Key language markers of depression on social media depend on race.

Depression has robust natural language correlates and can increasingly be measured in language using predictive models. However, despite evidence that language use varies as a function of individual demographic features (e.g., age, gender), previous work has not systematically examined whether and how depression's association with language varies by race. We examine how race moderates the relationship between language features (i.e., first-person pronouns and negative emotions) from social media posts and self-reported depression, in a matched sample of Black and White English speakers in the United States. Our findings reveal moderating effects of race: While depression severity predicts I-usage in White individuals, it does not in Black individuals. White individuals use more belongingness and self-deprecation-related negative emotions. Machine learning models trained on similar amounts of data to predict depression severity performed poorly when tested on Black individuals, even when they were trained exclusively using the language of Black individuals. In contrast, analogous models tested on White individuals performed relatively well. Our study reveals surprising race-based differences in the expression of depression in natural language and highlights the need to understand these effects better, especially before language-based models for detecting psychological phenomena are integrated into clinical practice.

The emotional and mental health impact of the murder of George Floyd on the US population.

On May 25, 2020, George Floyd, an unarmed Black American male, was killed by a White police officer. Footage of the murder was widely shared. We examined the psychological impact of Floyd's death using two population surveys that collected data before and after his death; one from Gallup (117,568 responses from n = 47,355) and one from the US Census (409,652 responses from n = 319,471). According to the Gallup data, in the week following Floyd's death, anger and sadness increased to unprecedented levels in the US population. During this period, more than a third of the US population reported these emotions. These increases were more pronounced for Black Americans, nearly half of whom reported these emotions. According to the US Census Household Pulse data, in the week following Floyd's death, depression and anxiety severity increased among Black Americans at significantly higher rates than that of White Americans. Our estimates suggest that this increase corresponds to an additional 900,000 Black Americans who would have screened positive for depression, associated with a burden of roughly 2.7 million to 6.3 million mentally unhealthy days.

The role of digital health technologies in COVID-19 surveillance and recovery: a specific case of long haulers.

Digital health and technologies are essential to curbing the novel coronavirus 2019 (COVID-19) pandemic especially with shelter-in-place and social distancing orders. Epidemiologists and public health officials are tapping into frequently used technologies like wearables, digital devices, digital and social media data to detect and validate COVID-19 symptoms throughout the pandemic, especially during early stages when symptoms were evolving. In this article, we review how digital technologies and social media platforms can identify and inform our understanding of COVID-19 pandemic surveillance and recovery efforts. We analyze Reddit narrative posts and comments on r/covidlonghaulers to demonstrate how social media can be used to better understand COVID-19 pandemic. Using Reddit data, we highlight long haulers' patient journeys and shed light on potential consequences of their condition. We identified 21 themes, of which the following were significantly associated with valence: COVID-19 Symptoms (r = -0.037), medical advice (r = -0.030), medical system (r = -0.029), bodily processes (r = -0.020), questions (r = 0.024), physical activity (r = 0.033), self-differentials and negations (r = 0.040) and supplements (r = 0.025). Our brief literature review and analysis of r/covidlonghaulers narrative posts demonstrate the value of digital technologies and social media platforms as they act as modern avenues for public health, safety, and well-being.

Partisan Differences in Twitter Language Among US Legislators During the COVID-19 Pandemic: Cross-sectional Study.

