Social Media and the Practicing Hematologist: Twitter 101 for the Busy Healthcare Provider.
Ontology highlight
ABSTRACT:
SUBMITTER: Thompson MA
PROVIDER: S-EPMC4679678 | biostudies-literature | 2015 Dec
REPOSITORIES: biostudies-literature
Publications
Social Media and the Practicing Hematologist: Twitter 101 for the Busy Healthcare Provider.
Thompson Michael A MA Majhail Navneet S NS Wood William A WA Perales Miguel-Angel MA Chaboissier Mélanie M
Current hematologic malignancy reports 20151201 4
Social media is a relatively new form of media that includes social networks for communication dissemination and interaction. Patients, physicians, and other users are active on social media including the microblogging platform Twitter. Many online resources are available to facilitate joining and adding to online conversations. Social media can be used for professional uses, therefore, we include anecdotes of physicians starting on and implementing social media successfully despite the limits o ...[more]
Similar Datasets
Project description:ObjectivesGiven the public health importance of communicating about mental illness and the growing use of social media to convey information, our goal was to develop an empirical model to identify periods of heightened interest in mental health topics on Twitter.Materials and methodsWe collected data on 176 million tweets from 2011 to 2014 with content related to depression or suicide. Using an autoregressive integrated moving average (ARIMA) data analysis, we identified deviations from predicted trends in communication about depression and suicide.ResultsTwo types of heightened Twitter activity regarding depression or suicide were identified in 2014: expected increases in response to planned behavioral health events, and unexpected increases in response to unanticipated events. Tweet volume following expected increases went back to the predicted level more rapidly than the volume following unexpected events.DiscussionAlthough ARIMA models have been used extensively in other fields, they have not been used widely in public health. Our findings indicate that our ARIMA model is valid for identifying periods of heightened activity on Twitter related to behavioral health. The model offers an objective and empirically based measure to identify periods of greater interest for timing the dissemination of credible information related to mental health.ConclusionSpikes in tweet volume following a behavioral health event often last for less than 2 days. Individuals and organizations that want to disseminate behavioral health messages on Twitter in response to heightened periods of interest need to take this limited time frame into account.
| S-EPMC7651954 | biostudies-literature
Project description:An understanding of healthcare super-utilizers' online behaviors could better identify experiences to inform interventions. In this retrospective case-control study, we analyzed patients' social media posts to better understand their day-to-day behaviors and emotions expressed online. Patients included those receiving care in an urban academic emergency department who consented to share access to their historical Facebook posts and electronic health records. Super-utilizers were defined as patients with more than six visits to the Emergency Department (ED) in a year. We compared posts by super-utilizers with a matched group using propensity scoring based on age, gender and Charlson comorbidity index. Super-utilizers were more likely to post about confusion and negativity (D = .65, 95% CI-[.38, .95]), self-reflection (D = .63 [.35, .91]), avoidance (D = .62 [.34, .90]), swearing (D = .52 [.24, .79]), sleep (D = .60 [.32, .88]), seeking help and attention (D = .61 [.33, .89]), psychosomatic symptoms, (D = .49 [.22, .77]), self-agency (D = .56 [.29, .85]), anger (D = .51, [.24, .79]), stress (D = .46, [.19, .73]), and lonely expressions (D = .44, [.17, .71]). Insights from this study can potentially supplement offline community care services with online social support interventions considering the high engagement of super-utilizers on social media.
| S-EPMC7994843 | biostudies-literature
Project description:Sentiment analysis is the automated coding of emotions expressed in text. Sentiment analysis and other types of analyses focusing on the automatic coding of textual documents are increasingly popular in psychology and computer science. However, the potential of treating automatically coded text collected with regular sampling intervals as a signal is currently overlooked. We use the phrase "text as signal" to refer to the application of signal processing techniques to coded textual documents sampled with regularity. In order to illustrate the potential of treating text as signal, we introduce the reader to a variety of such techniques in a tutorial with two case studies in the realm of social media analysis. First, we apply finite response impulse filtering to emotion-coded tweets posted during the US Election Week of 2020 and discuss the visualization of the resulting variation in the filtered signal. We use changepoint detection to highlight the important changes in the emotional signals. Then we examine data interpolation, analysis of periodicity via the fast Fourier transform (FFT), and FFT filtering to personal value-coded tweets from November 2019 to October 2020 and link the variation in the filtered signal to some of the epoch-defining events occurring during this period. Finally, we use block bootstrapping to estimate the variability/uncertainty in the resulting filtered signals. After working through the tutorial, the readers will understand the basics of signal processing to analyze regularly sampled coded text.
