The Paradigm-Shift of Social Spambots: Evidence, Theories, and Tools for the Arms Race

By Stefano Cresci, Roberto Di Pietro, Marinella Petrocchi, Angelo Spognardi, Maurizio Tesconi. The Web Conference, 2017

522 citations recorded by the source corpus.

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.

Subfields: Information Systems, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bd76d414c9770a7039e9936e30a7030c127efa40

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Citations in the corpus

Arming the public with artificial intelligence to counter social bots (2019, background): “While supervised methods have proven to be eﬀective in many cases, they do not perform well at detecting coordinated social bots that post human-generated content (Cresci et al., 2017; Grimme et al., 2018; Chen and Subramanian, 2018).”
Bots increase exposure to negative and inflammatory content in online social systems (2018, unknown): “The former training dataset is associated with Botometer [2,7]; the latter one is a labeled dataset provided by Cresci and collaborators [16].”
Contrast Pattern-Based Classification for Bot Detection on Twitter (2019, methodology): “Account Information: an account information feature is extracted from the Twitter account that has been allegedly used to post a message [14].”
Scalable and Generalizable Social Bot Detection through Data Selection (2019, background): “The bot accounts in the cresci-17 dataset contain a more fine-grained classification: traditional spambots, social spambots, and fake followers (Cresci et al. 2017).”
Deep Neural Networks for Bot Detection (2018, methodology): “It has been observed by Cresci et al. [14] that using simple textual features, as seen in prior work [42], reveals ineffective against the more sophisticated social bots we study in this work.”
A decade of social bot detection (2020, background): “th the recent rise of the adversarial ones. they share common goals. As such, botnets leave behind more traces of their automation and coordination than those left behind by sophisticated single bots [5]. Devising techniques for spotting suspiciously coordinated and synchronized behaviors is thus li”