推特上的专业话语分析:对#openeducation标签的混合方法分析

Fredrick W. Baker, Patrick R. Lowenthal
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引用次数: 1

摘要

专业人士和学者现在使用像Twitter这样的社交网站来进行有关资源和网络的学术讨论。在推特上添加标签可以让用户在共同感兴趣的领域与以前不认识的其他人联系起来,并提供了检查这些联系的机会。本研究通过一个可扩展的混合方法内容分析模型,探讨了Twitter上围绕#openeducation标签讨论开放教育的方式,该模型有助于对标签话语进行多管齐下的分析。研究人员分析了903条使用#开放教育标签的推文样本,并将结果分为主题。出现了32个主题,分为8类。为了扩展研究模型,从主题开发的问卷调查与活跃的标签用户子集进行了试点。研究结果提供了对开放教育主要话语的洞察,以及一种可扩展的方法来分析Twitter上的话语,识别活跃的参与者,并进一步探讨人们与感兴趣的话题之间的联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Professional Discourse on Twitter: A Mixed Methods Analysis of the #openeducation Hashtag
Professionals and academics now use social networking sites like Twitter for scholarly discourse around resources and networking. Adding hashtags to tweets allows users to connect with previously unknown others around areas of common interest and provides opportunities to examine these connections. This study explored how open education is discussed on Twitter around the #openeducation hashtag through a scalable mixed methods content analysis model useful for the multi-pronged analyse of hashtag discourse. Researchers analysed a convenience sample of 903 tweets using the #openeducation hashtag and grouped the results into themes. Thirty-two themes emerged, which were grouped into eight categories. To extend the research model, a questionnaire developed from the themes was piloted with a subset of active hashtag users. The results provide insight into the major discourse on open education, as well as a scalable means to analyse discourse on Twitter, identify active participants, and probe further about ties people have to a topic of interest.
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