Women in Cardiology Twitter Network: An Analysis of a Global Professional Virtual Community From 2016 to 2019.
ABSTRACT: Background Social media is an effective channel for the advancement of women physicians; however, its use by women in cardiology has not been systematically studied. Our study seeks to characterize the current Women in Cardiology Twitter network. Methods and Results Six women-specific cardiology Twitter hashtags were analyzed: #ACCWIC (American College of Cardiology Women in Cardiology), #AHAWIC (American Heart Association Women in Cardiology), #ilooklikeacardiologist, #SCAIWIN (Society for Cardiovascular Angiography and Interventions Women in Innovations), #WomeninCardiology, and #WomeninEP (Women in Electrophysiology). Twitter data from 2016 to 2019 were obtained from Symplur Signals. Quantitative and descriptive content analyses were performed. The Women in Cardiology Twitter network generated 48 236 tweets, 266 180 903 impressions, and 12 485 users. Tweets increased by 706% (from 2083 to 16 780), impressions by 207% (from 26 755 476 to 82 080 472), and users by 440% (from 796 to 4300), including a 471% user increase internationally. The network generated 6530 (13%) original tweets and 43 103 (86%) amplification tweets. Most original and amplification tweets were authored by women (81% and 62%, respectively) and women physicians (76% and 52%, respectively), with an increase in original and amplification tweets authored by academic women physicians (98% and 109%, respectively) and trainees (390% and 249%, respectively) over time. Community building, professional development, and gender advocacy were the most common tweet contents over the study period. Community building was the most common tweet category for #ACCWIC, #AHAWIC, #ilooklikeacardiologist, #SCAIWIN, and #WomeninCardiology, whereas professional development was most common for #WomeninEP. Conclusions The Women in Cardiology Twitter network has grown immensely from 2016 to 2019, with women physicians as the driving contributors. This network has become an important channel for community building, professional development, and gender advocacy discussions in an effort to advance women in cardiology.
Project description:BACKGROUND:Social media has changed the way surgeons communicate worldwide, particularly in dissemination of trial results. However, it is unclear if social media could be used in recruitment to surgical trials. This study aimed to investigate the influence of Twitter in promoting surgical recruitment in The Emergency Laparotomy and Frailty (ELF) Study. METHODS:The ELF Study was a UK-based, prospective, observational cohort that aimed to assess the influence of frailty on 90-day mortality in older adults undergoing emergency surgery. A power calculation required 500 patients to be recruited to detect a 10% change in mortality associated with frailty. A 12-week recruitment period was selected, calculated from information submitted by participating hospitals and the numbers of emergency surgeries performed in adults aged >?65?years. A Twitter handle was designed (@ELFStudy) with eye-catching logos to encourage enrolment and inform the public and clinicians involved in the study. Twitter Analytics and Twitonomy (Digonomy Pty Ltd) were used to analyse user engagement in relation to patient recruitment. RESULTS:After 90?days of data collection, 49 sites from Scotland, England and Wales recruited 952 consecutive patients undergoing emergency laparotomy, with data logged into a database created on REDCap. Target recruitment (n?=?500) was achieved by week 11. A total of 591 tweets were published by @ELFStudy since its conception, making 218,136 impressions at time of writing. The number of impressions (number of times users see a particular tweet) prior to March 20th 2017 (study commencement date) was 23,335 (343.2 per tweet), compared to the recruitment period with 114,314 impressions (256.3 per tweet), ending June 20th 2017. Each additional tweet was associated with an increase in recruitment of 1.66 (95%CI 1.36 to 1.97; p?<?0.001). CONCLUSION:The ELF Study over-recruited by nearly 100%, reaching over 200,000 people across the U.K. Branding enhanced tweet aesthetics and helped increase tweet engagement to stimulate discussion and healthy competition amongst clinicians to aid trial recruitment. Other studies may draw from the social media experiences of the ELF Study to optimise collaboration amongst researchers. TRIAL REGISTRATION:This study is registered online at www.clinicaltrials.gov (registration number NCT02952430 ) and has been approved by the National Health Service Research Ethics Committee.
