Malicious Detection in Social Media

Abstract

We have entered the era of social media networks represented by Facebook, Twitter, YouTube and Flickr. Internet users now spend more time on social networks than search engines. Business entities or public figures set up social networking pages to enhance direct interactions with online users. Social media systems heavily depend on users for content contribution and sharing. Information is spread across social networks quickly and effectively. However, at the same time social media networks become susceptible to different types of unwanted and malicious spammer or hacker actions. There is a crucial need in the society and industry for security solution in social media. In this demo, we propose Social Spam Guard; a scalable and online social media spam detection system based on data mining for social network security. We employ our GAD clustering algorithm for large scale clustering and integrate it with the designed active learning algorithm to deal with the scalability and real-time detection challenges.