Mapping gender transition sentiment patterns via social media data: toward decreasing transgender mental health disparities

Characterizing Gender-Affirming Voice Intervention Discussion on Social Media: A Cross-Platform Analysis

OBJECTIVES

Gender-affirming voice treatments, such as voice training and surgery, are highly impactful for transgender patients experiencing vocal dysphoria and may be discussed on social media platforms including Twitter and Reddit. Our goal was to characterize the content and sentiment of social media posts pertaining to gender-affirming voice interventions to better understand the needs of this patient population.

STUDY DESIGN

Retrospective data-mining study.

METHODS

A total of 18,695 Tweets from 2001 to 2021 and 23,742 r/Transvoice Reddit submissions and comments from 2009 to 2020 were extracted via publicly available application programming interfaces and analyzed using language processing and sentiment analysis techniques. One thousand eighty-six highly emotive r/Transvoice posts related to voice modification treatments were manually reviewed for further classification.

RESULTS

Online discussion of gender-affirming voice has increased over time and is centered on vocal feminization. Recurrent themes included use of online training resources, singing voice, and barriers to care such as cost and variable experiences with health care providers. Sentiment analysis demonstrated that posts discussing gender-affirming voice training had higher average sentiment scores than those discussing voice surgery, on both Twitter (0.252 vs 0.161; P < 0.001) and Reddit (0.349 vs 0.301; P < 0.001). Frequently appearing themes in highly negative surgery posts included mixed outcomes (9.3%), surgical complications (9.3%), and recovery time (8.5%). Common themes shared by the positive subgroup analysis included peer support, vocal quality, and importance of practice.

CONCLUSIONS

Gender-diverse patients share various concerns and resources relating to voice intervention in the online communities of Twitter and Reddit. The discussion has been growing over the past decade and is mostly positive, with significant social support and resource-sharing within the community. Aggregated online sentiment toward gender-affirming voice surgery is more negative than voice training, largely due to concerns about surgical outcomes and variability, risks, and recovery period.

Increase the Number of Views and Shares of COVID-19 Videos: Content Relevance and Emotional Consistency with Virus Variant Topics

(1) Background: The coronavirus variants have posed serious challenges for the prevention and control of the COVID-19 pandemic. Individuals selectively watch and forward videos that help them reduce the damage caused by the virus. Therefore, the factors influencing video viewing and sharing in the context of the COVID-19 pandemic caused by virus variation must be explored. (2) Method: Based on a combination of uncertainty reduction theory and functional emotion theory, this paper designed hypotheses regarding how content relevance and emotional consistency affect video views and shares. We used the support vector machine (SVM) classification algorithm to measure the content relevance between videos and virus variant topics. We performed sentiment analysis of video text to evaluate the emotional consistency between videos and virus variant topics. Then, we used empirical analysis to build the model. (3) Results: The trained SVM classifier was effective in judging whether the video text was related to virus variant topics (F = 88.95%). The content relevance between COVID-19 videos and virus variant topics was generally low. The results showed that the higher the content relevance, the more views (IRR = 1.005, p = 0.017) and shares (IRR = 1.008, p = 0.009) the video received. Individuals were more willing to view (IRR = 1.625, p < 0.001) and share (IRR = 1.761, p < 0.001) COVID-19 videos with high emotional consistency with virus variant topics. (4) Conclusions: The results of empirical analysis showed that content relevance and emotional consistency between videos and virus variant topics significantly positively impacted video views and shares. The trained SVM classifier can support public health departments in monitoring and assessing the COVID-19 pandemic. Our study provides management advice while helping individuals reduce harm and inform next-step decisions.

Emotional and cognitive changes surrounding online depression identity claims

As social media has proliferated, a key aspect to making meaningful connections with people online has been revealing important parts of one's identity. In this work, we study changes that occur in people's language use after they share a specific piece of their identity: a depression diagnosis. To do so, we collect data from over five thousand users who have made such a statement, which we refer to as an identity claim. Prior to making a depression identity claim, the Reddit user's language displays evidence of increasingly higher rates of anxiety, sadness, and cognitive processing language compared to matched controls. After the identity claim, these language markers decrease and more closely match the controls. Similarly, first person singular pronoun usage decreases following the identity claim, which was previously previously found to be indicative of self-focus and associated with depression. By further considering how and to whom people express their identity, we find that the observed longitudinal changes are larger for those who do so in ways that are more correlated with seeking help (sharing in a post instead of a comment; sharing in a mental health support forum). This work suggests that there may be benefits to sharing one's depression diagnosis, especially in a semi-anonymous forum where others are likely to be empathetic.