Project description:<h4>Objectives</h4>Social media use is associated with developing communities of practice that promote the rapid exchange of information across traditional institutional and geographical boundaries faster than previously possible. We aimed to describe and share our experience using #neoEBM (Neonatal Evidence Based Medicine) hashtag to organise and build a digital community of neonatal care practice.<h4>Materials and methods</h4>Analysis of #neoEBM Twitter data in the Symplur Signals database between 1 May 2018 to 9 January 2021. Data on tweets containing the #neoEBM hashtag were analysed using online analytical tools, including the total number of tweets and user engagement.<h4>Results</h4>Since its registration, a total of 3 228 distinct individual Twitter users used the hashtag with 23 939 tweets and 37 259 710 impressions generated. The two days with the greatest number of tweets containing #neoEBM were 8 May 2018 (n = 218) and 28 April 2019 (n = 340), coinciding with the annual Pediatric Academic Societies meeting. The majority of Twitter users made one tweet using #neoEBM (n = 1078), followed by two tweets (n = 411) and more than 10 tweets (n = 347). The number of individual impressions (views) of tweets containing #neoEBM was 37 259 710. Of the 23 939 tweets using #neoEBM, 17 817 (74%) were retweeted (shared), 15 643 (65%) included at least one link and 1 196 (5%) had at least one reply. As #neoEBM users increased over time, so did tweets containing #neoEBM, with each additional user of the hashtag associated with a mean increase in 7.8 (95% CI 7.7-8.0) tweets containing #neoEBM.<h4>Conclusion</h4>Our findings support the observation that the #neoEBM community possesses many of the characteristics of a community of practice, and it may be an effective tool to disseminate research findings. By sharing our experiences, we hope to encourage others to engage with or build online digital communities of practice to share knowledge and build collaborative networks across disciplines, institutions and countries.
Project description:With the success of hepatitis C virus (HCV) direct-acting antiviral therapies, there has been a shift in research focus to the other major chronic liver diseases (CLDs). The use of social media, specifically Twitter, has become a popular platform for understanding public health trends and for performing health care research. To evaluate this, we studied the areas of public interest and social media trends of the following three major CLDs: hepatitis B virus (HBV), HCV, and nonalcoholic fatty liver disease (NAFLD)/nonalcoholic steatohepatitis (NASH). Twitter activity data from January 1, 2013, through January 1, 2019, for HBV, HCV, and NAFLD/NASH were collected using the social media analytic tool Symplur Signals (Symplur LLC) software. Content and regression analyses were performed to understand and predict Twitter activity for each of the CLDs. Over the study period, there were 810,980 tweets generating 4,452,939,516 impressions. HCV tweet activity peaked in 2015 at 243,261 tweets, followed by a decline of 52.4% from 2015 to 2016 with a subsequent plateau through 2018. Meanwhile, NAFLD/NASH and HBV tweet activity has continued to increase, with projections that these two CLDs will surpass HCV by the second half of 2023 and 2024, respectively. Treatment and Management was the most popular content category for HCV and NAFLD/NASH, while Prevention was the most popular content category for HBV. Conclusion: Twitter is a useful social media tool to gauge public interest in liver disease over time. The information provided by Twitter can be used to identify gaps in public knowledge or highlight areas of interest that may need further research. Future studies on the use of Twitter in liver disease are warranted.
Project description:To investigate factors associated with engagement of U.S. Federal Health Agencies via Twitter. Our specific goals are to study factors related to a) numbers of retweets, b) time between the agency tweet and first retweet and c) time between the agency tweet and last retweet.We collect 164,104 tweets from 25 Federal Health Agencies and their 130 accounts. We use negative binomial hurdle regression models and Cox proportional hazards models to explore the influence of 26 factors on agency engagement. Account features include network centrality, tweet count, numbers of friends, followers, and favorites. Tweet features include age, the use of hashtags, user-mentions, URLs, sentiment measured using Sentistrength, and tweet content represented by fifteen semantic groups.A third of the tweets (53,556) had zero retweets. Less than 1% (613) had more than 100 retweets (mean ?=?284). The hurdle analysis shows that hashtags, URLs and user-mentions are positively associated with retweets; sentiment has no association with retweets; and tweet count has a negative association with retweets. Almost all semantic groups, except for geographic areas, occupations and organizations, are positively associated with retweeting. The survival analyses indicate that engagement is positively associated with tweet age and the follower count.Some of the factors associated with higher levels of Twitter engagement cannot be changed by the agencies, but others can be modified (e.g., use of hashtags, URLs). Our findings provide the background for future controlled experiments to increase public health engagement via Twitter.
Project description:<b>Background:</b> Many academic institutions and journals disseminate research through social media to increase accessibility and reach a wider audience. "Visual Abstracts" are well-suited for social media dissemination, and have been adopted by some as a novel approach to increase engagement with academic content. Visual abstracts are a visual representation of key methods and findings from a traditional peer-reviewed publication. This study expands on previous research by examining the impact of visual abstracts compared to traditional text abstracts to disseminate research produced in a national research center focused on preventing Veteran suicide. <b>Methods:</b> A prospective, randomized crossover design was utilized to compare Twitter posts with a visual abstract to those with a simple screen grab of the PubMed abstract (<i>n</i> = 50 journal publications). Outcomes were measured using native Twitter Analytics to track impressions, retweets, total engagements, and link clicks about 28 days post-tweet, and Altmetric It to track additional alternative metric outcomes. <b>Results:</b> Visual abstract tweets were associated with a significantly higher number of impressions (<i>p</i> < 0.001), retweets (<i>p</i> < 0.001), and link clicks (<i>p</i> = 0.02) compared with text abstract tweets. <b>Conclusions:</b> In line with results from prior studies, we found that visual abstracts resulted in significantly greater research dissemination and social media engagement via retweets and link clicks compared with text tweets. These findings provide further evidence that visual abstracts increase awareness and readership of journal publications, and that Twitter is an effective platform for research dissemination beyond the traditional academic researcher audience. Implications highlight the importance of social media for suicide prevention advocates, Veteran health researchers and other stakeholders to communicate research findings.
