# A Test of Time: Predicting the Sustainable Success of Online Collaboration in Wikipedia

arXiv: https://arxiv.org/abs/2410.19150 · HTML: https://mlsanity.com/paper/2410.19150/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 99 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 21 of 21)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [CatBoost: gradient boosting with categorical features support](https://mlsanity.com/paper/1810.11363/) (2018)
- [A Computational Approach to Politeness with Application to Social Factors](https://mlsanity.com/paper/1306.6078/) (2013)
- [Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications](https://mlsanity.com/paper/2109.14776/) (2021)
- [Don't lose the message while paraphrasing: A study on content preserving style transfer](https://mlsanity.com/paper/2308.09055/) (2023)
- [A Joint Model for Multimodal Document Quality Assessment](https://mlsanity.com/paper/1901.01010/) (2019)
- [On the Value of Wikipedia as a Gateway to the Web](https://mlsanity.com/paper/2102.07385/) (2021)
- [Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages](https://mlsanity.com/paper/2404.09764/) (2024)
- [Coding Together at Scale: GitHub as a Collaborative Social Network](https://mlsanity.com/paper/1407.2535/) (2014)
- [Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities](https://mlsanity.com/paper/1903.07724/) (2019)
- [NwQM: A neural quality assessment framework for Wikipedia](https://mlsanity.com/paper/2010.06969/) (2020)
- [Quality change: norm or exception? Measurement, Analysis and Detection of Quality Change in Wikipedia](https://mlsanity.com/paper/2111.01496/) (2021)
- [Social Networks Under Stress](https://mlsanity.com/paper/1602.00572/) (2016)
- [Tracing Community Genealogy: How New Communities Emerge from the Old](https://mlsanity.com/paper/1804.01990/) (2018)
- [With Flying Colors: Predicting Community Success in Large-scale Collaborative Campaigns](https://mlsanity.com/paper/2307.09650/) (2023)
- [Automatic Quality Assessment of Wikipedia Articles -- A Systematic Literature Review](https://mlsanity.com/paper/2310.02235/) (2023)
- [Coordination and Efficiency in Decentralized Collaboration](https://mlsanity.com/paper/1503.07431/) (2015)
- [Network Structure, Efficiency, and Performance in WikiProjects](https://mlsanity.com/paper/1804.03763/) (2018)
- [Analyzing the Productivity of GitHub Teams based on Formation Phase Activity](https://mlsanity.com/paper/2011.03423/) (2020)
- [Protection from Evil and Good: The Differential Effects of Page Protection on Wikipedia Article Quality](https://mlsanity.com/paper/2310.12696/) (2023)

## Abstract (arXiv)

The Internet has significantly expanded the potential for global collaboration, allowing millions of users to contribute to collective projects like Wikipedia. While prior work has assessed the success of online collaborations, most approaches are time-agnostic, evaluating success without considering its longevity. Research on the factors that ensure the long-term preservation of high-quality standards in online collaboration is scarce. In this study, we address this gap. We propose a novel metric, `Sustainable Success,' which measures the ability of collaborative efforts to maintain their quality over time. Using Wikipedia as a case study, we introduce the SustainPedia dataset, which compiles data from over 40K Wikipedia articles, including each article's sustainable success label and more than 300 explanatory features such as edit history, user experience, and team composition. Using this dataset, we develop machine learning models to predict the sustainable success of Wikipedia articles. Our best-performing model achieves a high AU-ROC score of 0.88 on average. Our analysis reveals important insights. For example, we find that the longer an article takes to be recognized as high-quality, the more likely it is to maintain that status over time (i.e., be sustainable). Additionally, user experience emerged as the most critical predictor of sustainability. Our analysis provides insights into broader collective actions beyond Wikipedia (e.g., online activism, crowdsourced open-source software), where the same social dynamics that drive success on Wikipedia might play a role. We make all data and code used for this study publicly available for further research.