Characterizing Twitter Influencers in Radiation Oncology.

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

SUBMITTER: Valle LF 

PROVIDER: S-EPMC9184867 | biostudies-literature | 2022 Nov-Dec

REPOSITORIES: biostudies-literature