Project description:<h4>Objective</h4>To examine public and media response to the draft (October 2011) and finalised (May 2012) recommendations of the United States Preventive Services Task Force (USPSTF) against prostate-specific antigen (PSA) testing via Twitter, a popular social network with over 200 million active users.<h4>Materials and methods</h4>We used a mixed-methods design to analyse posts on Twitter, known as 'tweets'. Using the search term 'prostate cancer', we archived tweets in the 24-h periods following the release of both the draft and the finalised USPSTF recommendations. We recorded tweet rate per h and developed a coding system to assess the type of user and sentiment expressed in tweets and linked articles.<h4>Results</h4>After the draft and finalised USPSTF recommendations were released, 2042 and 5357 tweets focused on the USPSTF report, respectively. The tweet rate nearly doubled within 2 h of both announcements. Fewer than 10% of tweets expressed an opinion about screening, and the majority of these were pro-screening during both periods. By contrast, anti-screening articles were tweeted more frequently in both the draft and finalised study periods. Between the draft and the finalised recommendations, the proportion of anti-screening tweets and anti-screening article links increased (P = 0.03 and P < 0.01, respectively).<h4>Conclusions</h4>There was increased Twitter activity surrounding the USPSTF draft and finalised recommendations. The percentage of anti-screening tweets and articles appeared to increase, perhaps due to the interval public comment period. Despite this, most tweets did not express an opinion, suggesting a missed opportunity in this important arena for advocacy.
Project description:This study examines temporal trends, geographic distribution, and demographic correlates of anti-vaccine beliefs on Twitter, 2009-2015. A total of 549,972 tweets were downloaded and coded for the presence of anti-vaccine beliefs through a machine learning algorithm. Tweets with self-disclosed geographic information were resolved and United States Census data were collected for corresponding areas at the micropolitan/metropolitan level. Trends in number of anti-vaccine tweets were examined at the national and state levels over time. A least absolute shrinkage and selection operator regression model was used to determine census variables that were correlated with anti-vaccination tweet volume. Fifty percent of our sample of 549,972 tweets collected between 2009 and 2015 contained anti-vaccine beliefs. Anti-vaccine tweet volume increased after vaccine-related news coverage. California, Connecticut, Massachusetts, New York, and Pennsylvania had anti-vaccination tweet volume that deviated from the national average. Demographic characteristics explained 67% of variance in geographic clustering of anti-vaccine tweets, which were associated with a larger population and higher concentrations of women who recently gave birth, households with high income levels, men aged 40 to 44, and men with minimal college education. Monitoring anti-vaccination beliefs on Twitter can uncover vaccine-related concerns and misconceptions, serve as an indicator of shifts in public opinion, and equip pediatricians to refute anti-vaccine arguments. Real-time interventions are needed to counter anti-vaccination beliefs online. Identifying clusters of anti-vaccination beliefs can help public health professionals disseminate targeted/tailored interventions to geographic locations and demographic sectors of the population.
Project description:<h4>Background</h4>While researchers have studied negative professional consequences of medical trainee social media use, little is known about how medical students informally use social media for education and career development. This knowledge may help future and current physicians succeed in the digital age.<h4>Objective</h4>We aimed to explore how and why medical students use Twitter for professional development.<h4>Design</h4>This was a digital ethnography.<h4>Participants</h4>Medical student "superusers" of Twitter participated in the study<h4>Approach</h4>The postings ("tweets") of 31 medical student superusers were observed for 8 months (May-December 2013), and structured field notes recorded. Through purposive sampling, individual key informant interviews were conducted to explore Twitter use and values until thematic saturation was reached (ten students). Three faculty key informant interviews were also conducted. Ego network and subnetwork analysis of student key informants was performed. Qualitative analysis included inductive coding of field notes and interviews, triangulation of data, and analytic memos in an iterative process.<h4>Key results</h4>Twitter served as a professional tool that supplemented the traditional medical school experience. Superusers approached their use of Twitter with purpose and were mindful of online professionalism as well as of being good Twitter citizens. Their tweets reflected a mix of personal and professional content. Student key informants had a high number of followers. The subnetwork of key informants was well-connected, showing evidence of a social network versus information network. Twitter provided value in two major domains: access and voice. Students gained access to information, to experts, to a variety of perspectives including patient and public perspectives, and to communities of support. They also gained a platform for advocacy, control of their digital footprint, and a sense of equalization within the medical hierarchy.<h4>Conclusions</h4>Twitter can serve as a professional tool that supplements traditional education. Students' practices and guiding principles can serve as best practices for other students as well as faculty.
