推特上的儿科癌症传播:自然语言处理和定性内容分析。

IF 3.3 Q2 ONCOLOGY JMIR Cancer Pub Date : 2024-05-07 DOI:10.2196/52061
Nancy Lau, Xin Zhao, Alison O'Daffer, Hannah Weissman, Krysta Barton
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引用次数: 0

摘要

背景:在 COVID-19 大流行期间,Twitter(最近更名为 "X")是使用最广泛的社交媒体平台,与癌症相关的推文超过 200 万条。患者和家庭成员、医疗机构和组织越来越多地使用社交媒体,这为研究癌症传播提供了新方法:本研究旨在研究儿科癌症相关推文,以捕捉癌症患者和幸存者、其护理人员、医疗服务提供者和其他利益相关者的经历。我们评估了 COVID-19 大流行期间与儿科癌症相关的推文的公众情绪和内容:我们使用 Twitter 应用程序接口获取了从 2019 年 12 月 11 日到 2022 年 5 月 7 日全球范围内发布的所有与儿科癌症相关的英文推文。根据 Bing、AFINN 和 NRC 词库进行了情感分析。我们使用 ChatGPT(3.0 版)进行了基于非词库的补充情感分析,通过 150 条推文的随机子集验证了我们的研究结果。我们还进行了定性内容分析,对 800 条推文的随机子集进行了人工编码:结果:共识别出 161,135 条与儿科癌症相关的独特推文。情感分析显示,正面词汇多于负面词汇。通过必应词典,最常见的正面词汇是支持、爱、神奇、天堂和快乐,最常见的负面词汇是悲伤、风险、艰难、虐待和错过。通过 NRC 词库,大多数推文被归类为积极、信任和快乐等情感类型。不同词库中的总体积极情绪是一致的,并通过 ChatGPT(3.0 版)补充分析得到了证实。定性编码的评分者之间的一致率为 91%,排名前 10 位的编码分别是意识、个人经历、研究、护理者经历、患者经历、政策和法律、治疗、生命终结、药品和药物以及幸存者。定性内容分析显示,Twitter 用户通常使用社交媒体平台提高公众对儿科癌症的认识,并从患者或幸存者及其照顾者的角度分享儿科癌症的个人经历。推特经常被用于传播健康知识,包括研究成果以及支持治疗和负担得起的医疗保健的联邦政策:Twitter可作为研究人员研究全球儿科癌症传播和公众情绪的有效手段。尽管在 COVID-19 大流行期间出现了公众心理健康危机,但儿科癌症相关推文的总体情绪是积极的。儿科癌症推文的内容主要集中在健康和治疗信息、社会支持以及提高人们对儿科癌症的认识等方面。
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Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis.

Background: During the COVID-19 pandemic, Twitter (recently rebranded as "X") was the most widely used social media platform with over 2 million cancer-related tweets. The increasing use of social media among patients and family members, providers, and organizations has allowed for novel methods of studying cancer communication.

Objective: This study aimed to examine pediatric cancer-related tweets to capture the experiences of patients and survivors of cancer, their caregivers, medical providers, and other stakeholders. We assessed the public sentiment and content of tweets related to pediatric cancer over a time period representative of the COVID-19 pandemic.

Methods: All English-language tweets related to pediatric cancer posted from December 11, 2019, to May 7, 2022, globally, were obtained using the Twitter application programming interface. Sentiment analyses were computed based on Bing, AFINN, and NRC lexicons. We conducted a supplemental nonlexicon-based sentiment analysis with ChatGPT (version 3.0) to validate our findings with a random subset of 150 tweets. We conducted a qualitative content analysis to manually code the content of a random subset of 800 tweets.

Results: A total of 161,135 unique tweets related to pediatric cancer were identified. Sentiment analyses showed that there were more positive words than negative words. Via the Bing lexicon, the most common positive words were support, love, amazing, heaven, and happy, and the most common negative words were grief, risk, hard, abuse, and miss. Via the NRC lexicon, most tweets were categorized under sentiment types of positive, trust, and joy. Overall positive sentiment was consistent across lexicons and confirmed with supplemental ChatGPT (version 3.0) analysis. Percent agreement between raters for qualitative coding was 91%, and the top 10 codes were awareness, personal experiences, research, caregiver experiences, patient experiences, policy and the law, treatment, end of life, pharmaceuticals and drugs, and survivorship. Qualitative content analysis showed that Twitter users commonly used the social media platform to promote public awareness of pediatric cancer and to share personal experiences with pediatric cancer from the perspective of patients or survivors and their caregivers. Twitter was frequently used for health knowledge dissemination of research findings and federal policies that support treatment and affordable medical care.

Conclusions: Twitter may serve as an effective means for researchers to examine pediatric cancer communication and public sentiment around the globe. Despite the public mental health crisis during the COVID-19 pandemic, overall sentiments of pediatric cancer-related tweets were positive. Content of pediatric cancer tweets focused on health and treatment information, social support, and raising awareness of pediatric cancer.

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来源期刊
JMIR Cancer
JMIR Cancer ONCOLOGY-
CiteScore
4.10
自引率
0.00%
发文量
64
审稿时长
12 weeks
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