Gruzd Anatoliy, Kumar Priya, Abul-Fottouh Deena, Haythornthwaite Caroline
Ted Rogers School of Information Technology Management, Ryerson University, 350 Victoria Street, Toronto, ON M5B2K3 Canada.
Social Media Lab, Ted Rogers School of Management, Ryerson University, 10 Dundas Street East, Toronto, Ontario M5B2G9 Canada.
Comput Support Coop Work. 2020;29(6):629-656. doi: 10.1007/s10606-020-09376-y. Epub 2020 Jun 29.
As social media become a staple for knowledge discovery and sharing, questions arise about how self-organizing communities manage learning outside the domain of organized, authority-led institutions. Yet examination of such communities is challenged by the quantity of posts and variety of media now used for learning. This paper addresses the challenges of identifying (1) what information, communication, and discursive practices support successful online communities, (2) whether such practices are similar on Twitter and Reddit, and (3) whether machine learning classifiers can be successfully used to analyze larger datasets of learning exchanges. This paper builds on earlier work that used manual coding of learning and exchange in Reddit 'Ask' communities to derive a coding schema we refer to as 'learning in the wild'. This schema of eight categories: explanation with disagreement, agreement, or neutral presentation; socializing with negative, or positive intent; information seeking; providing resources; and comments about forum rules and norms. To compare across media, results from coding Reddit's AskHistorians are compared to results from coding a sample of #Twitterstorians tweets ( = 594). High agreement between coders affirmed the applicability of the coding schema to this different medium. LIWC lexicon-based text analysis was used to build machine learning classifiers and apply these to code a larger dataset of tweets ( = 69,101). This research shows that the 'learning in the wild' coding schema holds across at least two different platforms, and is partially scalable to study larger online learning communities.
随着社交媒体成为知识发现和分享的主要方式，关于自组织社区如何在有组织的、由权威主导的机构领域之外进行学习的问题也随之出现。然而，对这类社区的研究受到用于学习的帖子数量和媒体种类的挑战。本文探讨了以下挑战：（1）确定哪些信息、沟通和话语实践支持成功的在线社区；（2）这些实践在推特和红迪网上是否相似；（3）机器学习分类器是否能成功用于分析更大的学习交流数据集。本文基于早期的工作，该工作对红迪网“提问”社区中的学习和交流进行了人工编码，以得出一种我们称为“自然学习”的编码模式。该模式分为八类：带有不同意见、赞同或中立表述的解释；带有消极或积极意图的社交；信息寻求；提供资源；以及关于论坛规则和规范的评论。为了在不同媒体间进行比较，将对红迪网“AskHistorians”的编码结果与对#Twitterstorians推文样本（n = 594）的编码结果进行对比。编码人员之间的高度一致性证实了该编码模式适用于这种不同的媒介。基于LIWC词典的文本分析被用于构建机器学习分类器，并将其应用于对更大的推文数据集（n = 69,101）进行编码。这项研究表明，“自然学习”编码模式至少在两个不同平台上适用，并且在一定程度上可扩展用于研究更大的在线学习社区。
