A Taste of Tweet: Reverse Engineering Twitter Spammers

In this paper, through reverse engineering Twitter spammers’ tastes (their preferred targets to spam), we aim at providing guidelines for building more effective social honeypots, and generating new insights to defend against social spammers. Specifically, we first perform a measurement study by deploying “benchmark” social honeypots on Twitter with diverse and fine-grained social behavior patterns to trap spammers. After five months’ data collection, we make a deep analysis on how Twitter spammers find their targets.
Based on the analysis, we evaluate our new guidelines for building effective social honeypots by implementing “advanced” honeypots. Particularly, within the same time period, using those advanced honeypots can trap spammers around 26 times faster than using “traditional” honeypots. In addition, given limited resources/time, a light-weight strategy to prioritize the sampling of more likely spam accounts from the huge Twittersphere is essentially useful in many scenarios (e.g., analyzing spammers’ social behaviors). Applying what we have learned about the tastes of spammers from the relative passive social honeypots, we design two lightweight, guided approaches to prioritize the active sampling of more likely spam accounts in the huge Twittersphere. According to our evaluation, our strategies could efficiently sample/infer over 17,000 spam accounts, with a considerably high “Hit Ratio” (about 0.6 correct
spam account per sampled/inferred account)

Author(s):

Chao Yang    
Texas A&M University
United States

Jialong Zhang    
Texas A&M University
United States

Guofei Gu    
Texas A&M University
United States