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BotOrNot: A System to Evaluate Social Bots

Authors:

Filippo MenczerAuthors Info & Claims

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

Pages 273 - 274

https://doi.org/10.1145/2872518.2889302

Published: 11 April 2016 Publication History

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Abstract

While most online social media accounts are controlled by humans, these platforms also host automated agents called social bots or sybil accounts. Recent literature reported on cases of social bots imitating humans to manipulate discussions, alter the popularity of users, pollute content and spread misinformation, and even perform terrorist propaganda and recruitment actions. Here we present BotOrNot, a publicly-available service that leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots. Since its release in May 2014, BotOrNot has served over one million requests via our website and APIs.

References

[1]

Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu. Design and analysis of a social botnet. Computer Networks, 57(2):556--578, 2013.

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[2]

E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini. The rise of social bots. Commun. ACM, in press. Preprint arXiv:1407.5225.

Google Scholar

[3]

K. Lee, B. D. Eoff, and J. Caverlee. Seven months with the devils: A long-term study of content polluters on Twitter. In Proc. 5th AAAI Intl. Conf. on Web and Social Media (ICWSM), pages 185--192, 2011.

Google Scholar

[4]

J. Ratkiewicz, M. Conover, M. Meiss, B. Gonçalves, S. Patil, A. Flammini, and F. Menczer. Truthy: mapping the spread of astroturf in microblog streams. In Proc. 20th ACM Intl. World Wide Web Conf. Companion (WWW), pages 249--252, 2011.

Digital Library

Google Scholar

[5]

V. Subrahmanian, A. Azaria, S. Durst, V. Kagan, A. Galstyan, K. Lerman, L. Zhu, E. Ferrara, A. Flammini, F. Menczer, et al. The darpa twitter bot challenge. arXiv preprint arXiv:1601.05140, 2016.

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Cited By

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Lingam GDas S(2025)Social bot detection using variational generative adversarial networks with hidden Markov models in Twitter networkKnowledge-Based Systems10.1016/j.knosys.2025.113019311(113019)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.113019
Huang DSong JZhang X(2025)Semi-Supervised Social Bot Detection with Relational Graph Attention Transformers and Characteristics of the social environmentInformation Fusion10.1016/j.inffus.2025.102956(102956)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2025.102956
AlKulaib LLu C(2025)HyperSMOTE-MC: Enhancing Multiclass Bot Detection on X Through Hypergraph-Based ResamplingSocial Networks Analysis and Mining10.1007/978-3-031-78554-2_11(171-186)Online publication date: 25-Jan-2025
https://doi.org/10.1007/978-3-031-78554-2_11
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Index Terms

BotOrNot: A System to Evaluate Social Bots
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Information & Contributors

Information

Published In

WWW '16 Companion: Proceedings of the 25th International Conference Companion on World Wide Web

April 2016

1094 pages

ISBN:9781450341448

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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DARPA
NSF
J.S. McDonnell Foundation

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WWW '16

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IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

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WWW '16 Companion Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Lingam GDas S(2025)Social bot detection using variational generative adversarial networks with hidden Markov models in Twitter networkKnowledge-Based Systems10.1016/j.knosys.2025.113019311(113019)Online publication date: Feb-2025
https://doi.org/10.1016/j.knosys.2025.113019
Huang DSong JZhang X(2025)Semi-Supervised Social Bot Detection with Relational Graph Attention Transformers and Characteristics of the social environmentInformation Fusion10.1016/j.inffus.2025.102956(102956)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2025.102956
AlKulaib LLu C(2025)HyperSMOTE-MC: Enhancing Multiclass Bot Detection on X Through Hypergraph-Based ResamplingSocial Networks Analysis and Mining10.1007/978-3-031-78554-2_11(171-186)Online publication date: 25-Jan-2025
https://doi.org/10.1007/978-3-031-78554-2_11
Rodriguez COppenheimer D(2024)Creating a Bot-tleneck for malicious AI: Psychological methods for bot detectionBehavior Research Methods10.3758/s13428-024-02357-956:6(6258-6275)Online publication date: 1-Apr-2024
https://doi.org/10.3758/s13428-024-02357-9
Sidorov MHadar OVilenchik D(2024)Revisiting Information Cascades in Online Social NetworksMathematics10.3390/math1301007713:1(77)Online publication date: 28-Dec-2024
https://doi.org/10.3390/math13010077
Schler JBonchek-Dokow E(2024)Profiling Astroturfers on Facebook: A Complete Framework for Labeling, Feature Extraction, and ClassificationMachine Learning and Knowledge Extraction10.3390/make60401086:4(2183-2200)Online publication date: 30-Sep-2024
https://doi.org/10.3390/make6040108
Tzoumanekas GChatzianastasis MIlias LKiokes GPsarras JAskounis D(2024)A graph neural architecture search approach for identifying bots in social mediaFrontiers in Artificial Intelligence10.3389/frai.2024.15091797Online publication date: 20-Dec-2024
https://doi.org/10.3389/frai.2024.1509179
O'Brien GGanjigunta RDhillon P(2024)Wellness Influencer Responses to COVID-19 Vaccines on Social Media: A Longitudinal Observational StudyJournal of Medical Internet Research10.2196/5665126(e56651)Online publication date: 27-Nov-2024
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Taban MGür İ(2024)Social Media as an Agent of Influence: Twitter Bots in Russia - Ukraine WarGüvenlik Stratejileri Dergisi10.17752/guvenlikstrtj.139670520:47(99-122)Online publication date: 26-Apr-2024
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Skorupka A(2024)Detecting anomalies in graph networks on digital marketsPLOS ONE10.1371/journal.pone.031584919:12(e0315849)Online publication date: 23-Dec-2024
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Twitter turing test

Social Bot Detection on 2019 Indonesia President Candidate’s Supporter’s Tweets

Understanding a prospective approach to designing malicious social bots

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Twitter turing test

Social Bot Detection on 2019 Indonesia President Candidate’s Supporter’s Tweets

Understanding a prospective approach to designing malicious social bots

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