用机器学习采样+大模型标注，精准测量平台违规内容的真实曝光率。
Measuring the Prevalence of Policy Violating Content with ML Assisted Sampling and LLM Labeling
- 基于机器学习加权采样，聚焦高曝光高风险内容，节省人工成本。
- 每日采集样本可支持多维度分析，误差可控，结果带置信区间。
- 适合内容安全团队做跨平台、跨区域的违规内容监测与决策参考。
内容安全团队需要反映用户真实体验的指标，而非仅依赖报告数据。本文研究‘流行度’：某日用户观看中违反特定政策的内容占比。准确测量流行度困难，因违规内容稀少且人工标注成本高，难以频繁开展具有平台代表性的研究。我们提出一种基于设计的测量系统：（i）利用机器学习辅助权重，从曝光流中每日抽取概率样本，将标注预算集中于高曝光、高风险内容，同时保持无偏性；（ii）使用多模态大模型结合政策提示和黄金标注集进行标签生成；（iii）产出符合设计的一致性流行度估计，包含置信区间与仪表盘下钻分析。核心设计目标是单一全局样本支持多维度分析：同一日样本可通过事后分层估计，支持按平台、用户地理、内容年龄等细分维度的流行度测算。本文详述统计估计器、方差与置信区间构建方法、标签质量监控机制，以及可配置的工程工作流，适用于多种政策场景。
原文摘要 · Abstract (English)
Content safety teams need metrics that reflect what users actually experience, not only what is reported. We study prevalence: the fraction of user views (impressions) that went to content violating a given policy on a given day. Accurate prevalence measurement is challenging because violations are often rare and human labeling is costly, making frequent, platform-representative studies slow. We present a design-based measurement system that (i) draws daily probability samples from the impression stream using ML-assisted weights to concentrate label budget on high-exposure and high-risk content while preserving unbiasedness, (ii) labels sampled items with a multimodal LLM governed by policy prompts and gold-set validation, and (iii) produces design-consistent prevalence estimates with confidence intervals and dashboard drilldowns. A key design goal is one global sample with many pivots: the same daily sample supports prevalence by surface, viewer geography, content age, and other segments through post-stratified estimation. We describe the statistical estimators, variance and confidence interval construction, label-quality monitoring, and an engineering workflow that makes the system configurable across policies.
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