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

TL;DR

This paper provides extensive evidence that social spambots on Twitter have evolved into a new paradigm, challenging current detection methods and highlighting the need for innovative approaches in the ongoing arms race.

Contribution

It offers the first comprehensive study of social spambots, evaluates current detection capabilities, and proposes new research directions based on collective behavior analysis.

Findings

Twitter's detection methods are ineffective against social spambots.

Humans struggle to distinguish social spambots from genuine accounts.

Existing state-of-the-art techniques do not reliably detect social spambots.

Abstract

Recent studies in social media spam and automation provide anecdotal argumentation of the rise of a new generation of spambots, so-called social spambots. Here, for the first time, we extensively study this novel phenomenon on Twitter and we provide quantitative evidence that a paradigm-shift exists in spambot design. First, we measure current Twitter's capabilities of detecting the new social spambots. Later, we assess the human performance in discriminating between genuine accounts, social spambots, and traditional spambots. Then, we benchmark several state-of-the-art techniques proposed by the academic literature. Results show that neither Twitter, nor humans, nor cutting-edge applications are currently capable of accurately detecting the new social spambots. Our results call for new approaches capable of turning the tide in the fight against this raising phenomenon. We conclude by reviewing the latest literature on spambots detection and we highlight an emerging common research trend based on the analysis of collective behaviors. Insights derived from both our extensive experimental campaign and survey shed light on the most promising directions of research and lay the foundations for the arms race against the novel social spambots. Finally, to foster research on this novel phenomenon, we make publicly available to the scientific community all the datasets used in this study.