I knew it was going to be right-skewed but I second-guessed myself on whether to say log-normal or power-law.
Start by clarifying the metric (comments per user over a given period) and the population (all Facebook users vs. active users). Then describe the expected shape as a heavily right-skewed distribution, likely following a power law or log-normal, and justify it by referencing user engagement patterns and platform dynamics.
Pro tip: Acknowledge that the distribution may vary by user segment (e.g., power users vs. casual users) and that the 'long tail' often contains bots or inactive accounts, which can affect the analysis. This shows you think about data quality and segmentation.
Define what 'comment counts' means: comments made by a user, received by a user, or both? Specify the time frame (e.g., daily, monthly) and the population (all users, active users).
State that the distribution is likely right-skewed, with a small number of users accounting for a large proportion of comments, and a long tail of users with few or zero comments.
Explain that engagement follows the Pareto principle (80/20 rule) due to varying user motivations, network effects, and content virality. Also mention that many users are passive consumers, leading to a spike at zero.
Discuss whether it might be log-normal (if multiplicative factors) or power law (if preferential attachment). Note that the shape could differ for comments received vs. made, and by user demographics.
If sketching, draw a curve with a high peak near zero, rapidly declining, and a long tail to the right. Label axes: x-axis for number of comments, y-axis for frequency or density.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.