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Sprint projectNov 23, 2025Remote (California, Denmark)

Comparative LLM methods for Social Media Bot Detection

Matthew Pagett, Andreas Raaskov · Team Bot Detection

Submitted to Defensive Acceleration Hackathon. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: Comparative LLM methods for Social Media Bot Detection

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This project examines the potential of LLMs to detect social media bots in (near) real-time, and the potential of using less-advanced LLMs to detect more advanced LLMs. It contributes to the cognitive defense toolset for protecting information ecosystems.

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Does this reduce AI-related catastrophic or existential risks?

Scoring guide
  1. 1Minimal Impact. The project has minimal relevance to AI safety. It doesn't meaningfully address risks uniquely enabled or accelerated by advanced AI systems.
  2. 2Tangential Connection. The project touches on AI safety concepts but lacks depth or specificity. The connection to AI-enabled threats (bio, cyber, or AI misuse) is weak or unclear.
  3. 3Clear AI Safety Value. The project clearly reduces AI-related risks with valuable contributions. It addresses specific threats from AI systems and engages meaningfully with biosecurity, cybersecurity, or AI safety challenges.
  4. 4Significant Impact Potential. The above, plus the project demonstrates scalable safety mechanisms or defensive approaches. It shows clear potential to buy time for solving harder problems like alignment, or creates positive externalities for the broader AI safety ecosystem.
  5. 5Major Advancement. The above, plus the project represents a significant leap forward in defensive AI safety. Judges would eagerly share this with biosecurity, cybersecurity, or AI safety researchers and expect it to influence the field.

Does this strengthen the shield against AI-enabled threats?

Scoring guide
  1. 1Minimal Relevance. The project is only tangentially related to defensive technology or societal protection. Connection to biosecurity, cybersecurity, or defensive infrastructure is unclear or missing.
  2. 2Some Relevance. The project has some relevance to defensive acceleration, but the connection is broad or generic. It touches on defense without specific focus on AI-enabled threats or protective capabilities.
  3. 3Clear Relevance. The project clearly addresses defensive gaps against AI-enabled threats. It connects to at least one track (biosecurity, cybersecurity, or defense infrastructure) and demonstrates understanding of the threat landscape.
  4. 4Strong Contribution. The above, plus the project builds on existing defensive approaches and offers novel tools, frameworks, or implementations. It explicitly explains how it strengthens defensive capabilities with realistic deployment potential.
  5. 5Breakthrough Impact. The above, plus the project provides breakthrough insights or tools that could significantly influence defensive technology development. It identifies critical gaps and presents compelling solutions with clear paths from prototype to deployed system.

Did you build something that actually works?

Scoring guide
  1. 1Incomplete or Flawed. The project appears rushed or incomplete. Technical implementation is flawed, core functionality doesn't work, or the approach is fundamentally unsound. Little to no documentation.
  2. 2Basic Competence. The project shows reasonable effort with basic technical competence. Core functionality partially works. Documentation exists but may be incomplete. Some limitations are acknowledged.
  3. 3Solid Hackathon Project. The project is technically solid and well-scoped for 48 hours. Core functionality works and is documented. Code/methods are understandable and limitations are honestly addressed. This is what a good weekend prototype should look like.
  4. 4Impressive Implementation. The above, plus the implementation exceeds typical hackathon quality. Clear methodology, thorough documentation, and working demo. The tool/prototype is immediately useful for defenders and could realistically be built upon.
  5. 5Exceptional Execution. The project far exceeds expectations with exceptional technical execution. The implementation is elegant, fully functional, and includes something special (e.g., deployed demo, exceptional documentation, innovative architecture, or clear startup potential).

  1. The method used here is really solid for figuring out whether we can detect bots robustly to protect the information environment from spam! I'd be really excited to see more follow-on work done here to find a method that is effective.

  2. Report is well written and the research question has clear value. The lack of a strong ground-truth dataset is the biggest concern. It is hard to evaluate whether this approach will work against the actual threat without real examples of advanced LLM-generated bot accounts. It may be worth a separate project focused on proactively building that dataset. In addition, the inconsistent classifications depending on prompt phrasing make the current prototype unreliable.

    Things for the team to consider:

    (1) A robust dataset is essential. What are the practical paths to collecting or constructing a vetted set of LLM-generated accounts for evaluation?

    (3) It may help to define the risk spectrum. Some bots are annoying while others are genuinely dangerous / harmful. A medium-accuracy classifier becomes more compelling if it focuses on accounts that are both likely AI-generated and capable of causing harm.

    (3) Developers will increasingly design bots to evade text-based filters. Are there non-content signals (behavioral patterns, metadata, network structure) that could improve detection reliability over time?

    Read full reviewShow less

Cite this project

@misc{pagett2025comparative,
  title = {{Comparative LLM methods for Social Media Bot Detection}},
  author = {Matthew Pagett and Andreas Raaskov},
  year = {2025},
  month = nov,
  note = {Submitted to Defensive Acceleration Hackathon, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/comparative-llm-methods-for-social-media-bot-detection-u9s4}},
  url = {https://apartresearch.com/sprints/projects/comparative-llm-methods-for-social-media-bot-detection-u9s4}
}

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