Popular Internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses. However, crowd-sourcing systems also pose a real challenge to existing security mechanisms deployed to protect Internet services, particularly those tools that identify malicious activity by detecting activities of automated programs such as CAPTCHAs. In this work, we leverage access to two large crowdturfing sites to gather a large corpus of ground-truth data generated by crowdturfing campaigns. We compare and contrast this data with "organic" content generated by normal users to identify unique characteristics and potential signatures for use in real-time detectors. This poster describes first steps taken focused on crowdturfing campaigns targeting the Sina Weibo microblogging system. We describe our methodology, our data (over 290K campaigns, 34K worker accounts, 61 million tweets...), and some initial results.

Microblogging services, such as Twitter, are among the most important online social networks(OSNs). Different from OSNs such as Facebook, the topology of microblogging service is a directed graph instead of an undirected graph. Recently, due to the explosive increase of population size, graph sampling has started to play a critical role in measurement and characterization studies of such OSNs. However, previous studies have only focused on the unbiased sampling of undirected social graphs. In this paper, we study the unbiased sampling algorithm for directed social graphs. Based on the traditional Metropolis-Hasting Random Walk (MHRW) algorithm, we propose an unbiased sampling method for directed social graphs(USDSG). Using this method, we get the first, to the best of our knowledge, unbiased sample of directed social graphs. Through extensive experiments comparing with the "ground truth" (UNI, obtained through uniform sampling of directed graph nodes), we show that our method can achieve excellent performance in directed graph sampling and the error to UNI is less than 10%.