Comparing Themes Extracted via Topic Modeling and Manual Content Analysis: Korean-Language Discussions of Dementia on Twitter.
Comparing Themes Extracted via Topic Modeling and Manual Content Analysis: Korean-Language Discussions of Dementia on Twitter.
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DOI：
10.3233/shti220704
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发表时间：
2022-06-29
影响因子：
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通讯作者：
Yoon, Sunmoo
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文献类型：
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作者：
Lee, Haeyoung;Jang, Sun Joo;Sun, Frederick F;Broadwell, Peter;Yoon, Sunmoo
我们随机调查了2020年11月28日至12月9日期间发布的有关痴呆症/阿尔茨海默病的韩语推文（n= 12413），不受地理位置的限制。我们独立地对Tweets文本应用了Latent Dirichlet Allocation （LDA）主题建模和定性内容分析。我们将LDA主题建模提取的主题与手工编码方法识别的主题进行了比较。手工编码共检测到16个主题，信度（Cohen’s kappa）为0.842。最突出的主题比例为：家庭照顾负担（48.50%）、失联家庭成员报告（18.12%）、污名化（13.64%）、预防策略（5.07%）、危险因素（4.91%）、医疗政策（3.26%）和虐待/安全问题（1.75%）。从LDA主题建模结果中提取了7个内容与人工编码主题相似的主题（perplexity:−6.39,coherence score: 0.45）。我们的研究结果表明，应用LDA主题建模可以相当有效地从韩语Twitter讨论中提取主题，以类似于定性编码的方式，获得有关痴呆症家庭成员护理的见解，并且我们的方法可以应用于其他语言。
We randomly examined Korean-language Tweets mentioning dementia/Alzheimer’s disease (n= 12,413) posted from November 28 to December 9, 2020, without limiting geographical locations. We independently applied Latent Dirichlet Allocation (LDA) topic modeling and qualitative content analysis to the texts of the Tweets. We compared the themes extracted by LDA topic modeling to those identified via manual coding methods. A total of 16 themes were detected from manual coding, with inter-rater reliability (Cohen’s kappa) of 0.842. The proportions of the most prominent themes were: burdens of family caregiving (48.50%), reports of wandering/missing family members with dementia (18.12%), stigma (13.64%), prevention strategies (5.07%), risk factors (4.91%), healthcare policy (3.26%), and elder abuse/safety issues (1.75%). Seven themes whose contents were similar to themes derived from manual coding were extracted from the LDA topic modeling results (perplexity: −6.39, coherence score: 0.45). Our findings suggest that applying LDA topic modeling can be fairly effective at extracting themes from Korean Twitter discussions, in a manner analogous to qualitative coding, to gain insights regarding caregiving for family members with dementia, and our approach can be applied to other languages.