Measuring the Prevalence of Policy Violating Content with ML Assisted Sampling and LLM Labeling

2026-02-19

AI Summary

Accurately measuring the proportion of policy-violating content actually encountered by users is challenged by the rarity of violations, high annotation costs, and the difficulty of conducting frequent, representative assessments. This work proposes a design-based measurement system that draws daily probability samples from user exposure streams using machine learning–assisted weighting. It enables efficient annotation through multimodal large language models, policy-guided prompting, and gold-set validation, and constructs unbiased estimators to produce prevalence metrics with confidence intervals. The system supports multidimensional post-stratification—such as by platform interface, user geography, or content age—using a single global sample, maintaining statistical unbiasedness while prioritizing high-exposure and high-risk content. This approach substantially improves monitoring efficiency, timeliness, and flexibility while significantly reducing annotation costs.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Application Domains: Misinformation & Fake News
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Web Mining and Content Analysis: Web measurements
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

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.

Problem

• prevalence measurement
• policy violating content
• content safety
• impression sampling
• rare event estimation

Innovation

• ML-assisted sampling
• multimodal LLM labeling
• prevalence estimation
• design-based measurement
• post-stratification