The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
The socialbot network: when bots socialize for fame and money
Abstract
Online Social Networks (OSNs) have become an integral part of today's Web. Politicians, celebrities, revolutionists, and others use OSNs as a podium to deliver their message to millions of active web users. Unfortunately, in the wrong hands, OSNs can be used to run astroturf campaigns to spread misinformation and propaganda. Such campaigns usually start off by infiltrating a targeted OSN on a large scale. In this paper, we evaluate how vulnerable OSNs are to a large-scale infiltration by socialbots: computer programs that control OSN accounts and mimic real users. We adopt a traditional web-based botnet design and built a Socialbot Network (SbN): a group of adaptive socialbots that are orchestrated in a command-and-control fashion. We operated such an SbN on Facebook---a 750 million user OSN---for about 8 weeks. We collected data related to users' behavior in response to a large-scale infiltration where socialbots were used to connect to a large number of Facebook users. Our results show that (1) OSNs, such as Facebook, can be infiltrated with a success rate of up to 80%, (2) depending on users' privacy settings, a successful infiltration can result in privacy breaches where even more users' data are exposed when compared to a purely public access, and (3) in practice, OSN security defenses, such as the Facebook Immune System, are not effective enough in detecting or stopping a large-scale infiltration as it occurs.
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Citations
Cites
Users of the world, unite! The challenges and opportunities of Social Media
Kaplan, A. M., & Haenlein, M. (2010) · Business Horizons · doi:10.1016/j.bushor.2009.09.003 · Cited by 17,875
Ellison, N. B., Steinfield, C., & Lampe, C. (2007) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2007.00367.x · Cited by 9,939
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206
Douceur, J. R. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-45748-8_24 · Cited by 4,419
A survey of socially interactive robots
Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(02)00372-x · Cited by 3,162 · cited 1×
“This is achieved by either simply mimicking the actions of a real OSN user or by simulating such a user using artificial intelligence, just as in social robotics [24].”
Measurement and analysis of online social networks
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007) · Proceedings of the 7th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1298306.1298311 · Cited by 3,057
Information revelation and privacy in online social networks
Gross, R., & Acquisti, A. (2005) · Proceedings of the 2005 ACM workshop on Privacy in the electronic society · doi:10.1145/1102199.1102214 · Cited by 2,028 · cited 1×
“Even though adversarial surveillance, such as online profiling [28], is a serious online privacy concern, we decided to only focus on users’ data that have monetary value such as Personally Identifiable Information (PII).”
BackgroundDetecting spammers on social networks
Stringhini, G., Kruegel, C., & Vigna, G. (2010) · Proceedings of the 26th Annual Computer Security Applications Conference · doi:10.1145/1920261.1920263 · Cited by 791
Tong, S. T., Van Der Heide, B., Langwell, L., & Walther, J. B. (2008) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2008.00409.x · Cited by 732
Ratkiewicz (2011) · doi:10.1145/1963192.1963301 · Cited by 541
Cited by
Online Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 981
“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).”
BackgroundAdvanced social engineering attacks
Krombholz, K., Hobel, H., Huber, M., & Weippl, E. (2015) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2014.09.005 · Cited by 575
Fame for sale: Efficient detection of fake Twitter followers
Cresci, S., Di Pietro, R., Petrocchi, M., Spognardi, A., & Tesconi, M. (2015) · Decision Support Systems · doi:10.1016/j.dss.2015.09.003 · Cited by 475
“, computer programs that control social accounts, as stealthy as to mimic real users [9]), or cyborgs (i.”
BackgroundSensing Trending Topics in Twitter
Aiello, L. M., Petkos, G., Martin, C., Corney, D., Papadopoulos, S., Skraba, R., Goker, A., Kompatsiaris, I., & Jaimes, A. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2265080 · Cited by 431
Fake news, social media and marketing: A systematic review
Domenico, G. D., Sit, J., Ishizaka, A., & Nunan, D. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.037 · Cited by 431
Metadata
- Authors
- Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M.
- Venue
- Proceedings of the 27th Annual Computer Security Applications Conference
- Publisher
- ACM
- Published
- 2011-12-05
- Pages
- 93-102
- ISSN
- 1063-9527 (ISSN-L)
- OpenAlex
- W1992685726
- Cited by
- 436
- References
- 11
- FWCI
- 25.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2011). The socialbot network: when bots socialize for fame and money. Proceedings of the 27th Annual Computer Security Applications Conference. https://doi.org/10.1145/2076732.2076746
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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