1. Introduction
The use of social media (SoMe), specifically Twitter, as a professional platform is increasingly common among healthcare workers, including urologists[
1]. Urology residents, medical students, programs, journals, and faculty have used Twitter to advertise virtual events, gain information for the match process, form mentorships, promote publications, and share clinical information. The circumstances imposed by the COVID-19 pandemic catalyzed the increasing use of Twitter among the urological community[
2]. The geographical and physical limitations spurred by the pandemic resulted in both academic programs and students alike having to adopt new approaches for communication with a focus on SoMe.
The dramatic reduction of clinical and research activities within the medical and surgical departments during COVID-19, coupled with virtual electives and conferences, have all posed important implications within academics. Furthermore, the current landscape of Twitter use among academic urology faculty at accredited US institutions has yet to be assessed. Given the heavy reliance on virtual interaction during the pandemic and the active role that SoMe plays, our study aims to characterize the state of Twitter use among current academic urology faculty. As Twitter offers an information-rich reservoir created by the urologic academic community, the interactions among users shape complex network structures that have not been previously evaluated. The aim of our study was to characterize the currently academic urology Twitter presence by sex and specialty. We hypothesize that while urologic oncology represents the total largest number of Twitter participants within urology, there is a growing trend among other subspecialties of urology, with an increasing number of interactions.
2. Materials and Methods
2.1. Data source
Data collection occurred from May 2021 to March 2022. A list of accredited US urology residency programs was pulled from the American Urological Association (AUA). All MD/DO faculty associated with the US urology residency program and academic centers were included in the study. Information including sex, program location, and subspecialty training were recorded from their respective websites. Faculty Twitter account was verified via a 2-factor verification process (name plus location) in addition to automated Twitter match. Transplant was not included in some analyses because of the low number of participating faculty on Twitter, while fellowship-trained sexual medicine faculty was included in the andrology group.
2.2. Collection of Twitter information
Collection of relevant tweets from 2006 until March 2022 was performed. The Twitter streaming application programming interface (API) dataset creation has previously been described[
1]. Briefly, tweets were collected using Twitter Streaming API using Python (v3.10.8). All tweets from predesignated academic faculty were used for analysis. In addition, all accounts followed by the user (following), accounts following the user (followers), timelines, and geographic location (when available from Twitter privacy setting) were collected using rtweet (R version 4.2.2). Further information and code regarding extraction of user-specific data can be found here (
https://github.com/ropensci/rtweet/)[
3]. After data preprocessing, relevant tweets were selected and analyzed. Tableau Desktop was fed into CSV and Excel files for data visualization.
2.3. Natural language processing (NLP) pipeline
All elements of microblogging, including retweets, likes, followers, following, sentiment, hashtags, and mentions were captured through Anaconda Navigator. Full-text tweets were preprocessed by converting the sentences into words (Tokenization) and removing unnecessary punctuations, tags, and stop words that do not have a specific semantic meaning (“the,” “are”). Preprocessing was done using the Natural Language Toolkit (NLTK) on Python (v3.10.8).
2.4. Sentiment analysis
Following processing of tweets and removal of duplicates and unnecessary punctuations, all tweets were split into 3 data frames based on sentiment (neural, negative, and positive). Sentiment analysis refers to identifying and classifying tweets that are expressed in text using the machine learning sentiment analysis model to compute users’ perception. Sentiment analysis was done using (
https://github.com/yalinyener/TwitterSentimentAnalysis) package in Python (v3.10.8).
2.5. Twitter interaction analysis
While the rtweet package allows for downloading tweets and stream API, extraction of tweets relevant to a topic at hand (designated around a hashtag #), followed by building of interaction centered around users (nodes), was performed via twinetverse in R and is freely available here (
http://graphtweets.john-coene.com/). Each interaction consists of a source and a target; in other words, the source is the screen name (faculty), the user who posted the tweet, while target is the users who were tagged or interacted with the tweet. This type of analysis allowed for interaction factor between subspecialties (grouped faculty) based on topic of discussion. Although we chose to group based on urologic subspecialty, this analysis can be user specific or topic specific. Interaction analysis was depicted as the number of interactions between the subspecialties only, and no regression analysis was performed to test for statistical significance as interaction is dependent on the topic.
2.6. Statistical analysis
Demographics were summarized using descriptive statistics. The categorical variables are presented as counts and percentages and were compared using a chi-square test. The continuous variables are expressed as means (standard deviation [SD]) or medians (interquartile range [IQR]). All statistical analyses were conducted by a statistician (M.S.) and cross referenced by a physician (L.B.) using R version 4.0.4. All tests were 2-sided with statistical significance defined by P < 0.05. All visualizations were performed by Tableau Desktop and Corel Draw.
