Evidence map›Paper›PMID 38713506›Full record

ArticleJMIR cancer2024

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

Nancy Lau, Xin Zhao, Alison O'Daffer, Hannah Weissman, Krysta Barton

Abstract read
In one paragraph

Article in JMIR cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. A Content Analysis of #Childhoodcancer Chatter on X.Journal of adolescent and young adult oncology · 2025
    Article
  3. Article
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nancy LauCenter for Child Health, Behavior and Development, Seattle Children's Research Institute, Seattle, WA, United States.ORCID https://orcid.org/0000-0002-2160-5362
Xin ZhaoDepartment of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, United States.ORCID https://orcid.org/0000-0001-8455-9757
Alison O'DafferDepartment of Psychiatry, University of California, San Diego, San Diego, CA, United States.ORCID https://orcid.org/0000-0002-4086-9574
Hannah WeissmanDepartment of Psychology, Vanderbilt University, Nashville, TN, United States.ORCID https://orcid.org/0009-0008-9699-0227
Krysta BartonBiostatistics Epidemiology and Analytics for Research (BEAR) Core, Seattle Children's Research Institute, Seattle, WA, United States.ORCID https://orcid.org/0000-0002-3424-3911

Funding

Pilot trial testing mobile health psychosocial intervention for Adolescentsand Young Adults with cancerK08CA263474 · NCI · SEATTLE CHILDREN'S HOSPITAL · PI Nancy Lau · 2022 to 2026
$1.0M
NCI NIH HHS K08 CA263474
6 · The paper itself

Abstract

backgroundDuring 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.

objectiveThis 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.

methodsAll 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.

resultsA 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.

conclusionsTwitter 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.

Indexed as

attitudeattitudescancercancerscaregiverschildchild healthchildren’ youthcommunicationCOVID-19experienceexperiencesoncologyopinionopinionspediatricpediatricsperceptionperceptionsperspectiveperspectivessentimentsocial mediatweettweetsTwitter

Identifiers

PMID38713506
PMCPMC11109854

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.