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ORES: Lowering Barriers with Participatory Machine Learning in Wikipedia

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 Added by Aaron Halfaker
 Publication date 2019
and research's language is English




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Algorithmic systems---from rule-based bots to machine learning classifiers---have a long history of supporting the essential work of content moderation and other curation work in peer production projects. From counter-vandalism to task routing, basic machine prediction has allowed open knowledge projects like Wikipedia to scale to the largest encyclopedia in the world, while maintaining quality and consistency. However, conversations about how quality control should work and what role algorithms should play have generally been led by the expert engineers who have the skills and resources to develop and modify these complex algorithmic systems. In this paper, we describe ORES: an algorithmic scoring service that supports real-time scoring of wiki edits using multiple independent classifiers trained on different datasets. ORES decouples several activities that have typically all been performed by engineers: choosing or curating training data, building models to serve predictions, auditing predictions, and developing interfaces or automated agents that act on those predictions. This meta-algorithmic system was designed to open up socio-technical conversations about algorithms in Wikipedia to a broader set of participants. In this paper, we discuss the theoretical mechanisms of social change ORES enables and detail case studies in participatory machine learning around ORES from the 5 years since its deployment.



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119 - Nathan TeBlunthuis 2021
Organizing complex peer production projects and advancing scientific knowledge of open collaboration each depend on the ability to measure quality. Article quality ratings on English language Wikipedia have been widely used by both Wikipedia community members and academic researchers for purposes like tracking knowledge gaps and studying how political polarization shapes collaboration. Even so, measuring quality presents many methodological challenges. The most widely used systems use labels on discrete ordinal scales when assessing quality, but such labels can be inconvenient for statistics and machine learning. Prior work handles this by assuming that different levels of quality are evenly spaced from one another. This assumption runs counter to intuitions about the relative degrees of effort needed to raise Wikipedia encyclopedia articles to different quality levels. Furthermore, models from prior work are fit to datasets that oversample high-quality articles. This limits their accuracy for representative samples of articles or revisions. I describe a technique extending the Wikimedia Foundations ORES article quality model to address these limitations. My method uses weighted ordinal regression models to construct one-dimensional continuous measures of quality. While scores from my technique and from prior approaches are correlated, my approach improves accuracy for research datasets and provides evidence that the evenly spaced assumption is unfounded in practice on English Wikipedia. I conclude with recommendations for using quality scores in future research and include the full code, data, and models.
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