4. Discussion
The steady increase in Twitter representation among academic urologists since 2006 reflects the increasing awareness of Twitter as a means of academic repre-sentation and promotion among individual programs and faculty. A recent study has also found increasing Twitter usage among urologists, prompted by different incentives. Urologists in the US and Canada who were in lower academic ranking and had higher H-indices were more likely to have Twitter accounts[
4].
The challenges imposed by COVID-19 limited in-person interactions, forcing the medical community to embrace other forms of communication. Although we did not see any notable changes in the number of registered accounts for faculty during COVID-19 (2019–2020), there was a significant jump in urology program account creation in 2020, the largest increase since 2009[
5].
While the majority of Twitter representation is largely skewed toward male faculty, we found a steady increase in female faculty representation across all urology subspecialties over the past 16 years. Proportionately, more female urologists use Twitter. This might be an effort of female urologists to build their professional reputation and to inspire other aspiring female surgeons. Besides the goal of improving healthcare, Twitter presence has been shown to increase industry support, with surgeons with an active Twitter account receiving 1.7 times the amount in payments compared with surgeons without an active account. Furthermore, among Twitter users, those with 321 to 172 000 followers received 4.7 times and 9.5 times the amount in payments compared with those with 0 to 80 followers[
6].
One of the top hashtags used by female faculty, but not in the top for male faculty, was #ilooklikeasurgeon. The sex differences in Twitter activity highlight how academic female urologists use SoMe as a platform for advocacy and cultural change initiatives. Women were also more likely to mention or tweet to a male and female urologist on Twitter, while male academic urologists limited their interaction to male colleagues as evidenced by top mentions. This finding is not unique to urology. In a recent study by Zhu et al., which evaluated a total of 3148 health services researchers on Twitter, women were more likely to follow other women (54.8% of users followed by women were women, whereas 42.6% of users followed by men were women)[
7]. Academic urologists fellowship trained in andrology, reconstruction, endourology, and oncology had the most Twitter representation. This is supported by data previously published, showing that physicians in the US and Canada trained in urological oncology, minimally invasive urology, and endourology were more likely to have Twitter accounts[
4]. One possible explanation for the high percentage of andrology faculty could be attributed to the stigma inherent to the topics within the subspecialty, such as sexual health and male infertility. Because these conversations may be difficult for patients to discuss openly, andrology faculty are likely using SoMe as an advocacy platform.
The most frequently used hashtags by academic urologists were dedicated hashtags created for annual AUA conferences. These hashtags not only organize posts associated with a certain conference but also reflect the increasing use of Twitter during medical conferences. In a 2017 survey of AUA members, a third of the 1280 respondents with SoMe accounts reported following a medical conference virtually[
8]. The 2018 AUA annual conference had a significant, 5-fold increase in Twitter posts compared to 2013[
9]. Another hashtag that dramatically increased in occurrence was #covid19. In just over 2 years, #covid19 surpassed others as one of the top 5 most frequently used hashtags in the past 16 years. This likely reflects the increased consumption of SoMe as a source of information on COVID-19. Valdez et al. showed that Twitter volume increased consistently from early to late March 2020, around when COVID-19 was declared a global pandemic[
10].
Our study is unique in that compares direct interaction among subspecialties within academic urology on Twitter. While previous research has focused mainly on trends of utilization and growth, our innovative analysis fostered by information networks and a novel API network, highlights the communication and engagement among the subspecialties. Based on our findings, we can establish that virtual interaction on Twitter is dependent upon the topic; however, strong intersubspecialty ties are seen from FPMRS and pediatric urology. As we previously mentioned, while it may seem that urologic oncology by number alone had the highest level of interaction, proportionally, FPMRS and pediatric urology were more likely to engage other subspecialties. It is unclear why this network structure exists, and the implications of this type of interaction; however, a higher level of interaction is more likely to spread to other networks and enhance the spread of information (ie, reach more users).
Our study is not without limitations. We analyzed only academic urologists. Thus, urologists who work in private practice or other non-academic institutions were not able to be accounted for. There are many urologists on social media who were not included in the analysis but represent an integral role in advocacy and growth of urologic knowledge (eg, Ashley Winter). Furthermore, faculty designation to subspecialty was based on urologists’ academic institution information, and while most were able to be categorized into a subspecialty, the process was dependent on the accuracy of information listed by the institution.