Department of Human-Centred Computing, Faculty of Information Technology, Monash University, Caulfield East, Australia.
Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
J Med Internet Res. 2021 Dec 23;23(12):e26093. doi: 10.2196/26093.
Low back pain (LBP) remains the leading cause of disability worldwide. A better understanding of the beliefs regarding LBP and impact of LBP on the individual is important in order to improve outcomes. Although personal experiences of LBP have traditionally been explored through qualitative studies, social media allows access to data from a large, heterogonous, and geographically distributed population, which is not possible using traditional qualitative or quantitative methods. As data on social media sites are collected in an unsolicited manner, individuals are more likely to express their views and emotions freely and in an unconstrained manner as compared to traditional data collection methods. Thus, content analysis of social media provides a novel approach to understanding how problems such as LBP are perceived by those who experience it and its impact.
The objective of this study was to identify contextual variables of the LBP experience from a first-person perspective to provide insights into individuals' beliefs and perceptions.
We analyzed 896,867 cleaned tweets about LBP between January 1, 2014, and December 31, 2018. We tested and compared latent Dirichlet allocation (LDA), Dirichlet multinomial mixture (DMM), GPU-DMM, biterm topic model, and nonnegative matrix factorization for identifying topics associated with tweets. A coherence score was determined to identify the best model. 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.
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.
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.
腰痛（LBP）仍然是全球导致残疾的主要原因。为了改善结局，了解人们对腰痛的信念以及腰痛对个人的影响非常重要。尽管腰痛的个人经历传统上是通过定性研究来探索的，但社交媒体可以访问来自大量异质和地理分布人群的数据，而使用传统的定性或定量方法是不可能的。由于社交媒体网站上的数据是以非请求的方式收集的，因此与传统的数据收集方法相比，个体更有可能自由和不受限制地表达他们的观点和情绪。因此，社交媒体的内容分析为理解像腰痛这样的问题如何被经历过它的人所感知以及它的影响提供了一种新方法。
本研究的目的是从第一人称的角度确定腰痛体验的背景变量，以深入了解个人的信念和观念。
我们分析了 2014 年 1 月 1 日至 2018 年 12 月 31 日期间与腰痛相关的 896867 条推文。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、双项主题模型和非负矩阵分解，以识别与推文相关的主题。我们确定了一个一致性得分来确定最佳模型。两位领域专家分别对具有最强一致性得分的主题进行了定性内容分析，并将其分为上下文类别。专家们开会并解决了任何分歧，并制定了最终标签。
LDA 优于所有其他算法，导致最高的一致性得分。最佳模型是具有 60 个主题的 LDA，一致性得分为 0.562。这 60 个主题被分为 19 个上下文类别。“情感与信念”的总推文比例最大（157563/896867，17.6%），其次是“体育活动”（124251/896867，13.85%）和“日常生活”（80730/896867，9%），而“食物和饮料”、“天气”和“不被理解”的比例最小（11551/896867，1.29%；10109/896867，1.13%；9180/896867，1.02%）。在“情感与信念”的 11 个主题中，有 113562/157563（72%）具有消极情绪。
对腰痛领域推文的内容分析确定了与传统定性研究结果一致的常见主题，但提供了个体与腰痛相关观点的更细粒度视图。这种理解有可能帮助制定更有效的个性化护理模式，以改善腰痛患者的结局。
