ArticleJMIR cancer2018
How to Optimize Health Messages About Cancer on Facebook: Mixed-Methods Study.
Article in JMIR cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Contextualizing Engagement With Health Information on Facebook: Using the Social Media Content and Context Elicitation Method.Journal of medical Internet research · 2022Article
- Overview of Facebook Use by Hospitals in Italy: A Nationwide Survey during the COVID-19 Emergency.International journal of environmental research and public health · 2021Review
- Misinformation on social networks during the novel coronavirus pandemic: a quali-quantitative case study of Brazil.BMC public health · 2021Article
- Breast cancer on social media: a quali-quantitative study on the credibility and content type of the most shared news stories.BMC women's health · 2021Article
- Use of Facebook to Serve Information Needs of Persons with Diabetes in the Philippines amid the COVID-19 Pandemic.Journal of the ASEAN Federation of Endocrine Societies · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIncidence rate of cancer is increasing worldwide, with longer life expectancy being one of the main causes. Yet, between 30% and 50% of cancer cases are preventable, and early detection contributes to a better prognosis. This makes health communication strategies essential. Facebook, the world's most used social networking site in 2017 and 2018, can be a useful tool for disseminating powerful messages on health promotion, prevention, and early detection.
objectiveWe aimed to (1) offer ways of optimizing health messages about cancer on Facebook, focusing on topics, such as risk factors, prevention, treatment, early diagnosis, and cure, and (2) investigate which aspects of these messages generate greater engagement.
methodsTo verify what generates greater engagement in topics related to cancer on Facebook, we analyzed 16 Brazilian pages with the main theme of cancer. We performed a manual analysis of texts, content, and engagement rates. Finally, we developed a software program to operationalize the analysis of Facebook posts. The tool we devised aims to automate the analysis of any Facebook page with cancer as the main theme.
resultsWe analyzed 712 posts over a 1-month period. We divided the posts into the following 8 categories: "Testimonies or real-life stories," "Solidarity," "Anniversaries," "Science and health," "Events," "Institutional," "Risk factors," and "Beauty." The pages were also organized into groups according to the type of profile to which they belonged (ie, hospitals or foundations, informative, nongovernmental organizations, and personal pages).The results showed that the categories generating greater engagement in Brazil were not those with the highest percentage of cancer-related content. For instance, in the "Informative" group the "Testimonies or real-life stories" category generated an engagement of 79.5%. However, only 9.5% (25/261) of the content within the relevant time period dealt with such topics. Another example concerns the category "Science and health." Despite being the one with the highest number of posts (129/261, 49.4%), it scored 5th in terms of engagement. This investigation served as the basis for the development of a tool designed to automate the analysis of Facebook pages. The list of categories and keywords generated by this analysis was employed to feed the system, which was then able to categorize posts appearing on a Facebook page. We tested the system on 163 posts and only 34 were classified incorrectly, which amounts to a 20.8% error rate (79.2% accuracy).
conclusionsThe analysis we conducted by categorizing posts and calculating engagement rates shows that the potential of Facebook pages is often underutilized. This occurs because the categories that generate the greatest engagement are often not those most frequently used. The software developed in this research may help administrators of cancer-related pages analyze their posts more easily and increase public interest as a result.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.