Social Media Discussions About Robotic Total Knee Arthroplasty: Cross-Sectional Analysis.

IF 2.3 Q1 HEALTH CARE SCIENCES & SERVICES
JMIR infodemiology Pub Date : 2025-10-09 DOI:10.2196/69883
Charles Desgagné, Jordan J Levett, Lior M Elkaim, John Antoniou
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引用次数: 0

Abstract

Background: The advent of robotic total knee arthroplasty (TKA) in the field of orthopedics has caused much discussion on social media. As social media grows, its platforms are becoming an increasingly popular medium for health care-related discussions.

Objective: This study aimed to better understand the current public discussion about robotic TKA on social media. We aimed to characterize these discussions by analyzing their contributors, the general sentiment, the temporal trends, and the content.

Methods: A comprehensive search of the Twitter database for academic research was performed from inception (March 2006) to April 1, 2023, to identify all tweets related to robotic TKA. General data regarding the tweets and the accounts were retrieved. ChatGPT-4o (OpenAI) was used to categorize the post's content and the accounts into different categories developed via iterative testing. The content was categorized using a rule-based classification algorithm developed using Python to assign categories based on keyword presence, phrase matching, and syntactic patterns. Regarding the accounts, an automated keyword-based rule engine was implemented in Python to classify accounts based on the account's name and description. We used a lexicon-based natural language processing Python library, via ChatGPT-4o, to assign a sentiment to the tweets and conducted subgroup sentiment analysis.

Results: A total of 2000 tweets were retrieved for analysis. Account analysis revealed that the most prevalent account categories were "medical professionals" (619/2000, 31.0%), "patients and community" (274/2000, 13.7%), and "media and publications" (268/2000, 13.4%). Content analysis revealed that the most prevalent tweet themes were "technology and innovation" (550/2000, 27.5%), "advertising and promotion" (176/2000, 8.8%), and "research and data" (172/2000, 8.6%). Sentiment analysis showed that 61.6% (1231/2000) of the tweets had a positive sentiment, while 9.2% (183/2000) were neutral, and 29.3% (586/2000) had a negative sentiment. Accounts categorized as "institutions" had the highest prevalence of positive sentiment (165/229, 72.1%), while accounts categorized as "media and publications" had the highest prevalence of negative sentiment (88/268, 32.8%). The number of tweets relating to robotic TKA has been steadily rising since 2016, with a peak incidence of 402 (20.1%) tweets published in 2022.

Conclusions: The increased number of tweets with a positive sentiment suggests a positive outlook toward robotic TKA. Institutions had the highest prevalence of positive sentiment, suggesting a possible bias toward positive reporting of robotic TKA, likely for commercial reasons. Media and publications had the highest prevalence of negative sentiment, which may represent skepticism and bias toward negative reporting on robotic technologies in health care. Medical professionals contributed significantly to the discussion about robotic TKA, while patient involvement was relatively small. The number of tweets relating to robotic TKA has been steadily growing since 2016, which indicates that robotic TKA has been gaining in popularity over recent years.

关于机器人全膝关节置换术的社交媒体讨论:横断面分析。
背景:机器人全膝关节置换术(TKA)在骨科领域的出现引起了社交媒体的广泛讨论。随着社交媒体的发展,其平台正在成为越来越受欢迎的医疗保健相关讨论的媒介。目的:本研究旨在更好地了解当前社交媒体上关于机器人TKA的公众讨论。我们的目的是通过分析这些讨论的贡献者、总体情绪、时间趋势和内容来描述这些讨论的特征。方法:对Twitter数据库进行全面的学术研究检索,从成立(2006年3月)到2023年4月1日,识别所有与机器人TKA相关的推文。检索了有关tweet和帐户的一般数据。chatgpt - 40 (OpenAI)用于将帖子的内容和帐户分类为通过迭代测试开发的不同类别。使用使用Python开发的基于规则的分类算法对内容进行分类,该算法根据关键字存在、短语匹配和语法模式分配类别。对于帐户,Python实现了一个基于关键字的自动规则引擎,根据帐户的名称和描述对帐户进行分类。我们使用基于词典的自然语言处理Python库,通过chatgpt - 40为推文分配情感,并进行子组情感分析。结果:总共检索了2000条tweet进行分析。账户分析显示,最普遍的账户类别是“医疗专业人员”(619/2000,31.0%)、“病人和社区”(274/2000,13.7%)和“媒体和出版物”(268/2000,13.4%)。内容分析显示,最流行的推文主题是“技术和创新”(550/2000,27.5%),“广告和推广”(176/2000,8.8%)和“研究和数据”(172/2000,8.6%)。情绪分析显示,61.6%(1231/2000)的推文为正面情绪,9.2%(183/2000)为中性情绪,29.3%(586/2000)为负面情绪。被分类为“机构”的账户的积极情绪患病率最高(165/229,72.1%),而被分类为“媒体和出版物”的账户的消极情绪患病率最高(88/268,32.8%)。自2016年以来,与机器人TKA相关的推文数量一直在稳步上升,2022年发布的推文最高发生率为402条(20.1%)。结论:积极情绪的推文数量的增加表明对机器人TKA的积极前景。机构的积极情绪最为普遍,这表明可能出于商业原因,对机器人TKA的正面报道存在偏见。媒体和出版物的负面情绪最为普遍,这可能代表了对医疗保健机器人技术负面报道的怀疑和偏见。医疗专业人员对机器人TKA的讨论做出了重大贡献,而患者的参与相对较少。自2016年以来,与机器人TKA相关的推文数量一直在稳步增长,这表明机器人TKA近年来越来越受欢迎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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