My first instinct was to say reactions are cannibalizing comments and just run with that.
Start by clarifying the metric definitions and the time period, then hypothesize why reactions might be up while comments are down, considering both substitution and causal effects. Prioritize hypotheses by potential impact and ease of testing, and propose a plan to validate the root cause before jumping to solutions.
Pro tip: Don't assume the metrics are directly related; consider external factors like algorithm changes or UI updates that could independently affect both. Also, segment the data by user type, content type, and geography to uncover nuanced insights.
Define what 'reactions up 20%' and 'comments down 10%' mean precisely: absolute vs relative, per user vs total, time frame, and whether these are statistically significant. Confirm the data source and any recent changes.
Break down the metrics by user demographics, content categories, device types, and geography to see if the trend is uniform or driven by a specific segment. Look for correlations with other metrics like time spent, shares, or reports.
Brainstorm possible reasons: reactions may be substituting for comments (e.g., easier to react), a UI change may have made commenting harder, algorithm changes may favor reaction-worthy content, or external events may drive more reactions but fewer comments.
Rank hypotheses by impact and likelihood, then design quick tests or analyses (e.g., A/B tests, cohort analysis, user surveys) to validate or invalidate them. Start with the most plausible and easily testable.
Based on findings, decide whether to intervene (e.g., adjust UI, algorithm, or incentives) or accept the change if it aligns with goals. Define success metrics and monitor long-term effects on user engagement and well-being.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.