Examining nonsuicidal self-injury content creation on TikTok through qualitative content analysis

IF 2.4 3区 管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE Library & Information Science Research Pub Date : 2022-10-01 DOI:10.1016/j.lisr.2022.101199
Valerie Lookingbill
{"title":"Examining nonsuicidal self-injury content creation on TikTok through qualitative content analysis","authors":"Valerie Lookingbill","doi":"10.1016/j.lisr.2022.101199","DOIUrl":null,"url":null,"abstract":"<div><p>Qualitative content analysis is a methodological approach for the subjective interpretation of data. Using empirical research to illustrate the methodological advantages and disadvantages of qualitative content analysis, this article examines the suitability of qualitative content analysis for the field of LIS and illustrates how the method can be used to inform LIS information practices research of marginalized populations through emerging information and communication technologies (ICTs). Specifically, this article examines the suitability of qualitative content analysis through its application in an ongoing study exploring how individuals who engage in nonsuicidal self-injury circumnavigate algorithmic exclusion in the emerging ICT TikTok. Qualitative content analysis can advance LIS research and practice by refuting deficit thinking and understanding information creation practices in context. Methodological shortcomings relate to the reduction of data and the inability to determine cause-and-effect relationships. The author recommends that researchers supplement qualitative content analysis with additional qualitative approaches to address these limitations.</p></div>","PeriodicalId":47618,"journal":{"name":"Library & Information Science Research","volume":"44 4","pages":"Article 101199"},"PeriodicalIF":2.4000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Library & Information Science Research","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0740818822000627","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 5

Abstract

Qualitative content analysis is a methodological approach for the subjective interpretation of data. Using empirical research to illustrate the methodological advantages and disadvantages of qualitative content analysis, this article examines the suitability of qualitative content analysis for the field of LIS and illustrates how the method can be used to inform LIS information practices research of marginalized populations through emerging information and communication technologies (ICTs). Specifically, this article examines the suitability of qualitative content analysis through its application in an ongoing study exploring how individuals who engage in nonsuicidal self-injury circumnavigate algorithmic exclusion in the emerging ICT TikTok. Qualitative content analysis can advance LIS research and practice by refuting deficit thinking and understanding information creation practices in context. Methodological shortcomings relate to the reduction of data and the inability to determine cause-and-effect relationships. The author recommends that researchers supplement qualitative content analysis with additional qualitative approaches to address these limitations.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
通过定性内容分析,对TikTok上的非自杀性自残内容创作进行检验
定性内容分析是对数据进行主观解释的一种方法学方法。本文通过实证研究来说明定性内容分析方法的优缺点,考察了定性内容分析在LIS领域的适用性,并说明了如何使用该方法通过新兴的信息和通信技术(ict)为边缘人群的LIS信息实践研究提供信息。具体而言,本文通过定性内容分析在一项正在进行的研究中的应用,考察了定性内容分析的适用性,该研究探索了在新兴的ICT TikTok中,从事非自杀式自残行为的个体如何绕过算法排斥。定性内容分析可以通过反驳缺陷思维和理解语境中的信息创造实践来推进信息科学的研究和实践。方法上的缺点涉及数据的减少和无法确定因果关系。作者建议研究人员用额外的定性方法来补充定性内容分析,以解决这些局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Library & Information Science Research
Library & Information Science Research INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
4.60
自引率
6.90%
发文量
51
期刊介绍: Library & Information Science Research, a cross-disciplinary and refereed journal, focuses on the research process in library and information science as well as research findings and, where applicable, their practical applications and significance. All papers are subject to a double-blind reviewing process.
期刊最新文献
The user experience of university library: A text mining analysis of a Q&A platform in China Data literacy in flux: Perspectives of community college librarians on evolving educational demands and library capacities Interpretable analysis of public library service outcomes based on ensemble learning models: Data study from China (2007–2021) A systematic review of library services platforms research and research agenda Editorial Board
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1