shah, Umm E Mariya and Su Mon, Chit (2026) Behavioural analysis and machine learning for social media bot detection: a comparative study of Random Forest, SVC, and Decision Trees. In: 31st International Conference on Artificial Life and Robotics 2026, Oita, Japan.

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Abstract

This study investigates the application of behavioural analytics and machine learning to the detection of social media bots, employing a quantitative research design implemented in Python. A labelled dataset of user activity was used to train and evaluate multiple models, including Random Forest, Support Vector Classifier, and Decision Tree algorithms. Model performance was assessed through cross-validation using accuracy, precision, recall, and F1-score metrics. The findings demonstrate that traditional machine learning models, when supported by robust feature engineering, can equal or surpass more complex approaches such as deep learning. The study’s significance lies in advancing scalable, transparent, and computationally efficient frameworks for combating malicious automation on social platforms.

Item Type:	Proceeding Paper (Invited Papers)
Uncontrolled Keywords:	Numerical integration, Multibody dynamics (MBD), analysis error, ordinary differential equation
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Economics and Management Sciences > Department of Finance Kulliyyah of Economics and Management Sciences
Depositing User:	Dr Umm e Mariya Shah
Date Deposited:	04 May 2026 16:53
Last Modified:	04 May 2026 16:53
Queue Number:	2026-04-Q3135
URI:	http://irep.iium.edu.my/id/eprint/128748

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