BACKGROUND: As policy makers continue to shape the national and local responses to the COVID-19 pandemic, the information they choose to share and how they frame their content provide key insights into the public and health care systems. OBJECTIVE: We examined the language used by the members of the US House and Senate during the first 10 months of the COVID-19 pandemic and measured content and sentiment based on the tweets that they shared. METHODS: We used Quorum (Quorum Analytics Inc) to access more than 300,000 tweets posted by US legislators from January 1 to October 10, 2020. We used differential language analyses to compare the content and sentiment of tweets posted by legislators based on their party affiliation. RESULTS: We found that health care-related themes in Democratic legislators' tweets focused on racial disparities in care (odds ratio [OR] 2.24, 95% CI 2.22-2.27; P<.001), health care and insurance (OR 1.74, 95% CI 1.7-1.77; P<.001), COVID-19 testing (OR 1.15, 95% CI 1.12-1.19; P<.001), and public health guidelines (OR 1.25, 95% CI 1.22-1.29; P<.001). The dominant themes in the Republican legislators' discourse included vaccine development (OR 1.51, 95% CI 1.47-1.55; P<.001) and hospital resources and equipment (OR 1.22, 95% CI 1.18-1.25). Nonhealth care-related topics associated with a Democratic affiliation included protections for essential workers (OR 1.55, 95% CI 1.52-1.59), the 2020 election and voting (OR 1.31, 95% CI 1.27-1.35), unemployment and housing (OR 1.27, 95% CI 1.24-1.31), crime and racism (OR 1.22, 95% CI 1.18-1.26), public town halls (OR 1.2, 95% CI 1.16-1.23), the Trump Administration (OR 1.22, 95% CI 1.19-1.26), immigration (OR 1.16, 95% CI 1.12-1.19), and the loss of life (OR 1.38, 95% CI 1.35-1.42). The themes associated with the Republican affiliation included China (OR 1.89, 95% CI 1.85-1.92), small business assistance (OR 1.27, 95% CI 1.23-1.3), congressional relief bills (OR 1.23, 95% CI 1.2-1.27), press briefings (OR 1.22, 95% CI 1.19-1.26), and economic recovery (OR 1.2, 95% CI 1.16-1.23). CONCLUSIONS: Divergent language use on social media corresponds to the partisan divide in the first several months of the course of the COVID-19 public health crisis.

Online Reviews of Specialized Drug Treatment Facilities-Identifying Potential Drivers of High and Low Patient Satisfaction.

BACKGROUND: Despite the importance of high-quality and patient-centered substance use disorder treatment, there are no standardized ratings of specialized drug treatment facilities and their services. Online platforms offer insights into potential drivers of high and low patient experience. OBJECTIVE: We sought to analyze publicly available online review content of specialized drug treatment facilities and identify themes within high and low ratings. DESIGN: This was a retrospective analysis of online ratings and reviews of specialized drug treatment facilities in Pennsylvania listed within the 2016 National Directory of Drug and Alcohol Abuse Treatment Facilities. Latent Dirichlet Allocation, a machine learning approach to narrative text, was used to identify themes within reviews. Differential Language Analysis was then used to measure correlations between themes and star ratings. SETTING: Online reviews of Pennsylvania's specialized drug treatment facilities posted to Google and Yelp (July 2010-August 2018). RESULTS: A total of 7823 online ratings were posted over 8 years. The distribution was bimodal (43% 5-star and 34% 1-star). The average weighted rating of a facility was 3.3 stars. Online themes correlated with 5-star ratings were the following: focus on recovery (r = 0.53), helpfulness of staff (r = 0.43), compassionate care (r = 0.37), experienced a life-changing moment (r = 0.32), and staff professionalism (r = 0.29). Themes correlated with a 1-star rating were waiting time (r = 0.41), poor accommodations (0.26), poor phone communication (r = 0.24), medications given (0.24), and appointment availability (r = 0.23). Themes derived from review content were similar to 9 of the 14 facility-level services highlighted by the Substance Abuse and Mental Health Services Administration's National Survey of Substance Abuse Treatment Services. CONCLUSIONS: Individuals are sharing their ratings and reviews of specialized drug treatment facilities on online platforms. Organically derived reviews of the patient experience, captured by online platforms, reveal potential drivers of high and low ratings. These represent additional areas of focus which can inform patient-centered quality metrics for specialized drug treatment facilities.

Patients' willingness to share digital health and non-health data for research: a cross-sectional study.

BACKGROUND: Patients generate large amounts of digital data through devices, social media applications, and other online activities. Little is known about patients' perception of the data they generate online and its relatedness to health, their willingness to share data for research, and their preferences regarding data use. METHODS: Patients at an academic urban emergency department were asked if they would donate any of 19 different types of data to health researchers and were asked about their views on data types' health relatedness. Factor analysis was used to identify the structure in patients' perceptions of willingness to share different digital data, and their health relatedness. RESULTS: Of 595 patients approached 206 agreed to participate, of whom 104 agreed to share at least one types of digital data immediately, and 78% agreed to donate at least one data type after death. EMR, wearable, and Google search histories (80%) had the highest percentage of reported health relatedness. 72% participants wanted to know the results of any analysis of their shared data, and half wanted their healthcare provider to know. CONCLUSION: Patients in this study were willing to share a considerable amount of personal digital data with health researchers. They also recognize that digital data from many sources reveal information about their health. This study opens up a discussion around reconsidering US privacy protections for health information to reflect current opinions and to include their relatedness to health.