| S-EPMC9311346 | biostudies-literature
Project description:PurposeTo determine the content posted by sports medicine orthopaedic surgeons on Instagram and Twitter and to evaluate the associated engagement.MethodsData were collected from sports medicine orthopaedic surgeons' Instagram profiles and Twitter profiles between December 2020 and June 2022. The median number of total number of likes, comments, and retweets was calculated. The content of posts from both platforms was divided into the following categories: personal life, research, patient testimony, day in the life, pathway to becoming a physician, clinical cases, medical facts, and team coverage.ResultsData from 71 Instagram profiles and 39 Twitter profiles were used in this study. A total of 1,193 posts were identified on Instagram and 1,284 posts were identified on Twitter. The personal life category had the greatest number of posts on Instagram (303, 25.4%), whereas the medical facts category had the greatest number of posts on Twitter (251, 19.5%). Pathway to becoming a physician had the greatest median number of likes on both Instagram (97.5, range 48-2,467) and Twitter (19, range 0-50) and the greatest median number of comments on Instagram at 16 (range 1-203). The team coverage category on Instagram had the greatest percentage of likes per follower at 9.9%. A significantly greater percentage of orthopaedic surgeons posted about day in the life content, medical facts, and research on Twitter in comparison with Instagram. Instagram resulted in significantly more social media engagement than Twitter in all 8 categories (P < .05).ConclusionsInstagram resulted in significantly more social media engagement across all categories in comparison with Twitter with team coverage, personal life, and pathway to becoming a physician being the most popular categories.Clinical relevanceThe information learned in this study may help sports surgeons understand how they may best utilize social media to engage with others and enhance their clinical practice.
| S-EPMC10792685 | biostudies-literature
Project description:We identify influential early adopters in a social network, where individuals are resource constrained, to maximize the spread of multiple, costly behaviors. A solution to this problem is especially important for viral marketing. The problem of maximizing influence in a social network is challenging since it is computationally intractable. We make three contributions. First, we propose a new model of collective behavior that incorporates individual intent, knowledge of neighbors actions and resource constraints. Second, we show that the multiple behavior influence maximization is NP-hard. Furthermore, we show that the problem is submodular, implying the existence of a greedy solution that approximates the optimal solution to within a constant. However, since the greedy algorithm is expensive for large networks, we propose efficient heuristics to identify the influential individuals, including heuristics to assign behaviors to the different early adopters. We test our approach on synthetic and real-world topologies with excellent results. We evaluate the effectiveness under three metrics: unique number of participants, total number of active behaviors and network resource utilization. Our heuristics produce 15-51% increase in expected resource utilization over the naïve approach.
| S-EPMC5053606 | biostudies-literature
Project description:It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using 'social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.
| S-EPMC5609738 | biostudies-literature
Project description:Background: Social media is ubiquitous as a tool for collaboration, networking, and dissemination. However, little is known about use of social media platforms by pulmonary and critical care medicine fellowship programs.Objective: We identify and characterize pulmonary and critical care fellowship programs using Twitter and Instagram, as well as the posting behaviors of their social media accounts.Methods: We identified all adult and pediatric pulmonary, critical care medicine (CCM), and combined pulmonary and critical care medicine (PCCM) programs in the United States using the Electronic Residency Application Service. We searched for Twitter profiles for each program between January 1, 2018, and September 30, 2018. Tweets and Twitter interactions were classified into the following three types: social, clinical, or medical education (MedEd) related. We collected data about content enhancements of tweets, including the use of pictures, graphics interchange format or videos, hashtags, links, and tagging other accounts. The types of tweets, content enhancement characteristics, and measures of engagement were analyzed for association with number of followers.Results: We assessed 341 programs, including 163 PCCM, 36 adult CCM, 20 adult pulmonary, 67 pediatric CCM, and 55 pediatric pulmonary programs. Thirty-three (10%) programs had Twitter accounts. Of 1,903 tweets by 33 of the 341 programs with Twitter accounts, 476 (25%) were MedEd related, 733 (39%) were clinical, and 694 (36%) were social. The median rate of tweets per month was 1.65 (interquartile range [IQR], 0.4-6.65), with 55% programs tweeting more than monthly. Accounts tweeting more often had significantly more followers than those tweeting less frequently (median, 240 followers; 25-75% IQR, 164-388 vs. median, 107 followers; 25-75% IQR, 13-188; P = 0.006). Higher engagement with clinical and social Twitter interactions (tweets, retweets, likes, and comments) was associated with more followers but not for the MedEd-related Twitter interactions. All types of content enhancements (pictures, graphics interchange format/videos, links, and tagging) were associated with a higher number of followers, except for hashtags.Conclusion: Despite the steadily increasing use of social media in medicine, only 10% of the pulmonary and critical care fellowship programs in the United States have Twitter accounts. Social and clinical content appears to gain traction online; however, additional evaluation is needed on how to effectively engage audiences with MedEd content.
