# The DARPA Twitter Bot Challenge

arXiv: https://arxiv.org/abs/1601.05140 · HTML: https://mlsanity.com/paper/1601.05140/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2016.

## Built on by (most cited first, 15 of 15)

- [Adversarial Socialbots Modeling Based on Structural Information Principles](https://mlsanity.com/paper/2312.08098/) (2023)
- [BotDGT: Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers](https://mlsanity.com/paper/2404.15070/) (2024)
- [LLMs Among Us: Generative AI Participating in Digital Discourse](https://mlsanity.com/paper/2402.07940/) (2024)
- [Demystifying Misconceptions in Social Bots Research](https://mlsanity.com/paper/2303.17251/) (2023)
- [Studying Fake News Spreading, Polarisation Dynamics, and Manipulation by Bots: a Tale of Networks and Language](https://mlsanity.com/paper/2109.07909/) (2021)
- [Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2110.10655/) (2021)
- [Identification of Twitter Bots Based on an Explainable Machine Learning Framework: The US 2020 Elections Case Study](https://mlsanity.com/paper/2112.04913/) (2021)
- [Selecting and combining complementary feature representations and classifiers for hate speech detection](https://mlsanity.com/paper/2201.06721/) (2022)
- [False Information, Bots and Malicious Campaigns: Demystifying Elements of Social Media Manipulations](https://mlsanity.com/paper/2308.12497/) (2023)
- [A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma](https://mlsanity.com/paper/2101.00480/) (2021)
- [Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly](https://mlsanity.com/paper/2405.18845/) (2024)
- [Political Communities on Twitter: Case Study of the 2022 French Presidential Election](https://mlsanity.com/paper/2204.07436/) (2022)
- [Russo-Ukrainian War: Prediction and explanation of Twitter suspension](https://mlsanity.com/paper/2306.03502/) (2023)
- [Public interest in science or bots? Selective amplification of scientific articles on Twitter](https://mlsanity.com/paper/2410.01842/) (2024)
- [CSC: Calibrated Simplicity for Conflict-Aware Social Bot Detection in the LLM Era](https://mlsanity.com/paper/2609.23320/) (2026)

## 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.