  Twitter has increasingly become a popular platform to share news and user
opinion. A tweet is considered to be important if it receives high number of
affirmative reactions from other Twitter users via Retweets. Retweet count is
thus considered as a surrogate measure for positive crowd-sourced reactions -
high number of retweets of a tweet aid in making its topic trending. This in
turn bolsters the social reputation of the author of the tweet. Since social
reputation/impact of users/tweets influences many decisions (such as promoting
brands, advertisement etc.), several blackmarket syndicates have actively been
engaged in producing fake retweets in a collusive manner. Users who want to
boost the impact of their tweets approach the blackmarket services, and gain
retweets for their own tweets by either paying money (Premium Services) or by
retweeting other customers' tweets. Thus they become customers of blackmarket
syndicates and engage in fake activities. Interestingly, these customers are
neither bots, nor even fake users - they are usually normal human beings; they
express a mix of organic and inorganic retweeting activities, and there is no
synchronicity across their behaviors. In this paper, we make a first attempt to
investigate such blackmarket customers engaged in producing fake retweets. We
collected and annotated a novel dataset comprising of customers of many
blackmarket services and show how their social behavior differs from genuine
users. We then use state-of-the-art supervised models to detect three types of
customers (bots, promotional, normal) and genuine users. We achieve a Macro
F1-score of 0.87 with SVM, outperforming four other baselines significantly. We
further design a browser extension, SCoRe which, given the link of a tweet,
spots its fake retweeters in real-time. We also collected users' feedback on
the performance of SCoRe and obtained 85% accuracy.
