










































This is an open access article under the terms of a license that permits non-commercial use, provided the original work is properly cited.  
© 2023 The Authors. Société Internationale d'Urologie Journal, published by the Société Internationale d'Urologie, Canada.

Key Words Competing Interests Article Information

Twitter, social media, sex, specialty, 
technology, urology

None declared. Received on October 15, 2022 
Accepted on January 18, 2023 
This article has been peer reviewed.

Soc Int Urol J. 2023;4(2):96–104

DOI: 10.48083/TKEK6928

Social Media Network Analysis of Academic 
Urologists’ Interaction Within Twitter  
Microblogging Environment

Spencer H. Bell,1 Clara Sun,2 Emma Helstrom,1 Justin M. Dubin,3 Ilaha Isali,4 Kirtishri Mishra,4  
Andrew Gianakopoulos,2 Seyed Behzad Jazayeri,6 Mohit Sindhani,7 Lee Ponsky,4 Alexander Kutikov,1 
Casey Seideman,8 Andres Correa,1 Diana Magee,1 Laura Bukavina1,3

1 Department of Urologic Oncology, Fox Chase Cancer Center, Philadelphia, United States 2 Case Western Reserve University School of Medicine, Cleveland, 
United States 3 Loyola University, Chicago, United States 4 Department of Urology, University Hospitals Cleveland Medical Center, Cleveland, United States  
5 Department of Urology, Northwestern University Feinberg School of Medicine, Chicago, United States 6 Department of Urology, University of Florida, Jacksonville, 
United States 7 Indian Institute of Technology, Delhi, India 8 Oregon Health and Science University, Portland, United States

Abstract

Objective To characterize academic urology Twitter presence and interaction by subspecialty designation.

Methods Using Twitter application programming interface of available data, 94 000 specific tweets were extracted 
for the analysis through the Twitter Developer Program. Academic urologists were defined based on American 
Urological Association (AUA) residency program registration of 143 residency programs, with a total of 2377 
faculty. Two of 3-factor verification (name, location, specialty) of faculty Twitter account was used. Additional 
faculty information including sex, program location, and subspecialty were manually recorded. All elements of 
microblogging were captured through Anaconda Navigator. Analyzed tweets were further evaluated using natural 
language processing for sentiment association, mentions, and quote tweeted and retweeted. Network analysis based 
on interactions of academic urologist within specialty for given topic were analyzed using D3 in JavaScript. Analysis 
was performed in Python and R.

Results We identified 143 residency programs with a total of 2377 faculty (1975 men and 402 women). Among all 
faculty, 945 (39.7%) had registered Twitter accounts, with the majority being men (759 [80.40%] versus 185 [19.60%]). 
Although there were more male academic urologists across programs, women within academic urology were more 
likely to have a registered Twitter account overall (46% versus 38.5%) compared with men. When assessing registered 
accounts by sex, there was a peak for male faculty in 2014 (10.05% of all accounts registered) and peak for female 
faculty in 2015 (2.65%). There was no notable change in faculty account registration during COVID-19 (2019–2020). In 
2022, oncology represented the highest total number of registered Twitter users (225), with the highest number of total 
tweets (24 622), followers (138 541), and tweets per user per day (0.32). However, andrology (50%) and reconstruction 
(51.3%) were 2 of the highest proportionally represented subspecialties within academic urology. Within the context 
of conversation surrounding a specified topic (#aua21), female pelvic medicine and reconstructive surgery (FPMRS) 
and endourology demonstrated the total highest number of intersubspecialty conversations.

Conclusions There is a steady increase in Twitter representation among academic urologists, largely unaffected by 
COVID-19. While urologic oncology represents the largest group, andrology and reconstructive urology represent 
the highest proportion of their respective subspecialties. Interaction analysis highlights the variant interaction among 
subspecialties based on topic, with strong direct ties between endourology, FPMRS, and oncology.

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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 land-
scape 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 char-
acterize 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 increas-
ing number of interactions.

Materials and Methods
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 
urolog y residency program and academic centers 
were included in the study. Information including 
sex, program location, and subspecialty training were 

Abbreviations 
API application programming interface
AUA American Urological Association
FPMRS female pelvic medicine and reconstructive surgery
SoMe social media

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.

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]. Brief ly, 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.

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).

Sentiment analysis
Follow ing processing of tweets and remova l 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).

