Detecting Social Spam Profile on Twitter

Abstract

The fast development of social networking sites such as imparting, sharing, putting away and overseeing huge data leads to pull in cybercriminals. Spammers misuse these social networking sites to abuse cyber laws for their unlawful arts. They start with email, and then quickly spread to new advancements, for example, texting, newsgroups and smart phones. As online social networks, for example, MySpace, Facebook and Twitter turned out to be progressively well known, spammers rapidly found another home for their spamming purposes. Spamming activities of social spammers not only causes dangerous for normal social network users but also annoys to these users. The aim of this paper is to develop social spammer detection approach with low cost and low overhead. The detection approach is a three-phase process: (1) features extraction, (2) features selection and (3) classification. Validation of this approach is tested with 1KS-10KN dataset and CRESCI-2015 dataset.