A test of time: Modeling the long-term success of crowd collaborations
Abraham Israeli, David Jurgens, Daniel M Romero · 2026
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Description
Israeli and colleagues studied in 2026 what makes online crowd collaborations stay good over time, using Wikipedia as the test case. Many articles reach top quality status, but some later lose it. The team defined a metric called Sustainable Success and built a dataset of about 48,700 top-rated English Wikipedia articles.
About seven percent of those articles were later demoted from high-quality status. For each article the dataset holds over 300 features, covering edit history, editor experience, and team makeup. Machine learning models trained on these features could flag articles at risk of decline. Editor experience emerged as the strongest predictor of lasting quality. Articles that took longer to earn top status were also more likely to keep it, the authors found.
Catalog record
| CREATOR | Abraham Israeli, David Jurgens, Daniel M Romero |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 56 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |