Examining the Association of Academic Rank and Productivity with Metrics of Twitter Utilization Amongst Kidney Cancer Specialists

Abstract

BACKGROUND: Twitter has emerged as an important platform for conversation surrounding cancer-related topics. As use has proliferated, a better classification of physicians engaging in cancer discussions on Twitter is warranted. OBJECTIVES: To better characterize the medical specialists involved in disseminating kidney cancer information on social media through academic and Twitter metrics. METHODS: Clinical practitioners with an expertise in kidney cancer were identified. Demographics, metrics of academic rank and productivity, and Twitter usage data were collected. Correlations were calculated for the generation of a model predictive of the number of Twitter followers. Analysis of the experts' Twitter content was performed. RESULTS: Among 59 kidney cancer experts identified, 14 (23.7%) were assistant professors, 24 (40.7%) were associate professors, and 21 (35.6%) were full professors. A total of 5424 tweets were analyzed, 86% of which were medically-related. We identified several differences between academic rank and Twitter variables. Associate professors registered a greater median number of followers subscribed to their Twitter accounts (2360) versus assistant professors (1253) and full professors (934) (p = 0.03) and a greater median number of accounts they themselves followed (752 vs. 290 vs. 235, respectively; p = 0.0009). Use of a more generalized approach (ANCOVA) showed that the most predictive variables for the number of followers are number of tweets, H-index, and percentage of medical tweets (R 2 = 0.70). CONCLUSIONS: This study supported correlations between metrics of academic and Twitter activity. The generation of a model to predict the number of followers on Twitter is novel-future work will validate this in other disease types.