%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 23
%N 6
%P e28648
%T Language and Sentiment Regarding Telemedicine and COVID-19 on Twitter: Longitudinal Infodemiology Study
%A Pollack,Catherine C
%A Gilbert-Diamond,Diane
%A Alford-Teaster,Jennifer A
%A Onega,Tracy
%+ Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, 1 Medical Center Drive, Lebanon, NH, 03766, United States, 1 540 497 3419, Catherine.c.pollack.gr@dartmouth.edu
%K telemedicine
%K telehealth
%K COVID-19 pandemic
%K social media
%K sentiment analysis
%K Twitter
%K COVID-19
%K pandemic
%D 2021
%7 21.6.2021
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: The COVID-19 pandemic has necessitated a rapid shift in how individuals interact with and receive fundamental services, including health care. Although telemedicine is not a novel technology, previous studies have offered mixed opinions surrounding its utilization. However, there exists a dearth of research on how these opinions have evolved over the course of the current pandemic. Objective: This study aims to evaluate how the language and sentiment surrounding telemedicine has evolved throughout the COVID-19 pandemic. Methods: Tweets published between January 1, 2020, and April 24, 2021, containing at least one telemedicine-related and one COVID-19–related search term (“telemedicine-COVID”) were collected from the Twitter full archive search (N=351,718). A comparator sample containing only COVID-19 terms (“general-COVID”) was collected and sampled based on the daily distribution of telemedicine-COVID tweets. In addition to analyses of retweets and favorites, sentiment analysis was performed on both data sets in aggregate and within a subset of tweets receiving the top 100 most and least retweets. Results: Telemedicine gained prominence during the early stages of the pandemic (ie, March through May 2020) before leveling off and reaching a steady state from June 2020 onward. Telemedicine-COVID tweets had a 21% lower average number of retweets than general-COVID tweets (incidence rate ratio 0.79, 95% CI 0.63-0.99; P=.04), but there was no difference in favorites. A majority of telemedicine-COVID tweets (180,295/351,718, 51.3%) were characterized as “positive,” compared to only 38.5% (135,434/351,401) of general-COVID tweets (P<.001). This trend was also true on a monthly level from March 2020 through April 2021. The most retweeted posts in both telemedicine-COVID and general-COVID data sets were authored by journalists and politicians. Whereas the majority of the most retweeted posts within the telemedicine-COVID data set were positive (55/101, 54.5%), a plurality of the most retweeted posts within the general-COVID data set were negative (44/89, 49.4%; P=.01). Conclusions: During the COVID-19 pandemic, opinions surrounding telemedicine evolved to become more positive, especially when compared to the larger pool of COVID-19–related tweets. Decision makers should capitalize on these shifting public opinions to invest in telemedicine infrastructure and ensure its accessibility and success in a postpandemic world. 
%M 34086591
%R 10.2196/28648
%U https://www.jmir.org/2021/6/e28648
%U https://doi.org/10.2196/28648
%U http://www.ncbi.nlm.nih.gov/pubmed/34086591