When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks

By Claudia Wagner, Silvia Mitter, Christian Körner, M. Strohmaier. 2012

169 citations recorded by the source corpus.

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Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “Similarly, non-human accounts (discussed in section 4.4) often have anomalous content production behavior, but despite not being “normal” accounts, they can inﬂuence the behavior of “normal” users (Wagner et al., 2012), and ﬁltering them out may hide important signals.”
The rise of social bots (2014, background): “…some groups are trying to reverse-engineer social bots to understand their functioning [Freitas et al. 2014], while others are creating bots themselves [Hwang et al. 2012; Briscoe et al. 2014] to study the susceptibility of people to their inﬂuence [Boshmaf et al. 2011; Wagner et al. 2012].”
The anatomy of online deception: what makes automated text convincing? (2016, background): “[17] modelled the susceptibility of users on Twitter to automated content about cats, finding that susceptible users tended to be more socially active.”
Integro: Leveraging Victim Prediction for Robust Fake Account Detection in OSNs (2015, background): “These victims use Twitter to promote themselves or their business by actively following other accounts without consideration.”