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Published November 25, 2025 | Version v1.0 — Initial Public Release
Preprint Open

AIMM: An AI-Driven Multimodal Framework for Detecting Social-Media-Influenced Stock Market Manipulation

Authors/Creators

  • 1. Independent Researcher

Contributors

Researcher:

  • 1. Independent Researcher

Description

AIMM (Automated Inference of Market Manipulation) is an AI-driven, multimodal framework designed to detect social-media-influenced stock-market manipulation. The system integrates market structure, Reddit-based social features, transformer-based sentiment analysis, bot-likeness heuristics, coordination density, and optional SEC regulatory signals to assess manipulation risk in near real-time.

AIMM introduces the AIMM Manipulation Risk Score (AMRS), a weighted and normalized risk metric combining:

  • Reddit social volume and author activity

  • FinBERT/VADER sentiment signals

  • Bot-heavy posting ratios

  • TF–IDF text-similarity coordination metrics

  • Market anomalies such as returns and volume z-scores

  • Optional ownership and insider-transaction features from SEC EDGAR

The framework is implemented as a reproducible end-to-end pipeline with parquet-backed storage, configurable thresholds, a rule-based alerting system, and a publicly accessible Streamlit dashboard for interactive exploration.

AIMM extends the author's prior work, the Stock-Pattern-Assistant (SPA) framework (doi:10.5281/zenodo.17618798), by moving beyond deterministic price-pattern analysis into multimodal manipulation intelligence. AIMM models how online narratives, bot-amplified messaging, coordinated activity clusters, and market microstructure events jointly influence price behavior.

The paper provides:

  • A full feature-engineering pipeline for social, market, bot, sentiment, and coordination metrics

  • Formal AMRS definition, normalization, and weighting

  • Synthetic but realistic experiments across multiple tickers (AAPL, NVDA, SCHW, AMC, GME, PGR, ALAB, XYZ)

  • Threat model and responsible-use guidelines

  • A reproducible implementation with open-source code and live dashboard

This release corresponds to AIMM v1.0 – Initial Public Release.

Files

AIMM_v1.0_Multimodal_Market_Manipulation_Detection_Preprint.pdf

Files (2.2 MB)

Additional details

Related works

Is supplement to
Preprint: 10.5281/zenodo.17618798 (DOI)

Dates

Submitted
2025-11-24

Software

Repository URL
https://github.com/sneela/aimm
Programming language
Python
Development Status
Active

References

  • [1] S. Neela, "Stock-Pattern-Assistant (SPA): Deterministic run detection and pattern-aware analysis of equity time series," Zenodo, Jan. 2025. doi:10.5281/zenodo.17618798. [2] F. Allen and D. Gale, "Stock price manipulation," The Review of Financial Studies, vol. 5, no. 3, pp. 503–529, 1992. [3] J. Bollen, H. Mao, and X. Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, vol. 2, no. 1, pp. 1–8, 2011. [4] T. Rao and S. Srivastava, "Intra-day stock market prediction using online textual messages," in 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1. IEEE, 2010, pp. 423–426. [5] J. J. Choi et al., "Reddit and the meme stock phenomenon," Yale School of Management Working Paper, 2022. [6] O. Varol et al., "Online human-bot interactions: Detection, estimation, and characterization," in Proceedings of the International AAAI Conference on Web and Social Media, vol. 11, no. 1, 2017, pp. 280–289. [7] D. Pacheco et al., "Uncovering coordinated networks on social media," in Proceedings of the 15th International AAAI Conference on Web and Social Media, 2021, pp. 128–139.