ArticleAdvances in radiation oncology
Characterizing Twitter Influencers in Radiation Oncology.
Article in Advances in radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- Academic Influencers on X (former Twitter) and Their Impact on Research Metrics: A Narrative Review and Correlation Analysis of Top 100 Altmetric Articles (2017-2021).Medical journal of the Islamic Republic of Iran · 2026Article
- Inflammatory Bowel Disease and X (Formerly Twitter) Influencers: Who Are They and What Do They Say?Cureus · 2023Article
- A Scoping Review of Physician Advocacy on Twitter.Cureus · 2023Article
- American Society for Radiation Oncology'sAdvances in radiation oncologyArticle
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Purpose: Both the superstructures of virtual discourse in radiation oncology and the entities occupying influential positions in the social media landscape of radiation oncology remain poorly characterized. Methods and Materials: NodeXL Pro was used to prospectively sample all tweets with the hashtag #radonc every 8 to 10 days during the course of 1 year (December 4, 2018, to November 29, 2019). Twitter handles were grouped into conversational clusters using the Clauset-Newman-Moore community detection algorithm. For each sample period, the top 10 #radonc Twitter influencers, defined using betweenness centrality, were categorized. Influencers were scored in each sample period according to their top 10 influence rank and summarized with descriptive statistics. Linear regression assessed for characteristics that predicted higher influence scores among top influencers. Results: In the study, 684,000 tweets were sampled over 38 periods. #radonc tweets took on the crowd superstructure of a hub-and-spoke broadcast network formed when prominent individuals are widely repeated by many audience members. Professional societies were the most influential category of Twitter handles with an average influence score of 7.63 out of 10 (standard deviation [SD] = 1.94). When industry handles were present among top 10 influencers, they exhibited the second highest average influence scores (6.75, SD = 1.06), followed by individuals with scores of 5.28 (SD = 0.43). The categories of influencers were stable during the course of 1 year. The role of attending physician, radiation oncology specialty, male sex, academic practice, and US-based handles in North America were predictors of higher influence score. Conclusions: Twitter influencers in radiation oncology represent a diverse group of people and organizations, but male academic radiation oncologists based in North America occupy particularly influential positions in virtual communities broadly characterized as "hub and spoke" broadcast networks. Periodic network-based analyses of the social media discourse in radiation oncology are warranted to maintain an awareness of the handles that are influencing discussions on Twitter and ensure that social media utilization continues to contribute to the field of radiation oncology in a meaningful way.
Identifiers
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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.