Measure Harmful Content Impact with Key Metrics

##### Scenario A social-media platform needs to quantify how serious harmful or inappropriate user-generated content is, and what its impact on users and the business actually is. As a data scientist, you are asked to design the measurement framework. ##### Question How would you measure the severity and platform impact of harmful content? 1. Which specific metric(s) would you choose as the primary (north-star) measure, and why? Consider candidates such as **View Prevalence**, **Content Prevalence**, and **Reach Prevalence**, and how (if at all) you would incorporate **severity weighting**. 2. What complementary or supporting metrics would you track alongside the primary metric (e.g., user exposure / reach, exposure intensity in the tail, time-weighted exposure, enforcement quality)? 3. Discuss the pros and cons of relying on **View Prevalence alone**. What does it capture well, and what does it hide or get wrong? 4. How would you ensure the measurement is **unbiased and timely** (sampling, human labeling, classifier calibration, confidence intervals, segmentation)? ##### Hints Tie metrics to user exposure and business risk; compare incidence-based (creator/supply-side) vs. view-weighted (exposure-side) rates; address severity buckets, breadth vs. depth of harm, denominator/window sensitivity, and measurement latency. Distinguish how many *items* are harmful, how many *views* are harmful, and how many *users* are touched.