| S-EPMC8357066 | biostudies-literature
Project description:BackgroundThe COVID-19 pandemic has drawn attention to various inequalities in global societies, highlighting discrepancies in terms of safety, accessibility, and overall health. In particular, sex workers are disproportionately at risk due to the nature of their work and the social stigma that comes alongside it.ObjectiveThis study examines how public social media can be used as a tool of professional and personal expression by sex workers during the COVID-19 pandemic. We aimed to explore an underresearched topic by focusing on sex workers' experiences with the ongoing COVID-19 pandemic on the social media platform Twitter. In particular, we aimed to find the main issues that sex workers discuss on social media in relation to the COVID-19 pandemic.MethodsA literature review followed by a qualitative analysis of 1458 (re)tweets from 22 sex worker Twitter accounts was used for this study. The tweets were qualitatively coded by theme through the use of intercoder reliability. Empirical, experimental, and observational studies were included in this review to provide context and support for our findings.ResultsIn total, 5 major categories were identified as a result of the content analysis used for this study: concerns (n=542, 37.2%), solicitation (n=336, 23.0%), herd mentality (n=231, 15.8%), humor (n=190, 13.0%), and blame (n=146, 10.0%). The concerns category was the most prominent category, which could be due to its multifaceted nature of including individual concerns, health issues, concerns for essential workers and businesses, as well as concerns about inequalities or intersectionality. When using gender as a control factor, the majority of the results were not noteworthy, save for the blame category, in which sexual and gender minorities (SGMs) were more likely to post content.ConclusionsThough there has been an increase in the literature related to the experiences of sex workers, this paper recommends that future studies could benefit from further examining these 5 major categories through mixed methods research. Examining this phenomenon could recognize the challenges unique to this working community during the COVID-19 pandemic and potentially reduce the widespread stigma associated with sex work in general.
| S-EPMC9285671 | biostudies-literature
Project description:The understanding of the public response to COVID-19 vaccines is the key success factor to control the COVID-19 pandemic. To understand the public response, there is a need to explore public opinion. Traditional surveys are expensive and time-consuming, address limited health topics, and obtain small-scale data. Twitter can provide a great opportunity to understand public opinion regarding COVID-19 vaccines. The current study proposes an approach using computational and human coding methods to collect and analyze a large number of tweets to provide a wider perspective on the COVID-19 vaccine. This study identifies the sentiment of tweets using a machine learning rule-based approach, discovers major topics, explores temporal trend and compares topics of negative and non-negative tweets using statistical tests, and discloses top topics of tweets having negative and non-negative sentiment. Our findings show that the negative sentiment regarding the COVID-19 vaccine had a decreasing trend between November 2020 and February 2021. We found Twitter users have discussed a wide range of topics from vaccination sites to the 2020 U.S. election between November 2020 and February 2021. The findings show that there was a significant difference between tweets having negative and non-negative sentiment regarding the weight of most topics. Our results also indicate that the negative and non-negative tweets had different topic priorities and focuses. This research illustrates that Twitter data can be used to explore public opinion regarding the COVID-19 vaccine.
| S-EPMC8540945 | biostudies-literature
Project description:Social media is increasingly utilized as a resource in healthcare. We sought to identify perceptions of using social media as an educational tool among healthcare practitioners. An electronic survey was distributed to healthcare administrators, nurses, nurse practitioners, pharmacists, physicians, and physician assistants f hospital systems and affiliated health science schools in Georgia, Maryland, South Carolina, and Wisconsin. Survey questions evaluated respondents' use and views of social media for educational purposes and workplace accessibility using a Likert scale (1 = strongly disagree, 5 = strongly agree). Nurses (75%), pharmacists (11%), and administrators (7%) were the most frequent respondents. Facebook® (27%), Pinterest® (17%), and Instagram® (17%) were the most frequently accessed social media platforms. Nearly 85% agreed or strongly agreed that social media can be an effective tool for educational purposes. Among those who had social media platforms, 43.0% use them for educational purposes. Pinterest® (30%), Facebook® (22%), LinkedIn® (16%), and Twitter® (14%) were most frequently used for education. About 50% of respondents had limited or no access to social media at work. Administrators, those with unlimited and limited work access, and respondents aged 20-29 and 30-39 years were more likely to agree that social media is an educational tool (OR: 3.41 (95% CI 1.31 to 8.84), 4.18 (95% CI 2.30 to 7.60), 1.66 (95% CI 1.22 to 2.25), 4.40 (95% CI 2.80 to 6.92), 2.14 (95% CI 1.53 to 3.01) respectively). Residents, physicians, and those with unlimited access were less likely to agree with allowing social media access at work for educational purposes only. Healthcare practitioners frequently utilize social media, and many believe it can be an effective educational tool in healthcare.
| S-EPMC7004337 | biostudies-literature