In this paper we investigate to what extent the trending topics in Twitter, a popular social network, are manipulated by spammers. Researchers have developed various models for spam detection in social media, but there has been little analysis on the effects of spam on Twitter's trending topics. We gathered over 9 million tweets in Twitter's hourly trending topics over a 7 day period and extracted tweet features identified by previous research as relevant to spam detection. Hand-labeling a random sample of 1500 tweets allowed us to train a moderately accurate naive Bayes classifier for tweet classification. Our findings suggest that spammers do not drive the trending topics in Twitter, but may opportunistically target certain topics for their messages.
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Stafford et al. (2013) studied this question.
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