Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis

被引:2
作者
Lau, Nancy [1 ]
Zhao, Xin [2 ]
O'Daffer, Alison [3 ,4 ]
Weissman, Hannah [5 ]
Barton, Krysta [6 ]
机构
[1] Seattle Childrens Res Inst, Ctr Child Hlth Behav & Dev, 1920 Terry Ave, Seattle, WA 98101 USA
[2] Univ Washington, Sch Med, Dept Psychiat & Behav Sci, Seattle, WA USA
[3] Univ Calif San Diego, Dept Psychiat, San Diego, CA USA
[4] Univ Calif San Diego, Sanford Inst Empathy & Compass, Ctr Empathy & Technol, San Diego, CA USA
[5] Vanderbilt Univ, Dept Psychol, Nashville, TN USA
[6] Seattle Childrens Res Inst, Biostat Epidemiol & Analyt Res BEAR Core, Seattle, WA 98101 USA
来源
JMIR CANCER | 2024年 / 10卷
基金
美国国家卫生研究院;
关键词
cancer; COVID-19; Twitter; communication; child health; caregivers; social media; tweet; tweets; sentiment; oncology; cancers; pediatric; pediatrics; child; children' youth; experience; experiences; attitude; attitudes; opinion; opinions; perception; perceptions; perspective; perspectives; UNITED-STATES; CARE;
D O I
10.2196/52061
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
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.
引用
收藏
页数:14
相关论文
共 46 条
  • [1] Analysis of Tweets Containing Information Related to Rheumatological Diseases on Twitter
    Abbasi-Perez, Adrian
    Angel Alvarez-Mon, Miguel
    Donat-Vargas, Carolina
    Ortega, Miguel A.
    Monserrat, Jorge
    Perez-Gomez, Ana
    Sanz, Ignacio
    Alvarez-Mon, Melchor
    [J]. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 2021, 18 (17)
  • [2] Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study
    Abd-Alrazaq, Alaa
    Alhuwail, Dari
    Househ, Mowafa
    Hamdi, Mounir
    Shah, Zubair
    [J]. JOURNAL OF MEDICAL INTERNET RESEARCH, 2020, 22 (04)
  • [3] Managing Cancer Care during the COVID-19 Pandemic and Beyond
    Alhalabi, Omar
    Subbiah, Vivek
    [J]. TRENDS IN CANCER, 2020, 6 (07): : 533 - 535
  • [4] A lexicon-based approach to examine depression detection in social media: the case of Twitter and university community
    Cha, Junyeop
    Kim, Seoyun
    Park, Eunil
    [J]. HUMANITIES & SOCIAL SCIENCES COMMUNICATIONS, 2022, 9 (01):
  • [5] ChatGPT, 2022, Optimizing language models for dialogue
  • [6] COVID-19 in the Twitterverse, from epidemic to pandemic: information-sharing behavior and Twitter as an information carrier
    Chong, Miyoung
    Park, Han Woo
    [J]. SCIENTOMETRICS, 2021, 126 (08) : 6479 - 6503
  • [7] Engagement of Institutions in Human Subjects Research, 2008, About us
  • [8] "The stakes could not be higher": A mixed methods study of parental social media use in pediatric oncology
    Foot, Elizabeth
    Leonhard, Amanda
    Majeski, Jill
    Zahn, Lauren
    Li, Hsin H.
    Brown, Amy E. Caruso
    [J]. PEDIATRIC BLOOD & CANCER, 2021, 68 (11)
  • [9] Loneliness and depression in patients with cancer during COVID-19
    Gallagher, Stephen
    Bennett, Kate Mary
    Roper, Louise
    [J]. JOURNAL OF PSYCHOSOCIAL ONCOLOGY, 2021, 39 (03) : 445 - 451
  • [10] Three approaches to qualitative content analysis
    Hsieh, HF
    Shannon, SE
    [J]. QUALITATIVE HEALTH RESEARCH, 2005, 15 (09) : 1277 - 1288