Gender Differences in X (Formerly Twitter) Use, Influence, and Engagement Among Cardiologists From the Top U.S. News Best Hospitals
Minji Seok, Sungjin Kim, Harper Tzou, Olivia Peony, Mitchell Kamrava, Andriana P Nikolova, Katelyn M Atkins
Abstract
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
| Institution name | State | Total physicians (N=2022), n (%) | Physicians on X (n=753), n (%) |
|---|---|---|---|
| Brigham and Womens | Massachusetts | 143 (7.07) | 73 (9.69) |
| Cedars Sinai | California | 56 (2.77) | 22 (2.92) |
| Cleveland Clinic | Florida and Ohio | 126 (6.23) | 51 (6.77) |
| Johns Hopkins | Maryland | 102 (5.04) | 35 (4.65) |
| Houston Methodist | Texas | 64 (3.17) | 33 (4.38) |
| Lenox Hill at Northwell | New York | 117 (5.79) | 27 (3.59) |
| Massachusetts General | Massachusetts | 100 (4.95) | 57 (7.57) |
| Mayo Clinic Rochester | Minnesota | 156 (7.72) | 68 (9.03) |
| Mount Sinai | Florida, New Jersey, and New York | 201 (9.94) | 64 (8.50) |
| NewYork-Presbyterian Hospital Columbia and Cornell | New York | 54 (2.67) | 15 (1.99) |
| NYU Langone Hospitals | New York | 164 (8.11) | 20 (2.66) |
| Northwell Northshore | New York | 93 (4.60) | 15 (1.99) |
| Northwestern | Illinois | 112 (5.54) | 47 (6.24) |
| Rush University | Illinois | 44 (2.18) | 22 (2.92) |
| Stanford Hospital | California | 88 (4.35) | 45 (5.98) |
| Texas Heart Institute at Baylor | Texas | 14 (0.69) | 5 (0.66) |
| University of California, Los Angeles | California | 76 (3.76) | 29 (3.85) |
| UT Southwestern | Texas | 77 (3.81) | 38 (5.05) |
| University of Pennsylvania | Pennsylvania | 134 (6.63) | 58 (7.70) |
| Vanderbilt | Tennessee | 101 (5.00) | 29 (3.85) |
| Variable | Not on X (n=1269) | On X (n=753) | Men on X (n=513) | Women on X (n=240) | ||
|---|---|---|---|---|---|---|
| Geographic region, n (% | <.001 | .72 | ||||
| Northeast | 741 (58.39) | 364 (48.34) | 245 (47.76) | 119 (49.58) | ||
| Midwest | 249 (19.62) | 187 (24.83) | 130 (25.34) | 57 (23.75) | ||
| South | 155 (12.21) | 106 (14.08) | 69 (13.45) | 37 (15.42) | ||
| West | 124 (9.77) | 96 (12.75) | 69 (13.45) | 27 (11.25) | ||
| Gender, n (% | <.001 | — | ||||
| Men | 1000 (78.8) | 513 (68.13) | — | — | ||
| Women | 269 (21.20) | 240 (31.87) | — | — | ||
| Faculty type, n (% | <.001 | .06 | ||||
| Not explicitly listed | 347 (27.34) | 191 (25.37) | 135 (26.32) | 56 (23.33) | ||
| Instructor/clinician | 97 (7.64) | 39 (5.18) | 21 (4.09) | 18 (7.5) | ||
| Assistant | 441 (34.75) | 227 (30.15) | 149 (29.04) | 78 (32.5) | ||
| Associate | 208 (16.39) | 153 (20.32) | 100 (19.49) | 53 (22.08) | ||
| Professor | 176 (13.87) | 143 (18.99) | 108 (21.05) | 35 (14.58) | ||
| Number of leadership titles, n (% | <.001 | .11 | ||||
| 0 | 840 (66.19) | 360 (47.81) | 239 (46.59) | 121 (50.42) | ||
| 1 | 306 (24.11) | 241 (32.01) | 159 (30.99) | 82 (34.17) | ||
| 2 | 95 (7.49) | 111 (14.74) | 85 (16.57) | 26 (10.83) | ||
| ≥3 | 28 (2.21) | 41 (5.44) | 30 (5.85) | 11 (4.58) | ||
| Subspecialty, n (% | <.001 | <.001 | ||||
| General | 552 (43.53) | 213 (28.29) | 133 (25.93) | 80 (33.33) | ||
| Interventional | 226 (17.82) | 112 (14.87) | 90 (17.54) | 22 (9.17) | ||
| Imaging | 193 (15.22) | 121 (16.07) | 68 (13.26) | 53 (22.08) | ||
| Congenital | 31 (2.44) | 24 (3.19) | 12 (2.34) | 12 (5) | ||
| Heart failure | 91 (7.18) | 121 (16.07) | 78 (15.2) | 43 (17.92) | ||
| Electrophysiology | 138 (10.88) | 95 (12.62) | 84 (16.37) | 11 (4.58) | ||
| Other | 37 (2.92) | 67 (8.9) | 48 (9.36) | 19 (7.92) | ||
| Dual degree, n (% | ||||||
| PhD | <.001 | .34 | ||||
| No | 1183 (93.22) | 662 (87.92) | 447 (87.13) | 215 (89.58) | ||
