@文章{信息:doi/10.2196/26093，作者=“Robert and Delir Haghighi, Pari and Burstein, Frada and Urquhart, Donna and Cicuttini, Flavia”，标题=“调查个体对腰痛经历的背景的感知:推特数据的主题建模分析”，期刊=“J Med Internet Res”，年=“2021”，月=“12”，日=“23”，量=“23”，数=“12”，页=“e26093”，关键词=“腰痛;推特;内容分析;社交媒体;主题建模;以病人为中心的方法;痛苦的经验;背景:腰痛(LBP)仍然是全球致残的主要原因。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。 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. Objective: 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. Methods: 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. 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. ", issn="1438-8871", doi="10.2196/26093", url="//www.mybigtv.com/2021/12/e26093", url="https://doi.org/10.2196/26093" }