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A Test of Time: Predicting the Sustainable Success of Online Collaboration in Wikipedia
Computers and Society · Submitted 24 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Additionally, user experience emerged as the most critical predictor of sustainability.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | The Internet has significantly expanded the potential for global collaboration, allowing millions of users to contribute to collective projects like Wikipedia. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | While prior work has assessed the success of online collaborations, most approaches are time-agnostic, evaluating success without considering its longevity. | gap 0.68 | 0.05 | 0.02 | asserted 1.00 |
| s2 | Research on the factors that ensure the long-term preservation of high-quality standards in online collaboration is scarce. | gap 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s3 | In this study, we address this gap. | contribution 0.93 | 0.54 | 0.02 | asserted 0.66 |
| s4 | We propose a novel metric, `Sustainable Success,' which measures the ability of collaborative efforts to maintain their quality over time. | contribution 0.91 | 0.95 | 0.06 | asserted 1.00 |
| s5 | 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. | method 0.77 | 0.96 | 0.03 | asserted 1.00 |
| s6 | Using this dataset, we develop machine learning models to predict the sustainable success of Wikipedia articles. | method 0.95 | 0.97 | 0.04 | asserted 1.00 |
| s7 | Our best-performing model achieves a high AU-ROC score of 0.88 on average. | result 1.00 | 0.98 | 0.10 | asserted 1.00 |
| s8 | Our analysis reveals important insights. | result 0.52 | 0.65 | 0.03 | asserted 0.74 |
| s9 | 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). | result 0.97 | 0.97 | 0.25 | asserted 0.79 |
| s10 | Additionally, user experience emerged as the most critical predictor of sustainability. | result 0.97 | 0.95 | 0.24 | asserted 1.00 |
| s11 | 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. | interpretation 0.97 | 0.49 | 0.09 | speculative 0.82 |
| s12 | We make all data and code used for this study publicly available for further research. | contribution 0.79 | 0.89 | 0.03 | asserted 1.00 |
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Original abstract
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.
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How the literature uses it
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What it cites
Uses the method or tool of 2 papers
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Specifically, we measure the sentiment [ref] , formality [ref] , politeness [ref] , toxicity [ref] , and certainty [ref] for each comment in a discussion.
Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications · Explanatory Features · 1.00
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We report only the results obtained from the Gradient-Boosted-Decision-Trees (GBT) model [ref] , which achieved comparable results to other ensemble models such as Random Forest [ref] and CatBoost [ref] .
CatBoost: gradient boosting with categorical features support · Classification Models · 0.91
Compares with 1 paper
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For other features, such as number-of-editors, editors-gini, and most network features, while we did not find those to be the most dominant, other `Success Prediction' studies found them as significant predictors [ref] .
Assessing the Quality of Wikipedia Pages Using Edit Longevity and Contributor Centrality · Discussion and Limitations · 0.99
Cites as background 2 papers
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In the past decade, numerous studies have demonstrated how factors such as edit behavior [ref] , network structure [ref] , regulation [ref] , and textual and visual content [ref] are associated with content assessment in Wikipedia.
Protection from Evil and Good: The Differential Effects of Page Protection on Wikipedia Article Quality · Online Collaboration and Success · 0.84
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Collaboration plays a crucial role in societal progress, shaping the way individuals and groups share knowledge, solve problems, and innovate [ref] .
Collective Intelligence in Humans: A Literature Review · Introduction · 0.92
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