We have entered the era of social media networks repre-sented by Facebook, Twitter, YouTube and Flickr. Internet users now spend more time on social networks than search engines. Business entities or public -gures set up social networking pages to enhance direct interactions with on-line users. Social media systems heavily depend on users for content contribution and sharing. Information is spread across social networks quickly and e®ectively. However, at the same time social media networks become susceptible to di®erent types of unwanted and malicious spammer or hacker actions. There is a crucial need in the society and in-dustry for security solution in social media. In this demo, we propose SocialSpamGuard, a scalable and online social me-dia 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.
|Original language||English (US)|
|Number of pages||4|
|Journal||Proceedings of the VLDB Endowment|
|State||Published - Aug 1 2011|
ASJC Scopus subject areas
- Computer Science (miscellaneous)
- Computer Science(all)