Abstract:
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 activ
ities 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 ef
f
icacy 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 out
performs the aforementioned and state-of-the-art methods.