| Yes | 86 (6.78) | 91 (12.08) | 66 (12.87) | 25 (10.42) | ||
| MS | <.001 | .71 | ||||
| No | 1220 (96.14) | 679 (90.17) | 464 (90.45) | 215 (89.58) | ||
| Yes | 49 (3.86) | 74 (9.83) | 49 (9.55) | 25 (10.42) | ||
| MPH | <.001 | .55 | ||||
| No | 1226 (96.61) | 680 (90.31) | 461 (89.86) | 219 (91.25) | ||
| Yes | 43 (3.39) | 73 (9.69) | 52 (10.14) | 21 (8.75) | ||
| MBA | .24 | .76 | ||||
| No | 1255 (98.9) | 740 (98.27) | 503 (98.05) | 237 (98.75) | ||
| Yes | 14 (1.1) | 13 (1.73) | 10 (1.95) | 3 (1.25) | ||
| Practice duration (years) | <.001 | .14 | ||||
| Median (IQR) | 21 (12‐31) | 11 (6‐21) | 12 (1‐48) | 10 (1‐45) | ||
| Overall: <9; physicians on X: <7, n (% | 152 (37.91) | 249 (62.09) | 117 (68.82) | 53 (31.18) | ||
| Overall: ≥9 and <17; physicians on X: ≥7 and <11, n (% | 270 (60.81) | 174 (39.19) | 153 (63.22) | 89 (36.78) | ||
| Overall: ≥17 and <28; physicians on X: ≥11 and <21, n (% | 292 (67.13) | 143 (32.87) | 123 (69.49) | 54 (30.51) | ||
| Overall: ≥28; physicians on X: ≥21, n (% | 359 (80.86) | 85 (19.14) | 120 (73.17) | 44 (26.83) | ||
| X use variables (publicly available), median (IQR) | ||||||
| Time on X (years) | — | — | — | 7.80 (5.30‐11.34) | 6.39 (5.06‐10.11) | <.001 |
| Average number of followers per year on X | — | — | — | 78.05 (24.96‐197.33) | 71.46 (24.8‐180.84) | .68 |
| Average number of people followed per year on X | — | — | — | 31.90 (11.48‐70.40) | 42.11 (16.8‐84.77) | .02 |
| Average number of tweets per year on X | — | — | — | 28.04 (5.22‐111.15) | 29.10 (5.06‐102.47) | .98 |
| Average number of media posts per year on X | — | — | — | 2.27 (0.26‐10.38) | 2.20 (0.26‐10.78) | .96 |
| Average number of liked posts per year on X | — | — | — | 64.49 (6.94‐318.98) | 112.52 (16.58‐430.1) | .02 |
| Thematic content of X biography, n (% | ||||||
| Job Roles | — | .36 | ||||
| No mention | — | — | 98 (19.10) | 39 (16.32) | ||
| Mention | — | — | 415 (80.90) | 200 (83.68) | ||
| Specialty | — | .48 | ||||
| No mention | — | — | 169 (32.94) | 85 (35.56) | ||
| Mention | — | — | 344 (67.06) | 154 (64.44) | ||
| Parent | — | .006 | ||||
| No mention | — | — | 449 (87.52) | 191 (79.92) | ||
| Mention | — | — | 64 (12.48) | 48 (20.08) | ||
| Spouse | — | .77 | ||||
| No mention | — | — | 467 (91.03) | 216 (90.38) | ||
| Mention | — | — | 46 (8.97) | 23 (9.62) | ||
| Institution | — | .56 | ||||
| No mention | — | — | 148 (28.85) | 64 (26.78) | ||
| Mention | — | — | 365 (71.15) | 175 (73.22) | ||
| Personal interests | — | .89 | ||||
| No mention | — | — | 444 (86.55) | 206 (86.19) | ||
| Mention | — | — | 69 (13.45) | 33 (13.81) | ||
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Taxonomy
TopicsSocial Media in Health Education · Diversity and Career in Medicine · Health Literacy and Information Accessibility
Introduction
Women in medicine face significant barriers to compensation, career advancement, and research support, even when controlling for specialty, age, and/or clinical experience [1]. These barriers are especially pronounced in cardiology, where women comprise only 15% of practicing cardiologists and are less likely to be clinical trial leaders or present late-breaking trials at major cardiovascular conferences [2-4]. Social media platforms, such as X (formerly Twitter), can foster collaboration, mentorship, and promotion of research [56]. However, studies examining X’s impact on existing gender gaps are limited. In this study, we aimed to analyze differences between X users and non–X users and differences in X use by gender among adult cardiologists.
