# Leveraging Large Language Models to Detect Influence Campaigns in Social Media

arXiv: https://arxiv.org/abs/2311.07816 · HTML: https://mlsanity.com/paper/2311.07816/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 14 Nov 2023.

## Builds on (most cited first, 19 of 19)

- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours](https://mlsanity.com/paper/2008.11308/) (2020)
- [Anatomy of an AI-powered malicious social botnet](https://mlsanity.com/paper/2307.16336/) (2023)
- [Botometer 101: Social bot practicum for computational social scientists](https://mlsanity.com/paper/2201.01608/) (2022)
- [LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection](https://mlsanity.com/paper/2306.17408/) (2023)
- [Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems](https://mlsanity.com/paper/2211.14114/) (2022)
- [Coordinated Behavior on Social Media in 2019 UK General Election](https://mlsanity.com/paper/2008.08370/) (2020)
- [Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter](https://mlsanity.com/paper/2310.09884/) (2023)
- [Social Media Polarization and Echo Chambers in the Context of COVID-19: Case Study](https://mlsanity.com/paper/2103.10979/) (2021)
- [Characterizing the 2016 Russian IRA Influence Campaign](https://mlsanity.com/paper/1812.01997/) (2018)
- [Unveiling Coordinated Groups Behind White Helmets Disinformation](https://mlsanity.com/paper/2003.01313/) (2020)
- [A General Method to Find Highly Coordinating Communities in Social Media through Inferred Interaction Links](https://mlsanity.com/paper/2103.03409/) (2021)
- [Tracking Fringe and Coordinated Activity on Twitter Leading Up To the US Capitol Attack](https://mlsanity.com/paper/2302.04450/) (2023)
- [Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls](https://mlsanity.com/paper/2210.08786/) (2022)
- [Evidence of Inter-state Coordination amongst State-backed Information Operations](https://mlsanity.com/paper/2305.05907/) (2023)
- [Susceptibility to Unreliable Information Sources: Swift Adoption with Minimal Exposure](https://mlsanity.com/paper/2311.05724/) (2023)
- [Early Detection of Promoted Campaigns on Social Media](https://mlsanity.com/paper/1703.07518/) (2017)
- [A General Language for Modeling Social Media Account Behavior](https://mlsanity.com/paper/2211.00639/) (2022)

## Built on by (most cited first, 13 of 13)

- [What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection](https://mlsanity.com/paper/2402.00371/) (2024)
- [Emergent Coordinated Behaviors in Networked LLM Agents: Modeling the Strategic Dynamics of Information Operations](https://mlsanity.com/paper/2510.25003/) (2025)
- [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024)
- [A Structure-Aware Framework for Learning Device Placements on Computation Graphs](https://mlsanity.com/paper/2405.14185/) (2024)
- [A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges](https://mlsanity.com/paper/2409.13521/) (2024)
- [Clustering Document Parts: Detecting and Characterizing Influence Campaigns from Documents](https://mlsanity.com/paper/2402.17151/) (2024)
- [Large Language Models Reveal Information Operation Goals, Tactics, and Narrative Frames](https://mlsanity.com/paper/2405.03688/) (2024)
- [Analyzing public sentiment to gauge key stock events and determine volatility in conjunction with time and options premiums](https://mlsanity.com/paper/2502.05403/) (2025)
- [From Post To Personality: Harnessing LLMs for MBTI Prediction in Social Media](https://mlsanity.com/paper/2509.04461/) (2025)
- [Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models](https://mlsanity.com/paper/2511.03699/) (2025)
- [Beyond Content: Behavioral Policies Reveal Actors in Information Operations](https://mlsanity.com/paper/2602.02838/) (2026)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns](https://mlsanity.com/paper/2607.05855/) (2026)

## Abstract (arXiv)

Social media influence campaigns pose significant challenges to public discourse and democracy. Traditional detection methods fall short due to the complexity and dynamic nature of social media. Addressing this, we propose a novel detection method using Large Language Models (LLMs) that incorporates both user metadata and network structures. By converting these elements into a text format, our approach effectively processes multilingual content and adapts to the shifting tactics of malicious campaign actors. We validate our model through rigorous testing on multiple datasets, showcasing its superior performance in identifying influence efforts. This research not only offers a powerful tool for detecting campaigns, but also sets the stage for future enhancements to keep up with the fast-paced evolution of social media-based influence tactics.