mlsanityData to 7 Oct 2026

The DARPA Twitter Bot Challenge

arXiv 1601.05140 ↗First posted 20 Jan 2016cs.SI cs.AI cs.CYcited by 15

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Abstract (arXiv)

A number of organizations ranging from terrorist groups such as ISIS to politicians and nation states reportedly conduct explicit campaigns to influence opinion on social media, posing a risk to democratic processes. There is thus a growing need to identify and eliminate "influence bots" - realistic, automated identities that illicitly shape discussion on sites like Twitter and Facebook - before they get too influential. Spurred by such events, DARPA held a 4-week competition in February/March 2015 in which multiple teams supported by the DARPA Social Media in Strategic Communications program competed to identify a set of previously identified "influence bots" serving as ground truth on a specific topic within Twitter. Past work regarding influence bots often has difficulty supporting claims about accuracy, since there is limited ground truth (though some exceptions do exist [3,7]). However, with the exception of [3], no past work has looked specifically at identifying influence bots on a specific topic. This paper describes the DARPA Challenge and describes the methods used by the three top-ranked teams.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 2; 2023: 4; 2024: 4; 2025: 0; 2026: 1.

15 papers.

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  1. 2023Adversarial Socialbots Modeling Based on Structural Information Principlescited by 10
  2. 2024BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformerscited by 10
  3. 2024LLMs Among Us: Generative AI Participating in Digital Discoursecited by 9
  4. 2023Demystifying Misconceptions in Social Bots Researchcited by 7
  5. 2021Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Languagecited by 6
  6. 2021Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learningcited by 3
  7. 2021Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Studycited by 3
  8. 2022Selecting and combining complementary feature representations and classifiers for hate speech detectioncited by 3
  9. 2023False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulationscited by 3
  10. 2021A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irmacited by 2
  11. 2024Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Uglycited by 2
  12. 2022Political Communities on Twitter: Case Study of the 2022 French Presidential Electioncited by 1
3 more
  1. 2023Russo-Ukrainian War: Prediction and explanation of Twitter suspensioncited by 1
  2. 2024Public interest in science or bots? Selective amplification of scientific articles on Twittercited by 0
  3. 2026CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Eracited by 0