Maybe they had a bad day: how LGBTQ and BIPOC patients react to bias in healthcare and struggle to speak out

OBJECTIVE

People who experience marginalization, including Black, Indigenous, People of Color (BIPOC) and Lesbian, Gay, Bisexual, Transgender, Queer, Plus (ie, all other marginalized genders and sexual orientations) people (LGBTQ+) experience discrimination during healthcare interactions, which negatively impacts patient-provider communication and care. Yet, scarce research examines the lived experience of unfair treatment among patients from marginalized groups to guide patient-centered tools that improve healthcare equity.

MATERIALS AND METHODS

We interviewed 25 BIPOC and/or LGBTQ+ people about their experiences of unfair treatment and discrimination when visiting healthcare providers. Through thematic analysis, we describe participants' immediate reactions and longer-term consequences of those experiences.

RESULTS

We identified 4 ways that participants reacted to discrimination in the moment: Fighting, Fleeing, Excusing, and Working Around Bias. Long-term consequences reflect 6 ways they coped: Delaying or Avoiding Care, Changing Healthcare Providers, Self-prescribing, Covering Behaviors, Experiencing Health Complications, and Mistrusting Healthcare Institutions.

DISCUSSION

By describing how patients react to experiences of unfair treatment and discrimination, our findings enhance the understanding of health disparities as patients cope and struggle to speak out.To combat these problems, we identify 3 future directions for informatics interventions that improve provider behavior, support patient advocacy, and address power dynamics in healthcare.

CONCLUSIONS

BIPOC and LGBTQ+ patients' perspectives on navigating unfair treatment and discrimination in healthcare offers critical insight into their experiences and long-term consequences of those experiences. Understanding the circumstances and consequences of unfair treatment, discrimination, and the impact of bias through this patient-centered lens is crucial to inform informatics technologies that promote health equity.

Factors that drive mental health disparities and promote well-being in transgender and nonbinary people

Transgender (or trans) is an umbrella term for individuals who experience their gender differently from what is associated with the sex they were assigned at birth. Individuals who are nonbinary experience their gender outside binary conceptions of gender. Although research has predominantly focused on negative mental health outcomes for transgender and non-binary (TNB) individuals, an emergent literature has begun to explore protective and health-promoting factors and dimensions of psychological well-being. In this Review, we discuss the sociocultural context for TNB people, beginning with a brief history of TNB identity before highlighting frameworks for understanding the higher prevalence of certain mental health concerns among TNB individuals. Next, we summarize the predictive factors associated with higher rates of depression, anxiety, suicidality, trauma-related concerns, disordered eating behaviours and substance use. We also review TNB coping, resilience and resistance to anti-trans stigma, as well as psychological well-being, flourishing and gender euphoria. Tying these topics together, we discuss affirming interventions for reducing mental health disparities and promoting psychological health in TNB individuals and communities. We conclude by discussing future research directions.

Individuals who are transgender or nonbinary experience a higher prevalence of certain mental health concerns, including depression, anxiety and disordered eating behaviours. In this Review, Tebbe and Budge discuss these disparities along with factors that protect these individuals from negative outcomes and promote well-being.

Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes

Disease-specific online health communities provide a convenient and common platform for patients to share experiences, change information, provide and receive social support. This study aimed to compare differences between online psychological and physiological disease communities in topics, sentiment, participation, and emotional contagion patterns using multiple methods as well as to discuss how to satisfy the users' different informational and emotional needs. We chose the online depression and diabetes communities on the Baidu Tieba platform as the data source. Topic modeling and theme coding were employed to analyze discussion preferences for various topic categories. Sentiment analysis was used to identify the sentiment polarity of each post and comment. The social network was used to represent the users' interaction and emotional flows to discover the differences in participation and emotional contagion patterns between psychological and physiological disease communities. The results revealed that people affected by depression focused more on their symptoms and social relationships, while people affected by diabetes were more likely to discuss treatment and self-management behavior. In the depression community, there were obvious interveners spreading positive emotions and more core users in the negative emotional contagion network. In the diabetes community, emotional contagion was less prevalent and core users in positive and negative emotional contagion networks were basically the same. The study reveals insights into the differences between online psychological and physiological disease communities, providing a greater understanding of the users' informational and emotional needs expressed online. These results are helpful for society to provide actual medical assistance and deploy health interventions based on disease types.

