TY - JOUR AU - Robert AU - Delir Haghighi, Pari AU - Burstein, Frada AU - Urquhart, Donna AU - Cicuttini, Flavia PY - 2021 DA - 201/12/23 TI -调查个体对下腰痛经历背景的感知:Twitter数据的主题建模分析JO - J Med Internet Res SP - e26093 VL - 23 IS - 12 KW -腰痛KW - Twitter KW -内容分析KW -社交媒体KW -主题建模KW -以患者为中心的方法KW -疼痛体验KW -疼痛AB -背景:腰痛(LBP)仍然是全球残疾的主要原因。为了改善结果，更好地理解关于LBP的信念和LBP对个人的影响是很重要的。虽然LBP的个人经历传统上是通过定性研究来探索的，但社交媒体可以从大量的、异质的、地理分布的人群中获得数据，这是使用传统的定性或定量方法所不可能实现的。由于社交媒体网站上的数据是在未经请求的情况下收集的，与传统的数据收集方式相比，个人更有可能自由地、不受约束地表达自己的观点和情绪。因此，社交媒体的内容分析提供了一种新的方法来理解经历过LBP等问题的人是如何感知它及其影响的。目的:本研究的目的是从第一人称视角识别LBP经验的上下文变量，以深入了解个人的信念和感知。方法:我们分析了2014年1月1日至2018年12月31日期间896,867条关于LBP的清洁推文。我们测试并比较了潜在狄利克雷分配(LDA)、狄利克雷多项式混合(DMM)、GPU-DMM、biterm主题模型和非负矩阵分解来识别与推文相关的主题。通过一致性评分来确定最佳模型。 Two domain experts independently performed qualitative content analysis of the topics with the strongest coherence score and grouped them into contextual categories. The experts met and reconciled any differences and developed the final labels. Results: LDA outperformed all other algorithms, resulting in the highest coherence score. The best model was LDA with 60 topics, with a coherence score of 0.562. The 60 topics were grouped into 19 contextual categories. “Emotion and beliefs” had the largest proportion of total tweets (157,563/896,867, 17.6%), followed by “physical activity” (124,251/896,867, 13.85%) and “daily life” (80,730/896,867, 9%), while “food and drink,” “weather,” and “not being understood” had the smallest proportions (11,551/896,867, 1.29%; 10,109/896,867, 1.13%; and 9180/896,867, 1.02%, respectively). Of the 11 topics within “emotion and beliefs,” 113,562/157,563 (72%) had negative sentiment. Conclusions: The content analysis of tweets in the area of LBP identified common themes that are consistent with findings from conventional qualitative studies but provide a more granular view of individuals’ perspectives related to LBP. This understanding has the potential to assist with developing more effective and personalized models of care to improve outcomes in those with LBP. SN - 1438-8871 UR - //www.mybigtv.com/2021/12/e26093 UR - https://doi.org/10.2196/26093 DO - 10.2196/26093 ID - info:doi/10.2196/26093 ER -