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Recent studies in social media spam and automation provide anecdotalargumentation of the rise of a new generation of spambots, so-called socialspambots. Here, for the first time, we extensively study this novel phenomenonon Twitter and we provide quantitative evidence that a paradigm-shift exists inspambot design. First, we measure current Twitter's capabilities of detectingthe new social spambots. Later, we assess the human performance indiscriminating between genuine accounts, social spambots, and traditionalspambots. Then, we benchmark several state-of-the-art techniques proposed bythe academic literature. Results show that neither Twitter, nor humans, norcutting-edge applications are currently capable of accurately detecting the newsocial spambots. Our results call for new approaches capable of turning thetide in the fight against this raising phenomenon. We conclude by reviewing thelatest literature on spambots detection and we highlight an emerging commonresearch trend based on the analysis of collective behaviors. Insights derivedfrom both our extensive experimental campaign and survey shed light on the mostpromising directions of research and lay the foundations for the arms raceagainst the novel social spambots. Finally, to foster research on this novelphenomenon, we make publicly available to the scientific community all thedatasets used in this study.

The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race