利用关联规则挖掘探索关系趋同：协作知识生产案例研究
Exploring Convergence in Relation using Association Rules Mining: A Case Study in Collaborative Knowledge Production
AI总结：
本文以开放公民科学平台为案例，运用ARM分析标签关联与时间演化，并提出基于Mann-Kendall检验的趋同测量算法，验证了非专家协作中的意识形态趋同。
摘要：
本研究深入考察了非专家在开放协作平台知识生产中所发挥的关键作用，尤其关注最终促成新故障类别提出的复杂标签开发过程。研究借助关联规则挖掘（Association Rule Mining，ARM），力图揭示公民科学家之间协作的底层动态。通过细致量化标签关联并审视其时间动态，本研究全面而细致地理解了非专家如何协作产生有价值的科学洞见。此外，本研究还将考察范围扩展到在线公民科学知识生产中的意识形态趋同现象。为此，研究提出了一种基于Mann-Kendall趋势检验的新型测量算法。这一创新方法阐明了协作知识生产的动态，揭示了利用非专家贡献开展科学研究时固有的巨大机遇与严峻挑战。值得注意的是，研究同时采用新提出的趋同检验方法和基于时间序列数据平稳性的传统方法，发现了稳健的意识形态趋同模式。这一突破性发现对于理解在线公民科学社区的动态具有重要意义，并凸显了非专家在塑造数字时代科学格局方面的关键作用。最终，本研究显著增进了我们对在线公民科学社区的理解，强调了其利用集体智能应对复杂科学任务的潜力，并丰富了我们对数字时代协作知识生产过程的认识。
英文摘要：
This study delves into the pivotal role played by non-experts in knowledge production on open collaboration platforms, with a particular focus on the intricate process of tag development that culminates in the proposal of new glitch classes. Leveraging the power of Association Rule Mining (ARM), this research endeavors to unravel the underlying dynamics of collaboration among citizen scientists. By meticulously quantifying tag associations and scrutinizing their temporal dynamics, the study provides a comprehensive and nuanced understanding of how non-experts collaborate to generate valuable scientific insights. Furthermore, this investigation extends its purview to examine the phenomenon of ideological convergence within online citizen science knowledge production. To accomplish this, a novel measurement algorithm, based on the Mann-Kendall Trend Test, is introduced. This innovative approach sheds illuminating light on the dynamics of collaborative knowledge production, revealing both the vast opportunities and daunting challenges inherent in leveraging non-expert contributions for scientific research endeavors. Notably, the study uncovers a robust pattern of convergence in ideology, employing both the newly proposed convergence testing method and the traditional approach based on the stationarity of time series data. This groundbreaking discovery holds significant implications for understanding the dynamics of online citizen science communities and underscores the crucial role played by non-experts in shaping the scientific landscape of the digital age. Ultimately, this study contributes significantly to our understanding of online citizen science communities, highlighting their potential to harness collective intelligence for tackling complex scientific tasks and enriching our comprehension of collaborative knowledge production processes in the digital age.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