I Let Depression and Anxiety Drown Me: Identifying Factors Associated with Resilience Based on Journaling using Machine Learning and Thematic Analysis

Over the years, there has been a global increase in the use of technology to deliver interventions for health and wellness, such as improving peoples mental health and resilience. An example of such technology is the Q-Life app which aims to improve peoples resilience to stress and adverse life events through various coping mechanisms, including journaling. Using a combination of sentiment and thematic analysis, this paper presents the results of analyzing 6023 journal entries from 755 users. We uncover both positive and negative factors that are associated with resilience. First, we apply two lexicon-based and eight machine learning (ML) techniques to classify journal entries into positive or negative sentiment polarity, and then compare the performance of these classifiers to determine the best performing classifier overall. Our results show that Support Vector Machine (SVM) is the best classifier overall, outperforming other ML classifiers and lexicon-based classifiers with a high F1-score of 89.7%. Second, we conduct thematic analysis of negative and positive journal entries to identify themes representing factors associated with resilience either negatively or positively, and to determine various coping mechanisms. Our findings reveal 14 negative themes such as stress, worry, loneliness, lack of motivation, sickness, relationship issues, as well as depression and anxiety. Also, 13 positive themes emerged including self-efficacy, gratitude, socialization, progression, relaxation, and physical activity. Seven (7) coping mechanisms are also identified including time management, quality sleep, and mindfulness. Finally, we reflect on our findings and suggest technological interventions that address the negative factors to promote resilience.

Network Structure and Community Evolution Online: Behavioral and Emotional Changes in Response to COVID-19

Background: The measurement and identification of changes in the social structure in response to an exceptional event like COVID-19 can facilitate a more informed public response to the pandemic and provide fundamental insights on how collective social processes respond to extreme events. Objective: In this study, we built a generalized framework for applying social media data to understand public behavioral and emotional changes in response to COVID-19. Methods: Utilizing a complete dataset of Sina Weibo posts published by users in Wuhan from December 2019 to March 2020, we constructed a time-varying social network of 3.5 million users. In combination with community detection, text analysis, and sentiment analysis, we comprehensively analyzed the evolution of the social network structure, as well as the behavioral and emotional changes across four main stages of Wuhan's experience with the epidemic. Results: The empirical results indicate that almost all network indicators related to the network's size and the frequency of social interactions increased during the outbreak. The number of unique recipients, average degree, and transitivity increased by 24, 23, and 19% during the severe stage than before the outbreak, respectively. Additionally, the similarity of topics discussed on Weibo increased during the local peak of the epidemic. Most people began discussing the epidemic instead of the more varied cultural topics that dominated early conversations. The number of communities focused on COVID-19 increased by nearly 40 percent of the total number of communities. Finally, we find a statistically significant "rebound effect" by exploring the emotional content of the users' posts through paired sample t-test (P = 0.003). Conclusions: Following the evolution of the network and community structure can explain how collective social processes changed during the pandemic. These results can provide data-driven insights into the development of public attention during extreme events.

Research on the Influencing Factors of Audience Popularity Level of COVID-19 Videos during the COVID-19 Pandemic

(1) Background: During the COVID-19 pandemic, users share and obtain COVID-19 information through video platforms, but only a few COVID-19 videos become popular among most audiences. Therefore, it is a very interesting and important research question to explore the influencing factors of the popularity of COVID-19 videos during the COVID-19 pandemic; (2) Our research collects video data related to the keyword “COVID-19” on video platform, the data are analyzed by content analysis and empirical analysis. We then constructed a theoretical model based on the information adoption model; (3) A total of 251 videos were divided into three categories. The least common category was the data and analysis category (11.2%), followed by the prevention and control status category (13.5%); the knowledge and general science category was the most common (75.3%). From the perspective of video quality, the information sources of most videos are relatively reliable, and the content of medical information is low. The research results showed that short video lengths, longer descriptions, more reliable video sources and lower medical information content were more popular with audiences. Audiences are more likely to be attracted to videos in the prevention and control status category and knowledge and general science category. Videos uploaded by uploaders who have a higher influence are more popular with audiences; (4) Conclusions: During the COVID-19 pandemic, information quality (video length, description length, video content type, and medical information and content index) and source credibility (information source reliability, influence and certification type) all significantly influence the popularity level of COVID-19 videos. Our research conclusions can provide management suggestions for the platform, make videos released by uploaders more popular with audiences, and help audiences better understand COVID-19 information and make prevention and control efforts.

Why Do Users of Online Mental Health Communities Get Likes and Reposts: A Combination of Text Mining and Empirical Analysis

An online community is one of the important ways for people with mental disorders to receive assistance and obtain support. This study aims to help users with mental disorders to obtain more support and communication through online communities, and to provide community managers with the possible influence mechanisms based on the information adoption model. We obtained a total of 49,047 posts of an online mental health communities in China, over a 40-day period. Then we used a combination of text mining and empirical analysis. Topic and sentiment analysis were used to derive the key variables—the topic of posts that the users care about most, and the emotion scores contained in posts. We then constructed a theoretical model based on the information adoption model. As core independent variables of information quality, on online mental health communities, the topic of social experience in posts (0.368 ***), the topic of emotional expression (0.353 ***), and the sentiment contained in the text (0.002 *) all had significant positive relationships with the number of likes and reposts. This study found that the users of online mental health communities are more attentive to the topics of social experience and emotional expressions, while they also care about the non-linguistic information. This study highlights the importance of helping community users to post on community-related topics, and gives administrators possible ways to help users gain the communication and support they need.

Developing a standardized protocol for computational sentiment analysis research using health-related social media data

OBJECTIVE

Sentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies.

MATERIALS AND METHODS

We developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019.

RESULTS

Of 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted.

CONCLUSIONS

A substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.

Associations Between Psychosocial Measures and Digital Media Use Among Transgender Youth: Cross-sectional Study

BACKGROUND

Transgender, nonbinary, and gender-diverse (TNG) youth encounter barriers to psychosocial wellness and also describe exploring identities and communities on the web. Studies of cisgender youth connect increased digital technology use with lower well-being, parent relationships, and body image scores as well as increased loneliness and fear of missing out (FOMO). However, little is known about the psychosocial factors associated with digital technology use among TNG compared with cisgender youth.

OBJECTIVE

This study aims to examine the associations between psychosocial measures and digital technology use and its importance for cisgender and TNG youth.

METHODS

We surveyed a nationally representative sample of adolescents (aged 13-18 years) about psychosocial wellness and digital technology use. Psychosocial measures included assessment of well-being, parental relationships, body image, loneliness, and FOMO. Digital media use assessments included the short Problematic and Risky Internet Use Screening Scale-3 and the Adolescent Digital Technology Interactions and Importance (ADTI) scale and subscales. We compared psychosocial measures between gender identity groups. We also compared stratified correlations for psychosocial measures (well-being, parent relationships, body image, loneliness, and FOMO) with ADTI and Problematic and Risky Internet Use Screening Scale-3 scores between gender identity groups. All comparisons were adjusted for age, race, and ethnicity.

RESULTS

Among 4575 adolescents, 53 (1.16%) self-identified as TNG youth. TNG youth had lower scores for well-being (23.76 vs 26.47; P<.001), parent relationships (19.29 vs 23.32; P<.001), and body image (13.50 vs 17.12; P<.001), and higher scores for loneliness (9.28 vs 6.55; P<.001) and FOMO (27.93 vs 23.89; P=.004), compared with cisgender peers. In a pattern different from that of their cisgender peers, better well-being scores and body image for TNG youth predicted higher problematic internet use (PIU) scores (correlation coefficients of 0.32 vs -0.07; P=.004 and 0.26 vs -0.21; P=.002, respectively). FOMO was a stronger positive predictor of higher ADTI total and subscale scores for cisgender youth compared with TNG youth.

CONCLUSIONS

Overall, this study supports previously demonstrated disparities in the psychosocial wellness of TNG youth and adds that these disparities include loneliness and FOMO. This study shows prediction of PIU by both higher well-being and better body image, indicating that PIU may not be unilaterally driven by problematic factors among TNG youth. We suggest that this may be because of the specific digital media functions that TNG youth engage with as a disenfranchised population.

Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data

We collected over 50 million tweets referencing COVID-19 to understand the public’s gendered discourses and concerns during the pandemic. We filtered the tweets based on English language and among three gender categories: men, women, and sexual and gender minorities. We used a mixed-method approach that included topic modelling, sentiment analysis, and text mining extraction procedures including words’ mapping, proximity plots, top hashtags and mentions, and most retweeted posts. Our findings show stark differences among the different genders. In relation to women, we found a salient discussion on the risks of domestic violence due to the lockdown especially towards women and girls, while emphasizing financial challenges. The public discourses around SGM mostly revolved around blood donation concerns, which is a reminder of the discrimination against some of these communities during the early days of the HIV/AIDS epidemic. Finally, the discourses around men were focused on the high death rates and the sentiment analysis results showed more negative tweets than among the other genders. The study concludes that Twitter influencers can drive major online discussions which can be useful in addressing communication needs during pandemics.

Health informatics and health equity: improving our reach and impact

Health informatics studies the use of information technology to improve human health. As informaticists, we seek to reduce the gaps between current healthcare practices and our societal goals for better health and healthcare quality, safety, or cost. It is time to recognize health equity as one of these societal goals-a point underscored by this Journal of the American Medical Informatics Association Special Focus Issue, "Health Informatics and Health Equity: Improving our Reach and Impact." This Special Issue highlights health informatics research that focuses on marginalized and underserved groups, health disparities, and health equity. In particular, this Special Issue intentionally showcases high-quality research and professional experiences that encompass a broad range of subdisciplines, methods, marginalized populations, and approaches to disparities. Building on this variety of submissions and other recent developments, we highlight contents of the Special Issue and offer an assessment of the state of research at the intersection of health informatics and health equity.

Coming Out to Doctors, Coming Out to "Everyone": Understanding the Average Sequence of Transgender Identity Disclosures Using Social Media Data

Purpose: Gender transition is a complex life change, and transgender identity disclosures are pivotal moments that delineate the gender transition process. The purpose of this study was to quantify the average sequence in which transgender people disclose their transgender identity to different people in their lives, such as medical professionals, family members, and online networks, and to understand the emotional implications of these disclosures.

Methods: We used mixed methods to identify 362 transgender identity disclosure social media posts within 41,066 total posts from 240 Tumblr transition blogs (online spaces in which transgender people document gender transitions). We manually assigned each disclosure post an audience category, and then calculated the average sequence in which people in this sample disclosed their transgender identity to different audiences.

Results: Health professionals, such as physicians and therapists, were on average some of the very first people to whom transgender Tumblr bloggers disclosed their transgender identity. Such disclosures were often anxiety provoking and emotionally difficult, whether intentional or involuntary. Next, they often disclosed to friends, followed by close family (e.g., parents and siblings) and then extended family (e.g., grandparents). Mass disclosures to large portions of a person's network, such as on one's Facebook profile, usually came late in the disclosure process.

Conclusion: Gender transition is a staged process that includes a series of disclosures to different audiences that follows an average sequence. Because health care providers (e.g., physicians and therapists) who work with transgender patients are often some of the very first people to whom transgender people in our sample disclosed, providers must practice extra sensitivity when responding to such disclosures.

Human-Computer Interaction, Ethics, and Biomedical Informatics

Objectives : To provide an overview of recent work at the intersection of Biomedical Informatics, Human-Computer Interaction, and Ethics.

Methods : Search terms for Human-Computer Interaction, Biomedical Informatics, and Ethics were used to identify relevant papers published between 2017 and 2019.Relevant papers were identified through multiple methods, including database searches, manual reviews of citations, recent publications, and special collections, as well as through peer recommendations. Identified articles were reviewed and organized into broad themes.

Results : We identified relevant papers at the intersection of Biomedical Informatics, Human-Computer Interactions, and Ethics in over a dozen journals. The content of these papers was organized into three broad themes: ethical issues associated with systems in use, systems design, and responsible conduct of research.

Conclusions : The results of this overview demonstrate an active interest in exploring the ethical implications of Human-Computer Interaction concerns in Biomedical Informatics. Papers emphasizing ethical concerns associated with patient-facing tools, mobile devices, social media, privacy, inclusivity, and e-consent reflect the growing prominence of these topics in biomedical informatics research. New questions in these areas will likely continue to arise with the growth of precision medicine and citizen science.