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A Quantum Beta Distributed Multi-Objective Particle Swarm Optimization Algorithm for Twitter Fake Accounts Detection

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posted on 04.04.2022, 04:42 authored by Ahlem AboudAhlem Aboud, Nizar Rokbani, Seyedali MirjaliliSeyedali Mirjalili, Abdulrahman M. Qahtani, Fahd S. Alharithi, Omar Almutiry, Amir Hussain, Adel M. Alimi

Fake account detection is a topical issue when many Online Social Networks (OSNs) encounter problems caused by a growing number of unethical online social activities. This study presents a new Quantum Beta-Distributed Multi-Objective Particle Swarm Optimization (QBD-MOPSO) system to detect fake accounts on Twitter. The proposed system aims to minimize two objective functions simultaneously: specifically features dimensionality and classification error rate. The QBD-MOPSO has two optimization profiles: the first uses a quantum behaved equation for improving the exploratory behaviour of PSO, while the second uses a beta function to enhance PSO’s exploitation. Six variants of the QBD-MOPSO approach are proposed to account for various data distribution types. The QBD-MOPSO system provides a feature selection technique based on the sigmoid function for position binary encoding. Each particle has a binary vector as a potential solution for feature subset selection, and a bit with the value of “1” indicating selection of a feature and “0” otherwise. Machine learning based classification models are trained and tested using a subset of selected features. An extensive experimental study is carried using two benchmark Twitter datasets with 1982 and 928 accounts. From 46 original features, QBD-MOPSO has selected 32 and 25 pertinent features and accurately classified 99.19% and 97.52% account on the datasets.

History

Email Address of Submitting Author

ahlem.aboud@regim.usf.tn

ORCID of Submitting Author

0000-0003-3915-7104

Submitting Author's Institution

University of Sousse, ISITCom

Submitting Author's Country

Tunisia