To Retweet or Not to Retweet: Understanding What Features of Cardiovascular Tweets Influence Their Retransmission.

Twitter is one of the largest social networking sites (SNSs) in the world, yet little is known about what cardiovascular health related tweets go viral and what characteristics are associated with retransmission. The current study aims to identify a function of the observable characteristics of cardiovascular tweets, including characteristics of the source, content, and style that predict the retransmission of these tweets. We identified a random sample of 1,251 tweets associated with CVD originating from the United States between 2009 and 2015. Automated coding was conducted on the affect values of the tweets as well as the presence/absence of any URL, mention of another user, question mark, exclamation mark, and hashtag. We hand-coded the tweets' novelty, utility, theme, and source. The count of retweets was positively predicted by message utility, health organization source, and mention of user handle, but negatively predicted by the presence of URL and nonhealth organization source. Regarding theme, compared to the tweets focusing on risk factor, tweets on treatment and management predicted fewer retweets while supportive tweets predicted more retweets. These findings suggest opportunities for harnessing Twitter to better disseminate cardiovascular educational and supportive information on SNSs.

"Do not inject our babies": a social listening analysis of public opinion about authorizing pediatric COVID-19 vaccines.

Designing effective childhood vaccination counseling guidelines, public health campaigns, and school-entry mandates requires a nuanced understanding of the information ecology in which parents make vaccination decisions. However, evidence is lacking on how best to "catch the signal" about the public's attitudes, beliefs, and misperceptions. In this study, we characterize public sentiment and discourse about vaccinating children against SARS-CoV-2 with mRNA vaccines to identify prevalent concerns about the vaccine and to understand anti-vaccine rhetorical strategies. We applied computational topic modeling to 149 897 comments submitted to regulations.gov in October 2021 and February 2022 regarding the Food and Drug Administration's Vaccines and Related Biological Products Advisory Committee's emergency use authorization of the COVID-19 vaccines for children. We used a latent Dirichlet allocation topic modeling algorithm to generate topics and then used iterative thematic and discursive analysis to identify relevant domains, themes, and rhetorical strategies. Three domains emerged: (1) specific concerns about the COVID-19 vaccines; (2) foundational beliefs shaping vaccine attitudes; and (3) rhetorical strategies deployed in anti-vaccine arguments. Computational social listening approaches can contribute to misinformation surveillance and evidence-based guidelines for vaccine counseling and public health promotion campaigns.

Detecting and monitoring concerns against HPV vaccination on social media using large language models.

Health risks due to preventable infections such as human papillomavirus (HPV) are exacerbated by persistent vaccine hesitancy. Due to limited sample sizes and the time needed to roll out, traditional methodologies like surveys and interviews offer restricted insights into quickly evolving vaccine concerns. Social media platforms can serve as fertile ground for monitoring vaccine-related conversations and detecting emerging concerns in a scalable and dynamic manner. Using state-of-the-art large language models, we propose a minimally supervised end-to-end approach to identify concerns against HPV vaccination from social media posts. We detect and characterize the concerns against HPV vaccination pre- and post-2020 to understand the evolution of HPV vaccine discourse. Upon analyzing 653 k HPV-related post-2020 tweets, adverse effects, personal anecdotes, and vaccine mandates emerged as the dominant themes. Compared to pre-2020, there is a shift towards personal anecdotes of vaccine injury with a growing call for parental consent and transparency. The proposed approach provides an end-to-end system, i.e. given a collection of tweets, a list of prevalent concerns is returned, providing critical insights for crafting targeted interventions, debunking messages, and informing public health campaigns.

A cross-cultural examination of temporal orientation through everyday language on social media.