Project description:Enthusiasm for using Twitter as a source of data in the social sciences extends to measuring the impact of research with Twitter data being a key component in the new altmetrics approach. In this paper, we examine tweets containing links to research articles in the field of dentistry to assess the extent to which tweeting about scientific papers signifies engagement with, attention to, or consumption of scientific literature. The main goal is to better comprehend the role Twitter plays in scholarly communication and the potential value of tweet counts as traces of broader engagement with scientific literature. In particular, the pattern of tweeting to the top ten most tweeted scientific dental articles and of tweeting by accounts is examined. The ideal that tweeting about scholarly articles represents curating and informing about state-of-the-art appears not to be realized in practice. We see much presumably human tweeting almost entirely mechanical and devoid of original thought, no evidence of conversation, tweets generated by monomania, duplicate tweeting from many accounts under centralized professional management and tweets generated by bots. Some accounts exemplify the ideal, but they represent less than 10% of tweets. Therefore, any conclusions drawn from twitter data is swamped by the mechanical nature of the bulk of tweeting behavior. In light of these results, we discuss the compatibility of Twitter with the research enterprise as well as some of the financial incentives behind these patterns.
Project description:BACKGROUND:Social media use continues to gain momentum in academic neurosurgery. To increase journal impact and broaden engagement, many scholarly publications have turned to social media to disseminate research. The Journal of Neurosurgery Publishing Group (JNSPG) established a dedicated, specialized social media team (SMT) in November 2016 to provide targeted improvement in digital outreach. OBJECTIVE:The goal of this study was to examine the impact of the JNSPG SMT as measured by increased engagement. METHODS:We analyzed various metrics, including impressions, engagements, retweets, likes, profile clicks, and URL clicks, from consecutive social media posts from the JNSPG's Twitter and Facebook platforms between February 1, 2015 and February 28, 2019. Standard descriptive statistics were utilized. RESULTS:Between February 2015 and October 2016, when a specialized SMT was created, 170 tweets (8.1 tweets/month) were posted compared to 3220 tweets (115.0 tweets/month) between November 2016 and February 2019. All metrics significantly increased, including the impressions per tweet (mean 1646.3, SD 934.9 vs mean 4605.6, SD 65,546.5; P=.01), engagements per tweet (mean 35.2, SD 40.6 vs mean 198.2, SD 1037.2; P<.001), retweets (mean 2.5, SD 2.8 vs mean 10.5, SD 15.3; P<.001), likes (mean 2.5, SD 4.0 vs mean 18.0, SD 37.9; P<.001), profile clicks (mean 1.5, SD 2.0 vs mean 5.2, SD 43.3; P<.001), and URL clicks (mean 13.1, SD 14.9 vs mean 38.3, SD 67.9; P<.001). Tweets that were posted on the weekend compared to weekdays had significantly more retweets (mean 9.2, SD 9.8 vs mean 13.4, SD 25.6; P<.001), likes (mean 15.3, SD 17.9 vs mean 23.7, SD 70.4; P=.001), and URL clicks (mean 33.4, SD 40.5 vs mean 49.5, SD 117.3; P<.001). Between November 2015 and October 2016, 49 Facebook posts (2.3 posts/month) were sent compared to 2282 posts (81.5 posts/month) sent between November 2016 and February 2019. All Facebook metrics significantly increased, including impressions (mean 5475.9, SD 5483.0 vs mean 8506.1, SD 13,113.9; P<.001), engagements (mean 119.3, SD 194.8 vs mean 283.8, SD 733.8; P<.001), and reach (mean 2266.6, SD 2388.3 vs mean 5344.1, SD 8399.2; P<.001). Weekend Facebook posts had significantly more impressions per post (mean 7967.9, SD 9901.0 vs mean 9737.8, SD 19,013.4; P=.03) and a higher total reach (mean 4975.8, SD 6309.8 vs mean 6108.2, SD 12,219.7; P=.03) than weekday posts. CONCLUSIONS:Social media has been established as a crucial tool for the propagation of neurosurgical research and education. Implementation of the JNSPG specialized SMT had a demonstrable impact on increasing the online visibility of social media content.