ReviewNeuro-oncology advances
Current landscape of social media use pertaining to glioblastoma by various stakeholders.
Review in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Social Media Discussions About Robotic Total Knee Arthroplasty: Cross-Sectional Analysis.JMIR infodemiology · 2025Article
- Leveraging social media data to study disease and treatment characteristics of Hodgkin's lymphoma Using Natural Language Processing methods.PLOS digital health · 2025Article
- Social media "SoMe" in neuro-oncology: a review of the literature.Journal of neuro-oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Given the potential for social media to allow widespread public engagement, its role in healthcare, including in cancer care as a support network, is garnering interest. To date, the use of social media in neuro-oncology has not been systematically explored. In the current manuscript, we sought to review Twitter use on glioblastoma among patients, caregivers, providers, researchers, and other stakeholders. Methods: The Twitter application programming interface (API) database was surveyed from inception to May 2022 to identify tweets about glioblastoma. Number of tweet likes, retweets, quotes, and total engagement were noted for each tweet. Geographic location, number of followers, and number of Tweets were noted for users. We also categorized Tweets based on their underlying themes. A natural language processing (NLP) algorithm was used to assign a polarity score, subjectivity score, and analysis label to each Tweet for sentiment analysis. Results: A total of 1690 unique tweets from 1000 accounts were included in our analyses. The frequency of tweets increased from 2013 and peaked in 2018. The most common category among users was MD/researchers (21.6%, Conclusion: This comprehensive analysis of tweets on glioblastoma found that the academic community are the most common user group on Twitter. Sentiment analysis revealed that most negative tweets are related to personal experience. These analyses provide the basis for further work into supporting and developing the care of patients with glioblastoma.
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.