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