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 

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https://github.com/ropensci/rtweet/
https://github.com/yalinyener/TwitterSentimentAnalysis
https://github.com/yalinyener/TwitterSentimentAnalysis
http://graphtweets.john-coene.com/
http://graphtweets.john-coene.com/
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(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.

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.

Results
Demographics
We identified 143 residency programs through the 
AUA website with a total of 2377 faculty (1975 men 
and 402 women). Among all faculty, 945 (39.75%) had 
registered Twitter accounts, with the majority being men 
(760 [80.42%] versus 185 [19.58%]). However, while the 
overall number of male academic urologists was higher, 
proportionally, female urologists were more likely to 
have a registered Twitter account (46 % versus 38.5%) 
(Figure 1).

The percentage of new Twitter accounts created from 
2006 to October 2021 stratified by sex was similarly 
assessed (Figure 1). Overall, there were more male than 
female accounts registered for every year, with a peak for 
male faculty in 2014 (10.05% of all accounts registered) 
and peak for female faculty in 2015 (2.65% of all accounts 
registered). Conversely, there was no notable change 
during the COVID-19 (2019–2020) peak epidemic.

Faculty fellowships were categorized into andrology, 
endourology, female pelvic medicine and reconstruc-
tive urology (FPMRS), general, oncology, pediatrics, 
reconstruction, robotics, and transplant. Consistent 
with AUA census, there are fewer female urologists 
across all fellowships compared with male urologists. 
Sex differences among fellowship training and SoMe use 
were also evident. For female faculty, andrology (60%), 
reconstruction (60%), and endourology (58.06%) had the 
highest proportion of Twitter representation (Figure 1). 

Among men, androlog y (48.59%), reconstruction 
(48.56%), and oncology (48.33%) had the highest propor-
tion of registered user accounts. General urology for 
both female (19.67%) and male faculty (17.86%) had the 
lowest Twitter representation (Figure 1). When compar-
ing female and male Twitter representation within each 
specialty, female faculty had the highest representation 
in female (45.77%), pediatric (28.29%), and reconstruc-
tive (24.59%) urology, while male faculty had the high-
est representation in oncology (93.30%), and robotics 
(91.85%) (Figure 1).

Program representation
When assessing program representation, The University 
of Colorado (69.57%), Mayo Clinic Rochester (67.86%), 
and Case Western Reserve (65.22%) had the most faculty 
on SoMe, proportional to the size of their program 
(Supplementary Appendix Figure S1). The 5 cities 
with the highest overall Twitter representation were 
Philadelphia (52; 5.50%), New York (5.50%), Cleveland 
(43; 4.55%), New Hyde (34; 3.59%), and Chicago  
(32; 3.38%) (Supplementary Appendix Figure S1).

Twitter activity
The top 5 words by occurrence since 2006 were “urology”, 
“dr”, “great”, “aua”, and “cancer” (Supplementary 
Appendix Figure S1). The most frequently used 
hashtags used by academic faculty users on Twitter were 
#prostatecancer (5369), #urology (3851), #bladdercancer 
(3122), and #covid19 (2839) (Supplementary Appendix 
Figure S1). The top 3 mentions were @daviesbj (14 159), 
@amerurological (12 730), and @urogeek (5124). Overall, 
45.72% of all tweets were considered positive, while 
43.64% were neutral, and 10.64% were negative.

Sex-specific differences were observed. Although the 
top 2 hashtags among female academic urologists were 
#urology (1112) and #covid19 (801), women were more 
likely to mention other female urologists including @
loebstacy (1052), @ashleywinter (741), and caseyseid-
man (544) compared with male faculty (Supplementary 
Appendix Figure S1).

The “sentiment” distribution across tweets by male 
and female faculty was different, with female faculty 
noted to tweet more “positively” than male faculty 
(48.50% versus 45.08%), although the difference was not 
statistically significant (chi-square, P = 0.4) (Figure 1).

Subspecialty analysis
As prev iously hy pot hesized, urologic oncolog y 
represented the largest overall subspecialty on Twitter, 
with the total number of active members of 225. The 
subspecialty also represented the highest tweets per 
day per user (0.32) and total tweets per day (73.72) and 
the highest total followers (333 951) and likes (143 261). 
Androlog y and pediatric urolog y represented the 
second-highest group among the subspecialties for tweet 

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per user per day (0.29) and similar number of followers 
(66 757 versus 66 425); however, pediatric urology was 
more likely to retweet a post than andrology (1 433 124 
versus 448 264) (Figure 2).