Past research has shown that culture can form and shape our temporal orientation-the relative emphasis on the past, present, or future. However, there are mixed findings on how temporal orientations vary between North American and East Asian cultures due to the limitations of survey methodology and sampling. In this study, we applied an inductive approach and leveraged big data and natural language processing between two popular social media platforms-Twitter and Weibo-to assess the similarities and differences in temporal orientation in the United States of America and China, respectively. We first established predictive models from annotation data and used them to classify a larger set of English Twitter sentences (NTW = 1,549,136) and a larger set of Chinese Weibo sentences (NWB = 95,181) into four temporal catetories-past, future, atemporal present, and temporal present. Results show that there is no significant difference between Twitter and Weibo on past or future orientations; the large temporal orientation difference between North Americans and Chinese derives from their different prevailing focus on atemporal (e.g., facts, ideas) present (Twitter) or temporal present (e.g., the "here" and "now") (Weibo). Our findings contribute to the debate on cultural differences in temporal orientations with new perspectives following a new methodological approach. The study's implications call for a reevaluation of how temporal orientation is measured in cross-cultural studies, emphasizing the use of large-scale language data and acknowledging the atemporal present category. Understanding temporal orientations can guide effective cross-cultural communication strategies to tailor approaches for different audience based on temporal orientations, enhancing intercultural understanding and engagement.

Characterizing Responses to COVID-19 Vaccine Promotion on TikTok.

PURPOSE: The Alabama Department of Public Health (ADPH) sponsored a TikTok contest to improve vaccination rates among young people. This analysis sought to advance understanding of COVID-19 vaccine perceptions among ADPH contestants and TikTok commenters. APPROACH: This exploratory content analysis characterized sentiment and imagery in the TikTok videos and comments. Videos were coded by two reviewers and engagement metrics were collected for each video. SETTING: Publicly available TikTok videos entered into ADPH's contest with the hashtags #getvaccinatedAL and #ADPH between July 16 - August 6, 2021. PARTICIPANTS: ADPH contestants (n = 44) and TikTok comments (n = 502). METHOD: A content analysis was conducted; videos were coded by two reviewers and engagement metrics was collected for each video (e.g., reason for vaccination, content, type of vaccination received). Video comments were analyzed using VADER, a lexicon and rule-based sentiment analysis tool). RESULTS: Of 44 videos tagged with #getvaccinatedAL and #ADPH, 37 were related to the contest. Of the 37 videos, most cited family/friends and civic duty as their reason to get the COVID-19 vaccine. Videos were shared an average of 9 times and viewed 977 times. 70% of videos had comments, ranging from 0-61 (mean 44). Words used most in positively coded comments included, "beautiful," "smiling face emoji with 3 hearts," "masks," and "good.;" whereas words used most in negatively coded comments included "baby," "me," "chips," and "cold." CONCLUSION: Understanding COVID-19 vaccine sentiment expressed on social media platforms like TikTok can be a powerful tool and resource for public health messaging.

Reddit language indicates changes associated with diet, physical activity, substance use, and smoking during COVID-19.

COVID-19 has adversely impacted the health behaviors of billions of people across the globe, modifying their former trends in health and lifestyle. In this paper, we compare the psychosocial language markers associated with diet, physical activity, substance use, and smoking before and after the onset of COVID-19 pandemic. We leverage the popular social media platform Reddit to analyze 1 million posts between January 6, 2019, to January 5, 2021, from 22 different communities (i.e., subreddits) that belong to four broader groups-diet, physical activity, substance use, and smoking. We identified that before the COVID-19 pandemic, posts involved sharing information about vacation, international travel, work, family, consumption of illicit substances, vaping, and alcohol, whereas during the pandemic, posts contained emotional content associated with quarantine, withdrawal symptoms, anxiety, attempts to quit smoking, cravings, weight loss, and physical fitness. Prevalent topic analysis showed that the pandemic was associated with discussions about nutrition, physical fitness, and outdoor activities such as backpacking and biking, suggesting users' focus shifted toward their physical health during the pandemic. Starting from the week of March 23, 2020, when several stay-at-home policies were enacted, users wrote more about coping with stress and anxiety, alcohol misuse and abuse, and harm-reduction strategies like switching from hard liquor to beer/wine after people were socially isolated. In addition, posts related to use of substances such as benzodiazepines (valium, xanax, clonazepam), nootropics (kratom, phenibut), and opioids peaked around March 23, 2020, followed by a decline. Of note, unlike the general decline observed, the volume of posts related to alternatives to heroin (e.g., fentanyl) increased during the COVID-19 pandemic. Posts about quitting smoking gained momentum after late March 2020, and there was a sharp decline in posts about craving to smoke. This study highlights the significance of studying social media discussions on platforms like Reddit which are a rich ecological source of human experiences and provide insights to inform targeted messaging and mitigation strategies, and further complement ongoing traditional primary data collection methods.