Methods
Ethical Considerations
This cross-sectional study was exempt from ethical approval by the Cedars-Sinai institutional review board due to the use of publicly available data.
Study Design
The top 20 U.S. News Best Hospitals for cardiology, heart surgery, and vascular surgery were identified from the 2023 ranking (Table 1) [7]. Available physician website profiles of fellowship-trained adult medicine cardiologists were manually reviewed by 3 investigators (MS, HT, and OP) for inclusion, and demographic information was collected (eg, academic appointment, apparent gender, and medical school and fellowship graduation years). Physicians were evaluated for the presence of an X account, and public data were manually collected between December 8, 2023, and May 9, 2024. Differences between non–X users and X users and between women and men X users were compared, using Wilcoxon rank-sum tests for continuous variables and chi-square or Fisher exact tests for categorical variables as appropriate.
Results
In total, 2022 cardiology physician profiles were analyzed; 37.61% (n=753) were on X, and 63.39% (n=1269) were not on X. Compared to nonusers, X users had a higher proportion of women (240/753, 31.87% vs 269/1269, 21.20%), higher academic faculty appointments, and a greater number of advanced degrees (all P<.001). Women and men X users had similar total practice durations (counted from fellowship training completion until 2024; median 10, IQR 1-45 y vs median 12, IQR 1-48 y; P=.14), but women’s practice durations since joining X were significantly lower (median 6.4, IQR 5-11 y vs median 7.8, IQR 5-10 y; P<.001). After adjusting for the number of years on X, women and men showed similar numbers of followers (median 71.46, IQR 24.8‐180.84 vs median 78.05, IQR 24.96‐197.33 per year on X; P=.68) and posts (median 29.1, IQR 5.06‐102.47 vs median 28.04, IQR 5.22‐111.15 per year on X; P=.98), but women had higher levels of self-engagement (number of users followed: median 42.11, IQR 16.8‐84.77 vs median 31.9, IQR 11.48‐70.4 per year on X; P=.02; number of liked posts: median 112.52, IQR 16.58‐430.1 vs median 64.49, IQR 6.94‐318.98 per year on X; P=.02; Table 2). Per a thematic analysis of biographical text, women were more likely than men to mention being a parent (48/239, 20.08% vs 64/513, 12.48%; P=.006), but there was no significant difference in mentions of jobs (P=.36) or hobbies (P=.89; Table 2).
Discussion
In our analysis of U.S. News Best Hospitals cardiologists, the proportion of women on X was higher than the proportion of women non–X users. One possible explanation for this is that women cardiologists may be seeking novel opportunities for networking, collaboration, visibility, and/or self-promotion that are not available through traditional channels [5]. Additionally, compared to men, women cardiologists had similar time-adjusted follower counts but liked more posts. This is consistent with content language analyses demonstrating higher expected levels of friendliness in women’s professional communications, including more frequent use of exclamation points as markers of friendly interaction, which is associated with increased emotional labor [89]. Further, women cardiologists were more likely to mention being a parent, suggesting that women may be more comfortable with highlighting work-life integration factors. This is unsurprising, as women physicians have joined social media groups discussing issues such as parenting, maternity leave, and women leadership in medicine [5]. These observations support efforts to better understand motivational differences in social media use and impacts on potential downstream professional benefits.
Our study has several limitations, including institutional websites being subject to inaccuracy and incompleteness, currently available X data being more limited compared to prior studies, limited physician practice type information, and potential misgendering [10]. However, our findings highlight the increased presence of women cardiologists on X, with similar influence to men and higher engagement despite shorter time on X. These findings suggest an inherent desire to engage on social media for professional use, though the motivating factors driving these behavioral differences and their impact on existing gender disparities warrant further study.
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