Valle Luca F, Chu Fang-I, Smith Marc, Wang Chenyang, Lee Percy, Moghanaki Drew, Chino Fumiko L, Steinberg Michael L, Raldow Ann C
UCLA Department of Radiation Oncology, University of California Los Angeles, Los Angeles, California.
Social Media Research Foundation, Redwood City, California.
Adv Radiat Oncol. 2022 Mar 23;7(6):100919. doi: 10.1016/j.adro.2022.100919. eCollection 2022 Nov-Dec.
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.
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.
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.
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.
放射肿瘤学虚拟话语的上层结构以及在放射肿瘤学社交媒体格局中占据有影响力地位的实体，目前仍缺乏明确的特征描述。
在1年期间（2018年12月4日至2019年11月29日），每8至10天使用NodeXL Pro对所有带有#radonc标签的推文进行前瞻性抽样。使用Clauset-Newman-Moore社区检测算法将Twitter账号分组为对话集群。对于每个抽样期，对使用介数中心性定义的前10名#radonc Twitter有影响力的人进行分类。在每个抽样期，根据有影响力的人的前10名影响力排名对他们进行评分，并用描述性统计进行总结。线性回归评估预测顶级有影响力的人获得更高影响力分数的特征。
在该研究中，在38个时期内共抽样了684,000条推文。#radonc推文呈现出一种中心辐射状广播网络的人群上层结构，这种结构是在许多受众广泛转发知名人士的内容时形成的。专业协会是Twitter账号中最具影响力的类别，平均影响力得分为7.63（满分10分，标准差[SD]=1.94）。当行业账号出现在前10名有影响力的人之中时，它们的平均影响力得分排第二高（6.75，SD=1.06），其次是个人，得分为5.28（SD=0.43）。在1年的时间里，有影响力的人的类别保持稳定。北美地区的主治医生角色、放射肿瘤学专业、男性、学术实践以及基于美国的账号是获得更高影响力分数的预测因素。
放射肿瘤学领域的Twitter有影响力的人代表了不同的人群和组织，但北美地区的男性放射肿瘤学学术专家在广泛被描述为“中心辐射状”广播网络的虚拟社区中占据着特别有影响力的地位。有必要定期对放射肿瘤学的社交媒体话语进行基于网络的分析，以了解正在影响Twitter上讨论的账号，并确保社交媒体的使用继续以有意义的方式为放射肿瘤学领域做出贡献。