Investigating Social Media to Evaluate Emergency Medicine Physicians' Emotional Well-being During COVID-19.

Importance: Emergency medicine (EM) physicians experience tremendous emotional health strain, which has been exacerbated during COVID-19, and many have taken to social media to express themselves. Objective: To analyze social media content from academic EM physicians and resident physicians to investigate changes in content and language as indicators of their emotional well-being. Design, Setting, and Participants: This cross-sectional study used machine learning and natural language processing of Twitter posts from self-described academic EM physicians and resident physicians between March 2018 and March 2022. Participants included academic EM physicians and resident physicians with publicly accessible posts (at least 300 total words across the posts) from the US counties with the top 10 COVID-19 case burdens. Data analysis was performed from June to September 2022. Exposure: Being an EM physician or resident physician who posted on Twitter. Main Outcomes and Measures: Social media content themes during the prepandemic period, during the pandemic, and across the phases of the pandemic were analyzed. Psychological constructs evaluated included anxiety, anger, depression, and loneliness. Positive and negative language sentiment within posts was measured. Results: This study identified 471 physicians with a total of 198 867 posts (mean [SD], 11 403 [18 998] words across posts; median [IQR], 3445 [1100-11 591] words across posts). The top 5 prepandemic themes included free open-access medical education (Cohen d, 0.44; 95% CI, 0.38-0.50), residency education (Cohen d, 0.43; 95% CI, 0.37-0.49), gun violence (Cohen d, 0.37; 95% CI, 0.32-0.44), quality improvement in health care (Cohen d, 0.33; 95% CI, 0.27-0.39), and professional resident associations (Cohen d, 0.33; 95% CI, 0.27-0.39). During the pandemic, themes were significantly related to healthy behaviors during COVID-19 (Cohen d, 0.83; 95% CI, 0.77-0.90), pandemic response (Cohen d, 0.71; 95% CI, 0.65-0.77), vaccines and vaccination (Cohen d, 0.60; 95% CI, 0.53-0.66), unstable housing and homelessness (Cohen d, 0.40; 95% CI, 0.34-0.47), and emotional support for others (Cohen d, 0.40; 95% CI, 0.34-0.46). Across the phases of the pandemic, thematic content within social media posts changed significantly. Compared with the prepandemic period, there was significantly less positive, and concordantly more negative, language used during COVID-19. Estimates of loneliness, anxiety, anger, and depression also increased significantly during COVID-19. Conclusions and Relevance: In this cross-sectional study, key thematic shifts and increases in language related to anxiety, anger, depression, and loneliness were identified in the content posted on social media by academic EM physicians and resident physicians during the pandemic. Social media may provide a real-time and evolving landscape to evaluate thematic content and linguistics related to emotions and sentiment for health care workers.

An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives.

Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.

The online language of work-personal conflict.

With the blurring of boundaries in this digital age, there is increasing concern around work-personal conflict. Assessing and tracking work-personal conflict is critical as it not only affects individual workers but is also a vital measure among broader well-being and economic indices. This inductive study examines the extent to which work-personal conflict corresponds to individuals' language use on social media. We apply an open-vocabulary analysis to the posts of 2810 Facebook users who also completed a survey for an established work-personal conflict scale. It was found that the language-based model can predict personal-to-work conflict (r = 0.23) and work-to-personal conflict (r = 0.15) and provide important insights into such conflicts. Specifically, we found that high personal-to-work conflict was associated with netspeak and swearing, while low personal-to-work conflict was associated with language about work and positivity. We found that high work-to-personal conflict was associated with negative emotion and negative tone, while low work-to-personal conflict was associated with positive emotion and language about birthdays.