Social Media as a Tool for Understanding the Role of Motor Differences in Neurodivergent Identity and Lived Experience.
Social Media as a Tool for Understanding the Role of Motor Differences in Neurodivergent Identity and Lived Experience.
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社交媒体作为理解运动差异在神经分歧身份和生活体验中的作用的工具。
DOI：
10.1123/jmld.2023-0024
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发表时间：
2023
影响因子：
1.3
通讯作者：
Miller,HaylieL
中科院分区：
文献类型：
--
作者：
Miller,HaylieL
社交媒体为运动发育和行为研究领域提供了一个令人兴奋的机会。随着Twitter等平台提供从用户的公共bios和帖子中获取历史数据的机会，研究社区对运动差异在身份和生活经验中的作用的看法还有未开发的潜力。与传统的定性方法（如结构化访谈或焦点小组）相比，在线话语分析具有优势，包括较少人为的设置，全球地理和文化代表性以及易于抽样。这个特殊部分的目的是提供一个管道，用于收集和分析与用户身份和话语特征相关的Twitter数据，特别是在运动发育和行为的背景下。这条管道在两项独立的研究中得到了证明，一项是关于自闭症用户的，另一项是关于发育协调障碍（DCD）/运动障碍用户的。这些研究表明，Twitter数据的实用性研究神经分歧和残疾人的观点，他们的运动差异，以及他们是否表示为他们的身份的一部分。每个研究的结果的影响进行了讨论，以及在更大的背景下，未来的研究使用各种方法来分析社交媒体数据，包括那些主要是基于图像和视频的平台。
Social media offers an exciting opportunity for the field of motor development and behavior research. With platforms such as Twitter offering access to historical data from users’ public bios and posts, there is untapped potential to examine community perspectives on the role of motor differences in identity and lived experience. Analysis of online discourse offers advantages over traditional qualitative methods like structured interviews or focus groups, including a less-contrived setting, global geographic and cultural representation, and ease of sampling. The aim of this special section is to present a pipeline for harvesting and analysis of Twitter data related to users’ identities and discourse characteristics, specifically situated in the context of motor development and behavior. This pipeline is demonstrated in two independent studies, one on autistic users and one on developmental coordination disorder (DCD)/dyspraxic users. These studies demonstrate the utility of Twitter data for research on neurodivergent and disabled people’s perspectives on their motor differences, and whether they are expressed as part of their identity. Implications of results are discussed for each study, as well as in the larger context of future research using a variety of approaches to analysis of social media data, including those from predominantly image- and video-based platforms.
影响因子：
1.3
作者：
Chatterjee,Riya;Fears,NicholasE;Lichtenberg,Gavin;Tamplain,PriscilaM;Miller,HaylieL
通讯作者：
Miller,HaylieL
收集 Twitter 数据以研究残疾人的运动行为：Python 简介和教程。
影响因子：
1.3
作者：
Fears,NicholasE;Chatterjee,Riya;Tamplain,PriscilaM;Miller,HaylieL
通讯作者：
Miller,HaylieL