Abstract We develop a method to assess the quality of peer-produced content in knowledge repositories using their development and coordination histories. We also develop a process to identify relevant features for quality assessment models and algorithms for processing datasets in large-scale knowledge repositories. Models using these features, on English language Wikipedia articles, outperform existing methods for quality assessment. We achieve an overall accuracy of 81 percent which is a 7 percent improvement over existing models. In addition, our features improve the precision and recall of each class up to 9 percent and 17 percent respectively. Finally, our models are robust to ten-fold cross validation and techniques used for classification. Overall, our research provides a comprehensive design science framework for both identifying and efficiently extracting features related to development and coordination activities and assessing quality using these features. We also provide details of potential implementation of a quality assessment system for knowledge repositories.

中文翻译：

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使用开发和协调活动对知识库中同行制作的内容进行质量评估

摘要 我们开发了一种方法来评估知识库中同行制作的内容的质量，使用它们的发展和协调历史。我们还开发了一个过程来识别质量评估模型和算法的相关特征，用于处理大规模知识库中的数据集。在英语维基百科文章中使用这些特征的模型优于现有的质量评估方法。我们实现了 81% 的整体准确率，比现有模型提高了 7%。此外，我们的功能将每个类别的准确率和召回率分别提高了 9% 和 17%。最后，我们的模型对十倍交叉验证和用于分类的技术具有鲁棒性。全面的，我们的研究提供了一个全面的设计科学框架，用于识别和有效提取与开发和协调活动相关的特征，并使用这些特征评估质量。我们还提供了知识库质量评估系统的潜在实施细节。