Using Social Media to Understand Primary Discussions in Gastrointestinal Cancers: Machine Learning Approach.

IF 2.4 3区 医学 Q1 NURSING Cancer Nursing Pub Date : 2024-07-16 DOI:10.1097/NCC.0000000000001381
Claire J Han, Xia Ning, Young Ji Lee, Fode Tounkara, Matthew F Kalady, Anne M Noonan, Diane Von Ah
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Abstract

Background: The incidence and mortality rates of gastrointestinal (GI) cancers are high in the United States as well as worldwide. The widespread use of social media provides unique opportunities to facilitate the dissemination of information, especially in the context of health.

Objective: We aim to characterize the public's primary discussions, including perceptions, concerns, and interests toward GI cancers, from prevention, diagnosis, and treatment to survivorship care through the social media platform Twitter, using tweets posted by Twitter users.

Methods: We analyzed 87 860 Twitter posts related to GI cancers. We used machine learning with natural language processing to identify salient topics and themes in the collected tweets.

Results: The most common themes across all GI cancer types included cancer risk prevention and awareness outreach programs, risk factors including lifestyles (primarily diet), and cancer survivorship-related discussions (primarily GI symptoms and quality of life). GI symptom-related tweets were prevalent in patients with colorectal and stomach cancers, whereas themes of newer clinical trials, end-of-life trials, palliative care trials, and disease prognosis were common in tweets related to liver/biliary and pancreatic cancers.

Conclusions: Our research emphasizes the importance of individualized approaches in managing GI cancers, considering lifestyle and diet, the need for comprehensive survivorship care, raising awareness, delivering information, and improving targeted interventions related to GI cancers.

Implications for practice: Our study suggests utilizing Twitter data to better understand the real-world interest and concerns about GI cancers among the public, which can guide future patient-centered research in this field.

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利用社交媒体了解胃肠道癌症的主要讨论:机器学习方法。
背景:在美国和全世界,胃肠道癌症(GI)的发病率和死亡率都很高。社交媒体的广泛使用为促进信息传播提供了独特的机会,尤其是在健康方面:我们旨在通过社交媒体平台 Twitter,利用 Twitter 用户发布的推文来描述公众的主要讨论内容,包括对消化道癌症从预防、诊断、治疗到生存护理的看法、关注和兴趣:我们分析了 87 860 条与消化道癌症相关的 Twitter 帖子。方法:我们分析了 87 860 条与消化道癌症相关的 Twitter 帖子,并使用机器学习和自然语言处理技术来识别所收集推文中的突出主题和主题:所有消化道癌症类型中最常见的主题包括癌症风险预防和宣传计划、包括生活方式(主要是饮食)在内的风险因素以及癌症幸存者相关讨论(主要是消化道症状和生活质量)。与消化道症状相关的推文在结直肠癌和胃癌患者中很普遍,而较新的临床试验、临终试验、姑息治疗试验和疾病预后等主题在与肝癌/胆癌和胰腺癌相关的推文中很常见:我们的研究强调了个体化方法在消化道癌症管理中的重要性,考虑到了生活方式和饮食、全面幸存者护理的需要、提高认识、提供信息以及改善与消化道癌症相关的针对性干预措施:我们的研究建议利用推特数据更好地了解公众对消化道癌症的实际兴趣和担忧,从而指导该领域未来以患者为中心的研究。
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来源期刊
Cancer Nursing
Cancer Nursing 医学-护理
CiteScore
4.80
自引率
3.80%
发文量
244
审稿时长
6-12 weeks
期刊介绍: Each bimonthly issue of Cancer Nursing™ addresses the whole spectrum of problems arising in the care and support of cancer patients--prevention and early detection, geriatric and pediatric cancer nursing, medical and surgical oncology, ambulatory care, nutritional support, psychosocial aspects of cancer, patient responses to all treatment modalities, and specific nursing interventions. The journal offers unparalleled coverage of cancer care delivery practices worldwide, as well as groundbreaking research findings and their practical applications.
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