用社交媒体数据预警股市操纵，提前22天发现游戏驿站异常
AIMM: An AI-Driven Multimodal Framework for Detecting Social-Media-Influenced Stock Market Manipulation
- 融合贴吧内容、机器人特征和价格数据，生成每日操纵风险评分
- 在33个标记日中验证有效，对游戏驿站事件提前22天预警
- 适合监管机构、券商和研究者追踪社交驱动的市场异常
股市操纵如今多源于协同社交媒体活动，而非孤立交易。零售投资者、监管机构与券商亟需将网络叙事与行为模式关联到市场表现的工具。我们提出AIMM框架，整合Reddit活动、机器人与协同指标及OHLCV市场特征，为每只股票生成每日操纵风险评分。系统采用原生Parquet管道与Streamlit仪表板，支持分析师探索可疑时段、查看原始帖子与价格走势，并长期记录模型输出。受限于Reddit API，我们使用校准的合成社交特征以匹配已知事件特征；市场数据（OHLCV）采用Yahoo Finance真实历史数据。本工作贡献三方面：第一，构建了AIMM基准数据集（AIMM-GT），包含33个标注的股票-日期样本，涵盖8只股票，来自SEC执法行动、社区验证的操纵案例及匹配的正常对照；第二，实现前向回溯评估与前瞻性预测记录，支持回顾性与部署式评估；第三，分析领先时间，显示AIMM在2021年1月游戏驿站做空挤压峰值前22天即已发出预警。当前标注集较小（33个股票-日期，3个正例），但结果展现初步区分能力与早期预警潜力。代码、数据结构与仪表板设计均已公开，以支持社交媒体驱动的市场监控研究。
原文摘要 · Abstract (English)
Market manipulation now routinely originates from coordinated social media campaigns, not isolated trades. Retail investors, regulators, and brokerages need tools that connect online narratives and coordination patterns to market behavior. We present AIMM, an AI-driven framework that fuses Reddit activity, bot and coordination indicators, and OHLCV market features into a daily AIMM Manipulation Risk Score for each ticker. The system uses a parquet-native pipeline with a Streamlit dashboard that allows analysts to explore suspicious windows, inspect underlying posts and price action, and log model outputs over time. Due to Reddit API restrictions, we employ calibrated synthetic social features matching documented event characteristics; market data (OHLCV) uses real historical data from Yahoo Finance. This release makes three contributions. First, we build the AIMM Ground Truth dataset (AIMM-GT): 33 labeled ticker-days spanning eight equities, drawing from SEC enforcement actions, community-verified manipulation cases, and matched normal controls. Second, we implement forward-walk evaluation and prospective prediction logging for both retrospective and deployment-style assessment. Third, we analyze lead times and show that AIMM flagged GME 22 days before the January 2021 squeeze peak. The current labeled set is small (33 ticker-days, 3 positive events), but results show preliminary discriminative capability and early warnings for the GME incident. We release the code, dataset schema, and dashboard design to support research on social media-driven market surveillance.
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