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 (n = 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 (n = 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共享的主要手段，人们开始质疑自组织社区如何在有组织的，由权威领导的机构范围之外管理学习。但是，现在用于学习的职位数量和各种媒体都对这类社区的检查提出了挑战。本文解决了以下挑战：确定（1）哪些信息，通信和话语实践支持成功的在线社区;（2）Twitter和Reddit上的此类实践是否相似;以及（3）是否可以成功地使用机器学习分类器进行分析更大的学习交流数据集。本文基于早期的工作，该工作使用Reddit“询问”社区中的学习和交流的手动编码来得出一种编码模式，我们称之为“野外学习”。这种模式分为八类：带有异议，同意或中立陈述的解释；带有消极或积极意图的社交活动；寻求信息；提供资源；以及有关论坛规则和规范的评论。为了进行跨媒体比较，将Reddit的AskHistorians编码结果与#Twitterstorians tweet样本编码结果进行了比较（n  = 594）。编码人员之间的高度一致确认了编码模式在这种不同媒介上的适用性。使用基于词典的LIWC文本分析来构建机器学习分类器，并将其应用于更大的推文数据集（n  = 69,101）。这项研究表明，“野外学习”编码模式至少在两个不同的平台上有效，并且可以部分扩展以研究更大的在线学习社区。