Journal
WWW'17 COMPANION: PROCEEDINGS OF THE 26TH INTERNATIONAL CONFERENCE ON WORLD WIDE WEB
Volume -, Issue -, Pages 963-972Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3041021.3055135
Keywords
Social spambots; Social networks security; Twitter
Categories
Funding
- EU [654024, 675320]
- Registro.it IIT-CNR project MIB (My Information Bubble)
- Fondazione Cassa di Risparmio di Lucca
- MIUR (Ministero dell'Istruzione, dell'Universita e della Ricerca)
- Regione Toscana (Tuscany, Italy)
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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.
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