When assessing temporal trends across specialties 
by month and day, urologic oncology represented the 
highest tweet count overall, with peaks in September 
2021 (529), January 2022 (724), and February 2022 (731).  
January and February corresponded to similar trends 
among other specialties likely reflecting AUA match, 
while many specialty meetings corresponded to peaks in 
activity. Overall, Monday represented the lowest activ-
ity day on average, with Twitter activity increasing from 
Thursday and peaking on Friday night (Figure 3).

FIGURE 1. 

General Twitter presence and number of user accounts from 2007 to 2021. Academic Urology Twitter presence is 
shown by number of Twitter accounts by year (A) stratified by male vs female. In addition, total Twitter academic 
urology presence is broken down by specialty and gender (B).  Overall academic Twitter presence based on gender 
can be seen in C, showing overall percentage of Twitter accounts (top) and broken down by proportion of all 
academic urologist (bottom). (D). Sentiment analysis designated as neutral, negative or positive from each users’ 
tweets, with birdgram representative of most commonly utilized words within the tweets corresponding to  
the size of the word

Twitter network interaction analysis among 
subspecialties
We assessed urology subspecialty interaction on Twitter 
by counting the total number of interactions between 
specialties centered around the hashtags: #aua21, 
#auamatch, and #urosome. Although any hashtag can 
be used for the interaction analysis, we chose these 
particular “specialty neutral” hashtags to visualize 
active direct communication. While urologic oncology 
consistently interacted with other subspecialties most 
frequently due to the total number of participants within 
urologic oncology (ie, 39 interactions with FPMRS), 
when assessing global proportional interaction, FPMRS 
represented the most diverse interaction network. 

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As seen in Figure 4, conversations focused within a 
subspecialty were also more likely to be seen within 
urologic oncology (visualized as blind ending blue 
node). While this interaction improved with #urosome 
and #auamatch, the interactions as seen in Figure 4 are 
topic dependent, with more interactions seen between 
andrology and urologic oncology with #urosome than 
the other 2 topics. Overall, pediatric urology and FPMRS 
did highlight more interactive ties compared with other 
subspecialties.

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 

FIGURE 2.

Twitter profiles of urologic subspecialties on Twitter. Hashtag results based on 2006 to present analysis, remainder 
of the reported features reflective of May 2021 to March 2022 data 

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, 

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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 pres-
ence 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 

FIGURE 3. 

Temporal distribution of online Twitter activity by day and month, further categorized by sub-specialty  
within urology

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, endou-
rology, and oncology had the most Twitter represen-
tation. 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 

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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 urol-
ogists 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 

FIGURE 4. 

Interaction analysis based on subspecialty direct interaction with others on three different topics including #aua21, 
#auamatch, and #urosome. Interaction tables represent the number of direct conversations between members of 
each subspecialty focused on the topic at hand 

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 interac-
tion among subspecialties within academic urology on 
Twitter. While previous research has focused mainly on 
trends of utilization and growth, our innovative anal-
ysis fostered by information networks and a novel API 
network, highlights the communication and engage-
ment among the subspecialties. Based on our findings, 
we can establish that virtual interaction on Twitter is 
dependent upon the topic; however, strong intersubspe-
cialty 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 pedi-
atric urology were more likely to engage other subspe-
cialties. It is unclear why this network structure exists, 

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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.

Conclusion
Our research indicated that there is a growing 
representation of women in the field of academic urology 
on the social media platform Twitter. This presence 
offers opportunities for enhanced communication and 
connection within the field. Furthermore, the study 
revealed variations in interactions between various 
subspecia lties within urolog y, with FPMRS and 
pediatric urology found to have a notable higher level 
of intersubspecialty engagement compared with other 
subspecialties.

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SUPPLEMENTARY APPENDIX FIGURE S1.

AUA programs faculty Twitter presence, listing of top 10 programs with percent of total faculty (A), as well as total 
number of registered academic Twitter accounts by city (B). C-D representing overall, male and female specific 
hashtags and mentions from 2006 to present by faculty, with overall sentiment analysis shown in D and most 
commonly utilized words (E)  

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