Our newsletterAcademicACM, 2025
Among profile features, an automated beauty score carried the most weight in predicting follower count, but its weight swung from 35% to 81% depending on which model was used
Organic reach & algorithm
- Reader Note:
- This is the share of the model's own feature weighting, not the share of follower differences explained. The follower models accounted for only about 27% of that variation to begin with.
- Evidence:
- Observed behaviour
- Vendor interest:
- None
- Sample:
- 62,706 anonymized LinkedIn profiles from a Kaggle dataset; original sampling method not clearly disclosed.
Reader Note:
Evidence:
Vendor interest:
Sample:Good for
Comparing yourself against a similar segment, and choosing what to test next.
Not for
- Saying that one thing caused another, or predicting what you would get.
- This is an aggregated or average number that covers very different cases. Real results in a particular segment may differ.
- The source never says when this data was collected. It could be considerably older than the publication date suggests.
- The source says how much it looked at, but not how any of it was chosen.
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