ArticleInternational journal of environmental research and public health2018
Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis.
Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- [Short paths to diagnosis with artificial intelligence: systematic literature review on diagnostic decision support systems].Schmerz (Berlin, Germany) · 2024Pooled it
- Opportunities and pitfalls of social media research in rare genetic diseases: a systematic review.Genetics in medicine : official journal of the American College of Medical Genetics · 2021Pooled it
- "Do It by Myself" or Autonomy, Participation, and Assistive Devices and Technology Needs of Children and Youth With Disabilities: Text Mining Analysis of a National Survey in France.JMIR medical informatics · 2026Article
- Emotional journey of patients with specified intractable diseases in Japan.Intractable & rare diseases research · 2024Article
- Content analysis of patient support groups related to myositis on Facebook.Clinical rheumatology · 2024Article
- Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs.Frontiers in neurology · 2024Article
- Challenges associated with delayed definitive diagnosis among Japanese patients with specific intractable diseases: A cross-sectional study.Intractable & rare diseases research · 2023Article
- A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D.Orphanet journal of rare diseases · 2023Article
- Weak and strong ties and its connection to experts' problem-solving styles in scaffolding students' PBL activities on social media.F1000Research · 2021Article
- Pain drawings as a diagnostic tool for the differentiation between two pain-associated rare diseases (Ehlers-Danlos-Syndrome, Guillain-Barré-Syndrome).Orphanet journal of rare diseases · 2020Observational
- Biomedical Holistic Ontology for People with Rare Diseases.International journal of environmental research and public health · 2020Article
- A proof-of-concept study of extracting patient histories for rare/intractable diseases from social media.Genomics & informatics · 2020Article
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Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis. The Facebook analysis shows that posts with photos and positive comments have the highest engagement. We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue. Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day.
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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.