Detecting Malicious Users in Twitter using Classifiers
Monika Singh, Divya Bansal, Sanjeev Sofat · 2014
The web has become a vital global platform that binds together almost all daily activities like communication, sharing, and collaboration. Impersonators, phishers, scammers and spammers crop up all the time in Online Social Networks (OSNs), and are even harder to identify. People in the public eyes like politicians, celebrities, sports persons, media persons and other public figures with huge followings are particularly vulnerable to this type of attacks. The main objective in this work is to identify those forged users who harm genuine ones, jeopardize the identity and hence the security and privacy of users. In this paper a framework for the detection of malicious users, non-malicious users and celebrities has been developed by using an attribute set for user classification based on user characteristics. For the purpose of detecting malicious users, non-malicious users and celebrities, a crawler has been developed for Twitter and data of around 22K users have been collected from publicly available information. Data of around 7,500 users have been used for training and testing purpose in Weka for classification of users. 5 classifiers have been used and compared on the basis of performance metrics like precision, recall, F-measure and accuracy. RandomForest outperforms all the classifiers with 99.8% accuracy.