Detect and evaluate "stolen" posts

You are a Data Scientist at a Twitter-like app. The platform suspects an increase in “stolen posts” (users reposting/plagiarizing others’ content without attribution), and a new algorithm has been built to reduce stolen posts. Answer the following product/DS questions. ## 1) What additional information do you need? Given only the basic post table schema (post id/author/time/type/content/parent), list the **additional data** you would request to reliably determine whether a post is “stolen.” ## 2) What are drawbacks of your methodology? Assume you propose a detection methodology (rules, ML, similarity search, etc.). Explain key limitations and failure modes. ## 3) What harms can stolen posts cause? Describe potential user, creator, and platform harms. Include at least one harm that affects **metrics** or **model feedback loops**. ## 4) How do you evaluate the new algorithm’s effectiveness? Design an evaluation plan (online experiment or quasi-experiment) to measure whether the algorithm reduces stolen posts **without** hurting the product. Your plan should include: - A clear **primary metric** plus **diagnostic** and **guardrail** metrics - How you will handle **confounding** (e.g., seasonality, creator mix shifts, enforcement effects) - How to validate that the metric truly reflects “stolen post” reduction (label quality / delayed labels) - What decision rule you would use to launch/iterate