Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter

By Chao Yang, Robert Chandler Harkreader, Jialong Zhang, Seungwon Shin, Guofei Gu. 2012

345 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Neil Zhenqiang Gong
2. Binghui Wang
3. Kurt Thomas
4. Prateek Mittal
5. Chris Grier
6. Vern Paxson
7. Damon McCoy
8. Chao Yang

In Sociology and Political Science

1. Prateek Mittal
2. Neil Zhenqiang Gong
3. Vern Paxson
4. Mario Frank

In Statistical and Nonlinear Physics

1. Neil Zhenqiang Gong
2. Prateek Mittal
3. Chao Yang
4. Robert Chandler Harkreader
5. Mario Frank
6. Kurt Thomas
7. Guofei Gu
8. Binghui Wang

Citations in the corpus

• Exploiting Temporal Dynamics in Sybil Defenses (2015, neutral): “firmation [33, 23, 21].”
• Trafficking Fraudulent Accounts: The Role of the Underground Market in Twitter Spam and Abuse (2013, background): “With no central operation of the underground market, we resort to investigating common haunts: advertisements via search engines, blackhat forums such as blackhat-world.com , and freelance labor pages including Fiverr and Freelancer [20, 21].”
• Empirical Evaluation and New Design for Fighting Evolving Twitter Spammers (2011, background): “Furthermore, if spammer accounts tend to follow with each other, they could be identified by existing sybil-attack detection approaches (e.g., SybilGuard [38] ) or relationship analysis approaches (e.g., [39]).”
• SybilBelief: A Semi-Supervised Learning Approach for Structure-Based Sybil Detection (2013, methodology): “, SybilRank [13] and Criminal account Inference Algorithm [14].”