Page 4 of 7 — Statistical testing of social activation thresholds.
Each hypothesis asks: do users who cross a social threshold in their first 7 days read more in the subsequent 30-day outcome window?
In plain terms: we split users into two groups (met the threshold vs. didn't), then ask whether one group reads significantly more than the other. A small p-value means the difference is unlikely due to chance. Effect size measures how large the practical difference is (0 = none, 1 = complete separation).
Users who follow ≥ N people by day 7 vs. those who don't. Outcome: reading hours in days 8–37.
Following ≥1 person in the first week is the sharpest threshold — users who follow anyone at all read 8.8x more. The effect plateaus after ~5 follows.
Users who have ≥ N followers by day 7 vs. those who don't. Outcome: reading hours in days 8–37.
Gaining even 1 follower matters, but the effect is weaker than following — being discovered matters less than actively connecting.
Users who receive ≥ N kudos by day 7 vs. those who don't. Outcome: reading hours in days 8–37.
Kudos received shows the strongest raw effect, but this is confounded: kudos require reading sessions to exist. See H6 for the control.
Users who give ≥ N kudos by day 7 vs. those who don't. Outcome: reading hours in days 8–37.
Giving kudos also predicts reading — users who engage with others' content tend to read more themselves.
Combined score = following + followers + kudos received at day 7. Users with score ≥ N vs. those below.
The combined score aggregates all social signals. It's useful but doesn't outperform the individual metrics.
Users with bidirectional engagement score (min of following, followers) ≥ N at day 7 vs. those below. Outcome: reading hours in days 8–37.
Bidirectional engagement (both following and being followed) captures a qualitative shift: the user is participating in the community, not just observing.
Users with ≥ N reading sessions in their first 7 days vs. those with fewer. Outcome: reading hours in days 8–37. This is the baseline control.
Reading momentum is the strongest predictor — unsurprisingly, users who read in week 1 continue reading. This is the baseline that social metrics must beat to prove independent value.
Comparing the single best-performing threshold from each hypothesis, ranked by effect size.
Kudos received (H3a) and reading momentum (H6) have the highest effect sizes, but both are confounded by existing reading behavior. The most actionable metrics are following (H1) and bidirectional engagement (H5), which represent social connections we can directly influence through experiments.