The socialbot network: when bots socialize for fame and money

By Yazan Boshmaf, Ildar Muslukhov, Konstantin Beznosov, Matei Ripeanu. Asia-Pacific Computer Systems Architecture Conference, 2011

509 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f5da26aa134f918bf8658e1fd0073cf3331ada36

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Citations in the corpus

Fake Twitter followers detection by denoising autoencoder (2017, background): “Many works already tried to apply machine learning techniques to detect fake profiles on social networks [9], [8], [12], [3], [2], spammers, bots and stolen accounts [24], [30], [22], [4], [19].”
SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, background): “Previous work [8], [21] also showed that Sybil nodes could befriend benign users on Facebook at a large scale.”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, background): “A growing body of research is addressing social bot activity, its implications on the social network, and the detection of these accounts (Lee, Eoff, and Caverlee 2011; Boshmaf et al. 2011; Beutel et al. 2013; Yang et al. 2014; Ferrara et al. 2016a; Chavoshi, Hamooni, and Mueen 2016).”
Aiding the Detection of Fake Accounts in Large Scale Social Online Services (2012, background): “[19] recently showed that the automated Facebook Immune System [50] was able to detect only 20% of the fakes they deployed, and almost all the detected accounts were flagged by users.”
The anatomy of online deception: what makes automated text convincing? (2016, background): “Indeed, a determined actor with resources, such as a controlling government or an ambitious corporation, would be able to achieve a signiﬁcantly higher success rate using more social bots to deceive users into accepting their friend requests [4].”
Sensing Trending Topics in Twitter (2013, background): “Finally, since social media could be used to manipulate the course of online and offline human dynamics [13], [14], [15] (e.g., by augmenting the consensus on politicians for electoral purposes), detecting anomalous activity can help prevent possible misuses of online social platforms.”
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].”
Fame for sale: Efficient detection of fake Twitter followers (2015, background): “, computer programs that control social accounts, as stealthy as to mimic real users [9]), or cyborgs (i.”
The spread of low-credibility content by social bots (2017, unknown): “The level of sophistication of bot-based manipulation can vary greatly [5].”
Advanced social engineering attacks (2015, background): “In this scenario, an attacker exploits this situation and uses the cloud as a channel for the social engineering attack.”