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Quantum behaved binary gravitational search algorithm with random forest for twitter spammer detection
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DOI：10.1016/j.rineng.2025.103993.png)
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Abstract
En 中文
The emergence of social media platforms like Twitter has significantly changed the landscape of communication by increasing accessibility for widely disseminating official announcements, professional interactions, and important news in real-time. Despite these advantages, the prevalence of spammers and their spamming activities is increasing regularly. To mitigate the growing number of spammers, it is essential to develop an efficient and robust method for Twitter spammer detection. This research presents a novel QBGSRF method by combining the quantum-behaved binary gravitational search algorithm (QBGSA) with random forest (RF) for timely detection of Twitter spammers. The QBGSA algorithm adds the characteristics of quantum computing (QC) and binary gravitational search algorithm (BGSA), which enables the quantum agents to quickly determine solutions using the superposition attributes of QC and the position update via bit-flipping based on velocity probabilities of the BGSA algorithm. In the proposed QBGSRF method, the quantum agents utilize the aforementioned attributes and the principles of the RF algorithm to construct the decision trees for effectively detecting Twitter spammers. The proposed method is assessed for the datasets of 1KS-10KN and Social Honeypot. In order to access the efficacy of the proposed method, the results are also evaluated using the BGSRF method (a combination of BGSA and RF algorithm) and RF algorithm. The experimental evaluations indicate that the proposed method outperforms the aforementioned and state-of-the-art methods.
Keywords:
Twitter
Twitter spammer detection
Gravitational search algorithm
Quantum computing
Binary gravitational search algorithm
Random forest
Machine learning
Metaheuristic
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