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.

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.

Generative adversarial networks based skin lesion segmentation.

Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way to assist clinicians in this task is using computer-aided diagnosis tools that automatically segment skin lesions from dermoscopic images. We propose a novel adversarial learning-based framework called Efficient-GAN (EGAN) that uses an unsupervised generative network to generate accurate lesion masks. It consists of a generator module with a top-down squeeze excitation-based compound scaled path, an asymmetric lateral connection-based bottom-up path, and a discriminator module that distinguishes between original and synthetic masks. A morphology-based smoothing loss is also implemented to encourage the network to create smooth semantic boundaries of lesions. The framework is evaluated on the International Skin Imaging Collaboration Lesion Dataset. It outperforms the current state-of-the-art skin lesion segmentation approaches with a Dice coefficient, Jaccard similarity, and accuracy of 90.1%, 83.6%, and 94.5%, respectively. We also design a lightweight segmentation framework called Mobile-GAN (MGAN) that achieves comparable performance as EGAN but with an order of magnitude lower number of training parameters, thus resulting in faster inference times for low compute resource settings.

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.

Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign.

BACKGROUND: The COVID-19 pandemic was accompanied by an "infodemic"-an overwhelming excess of accurate, inaccurate, and uncertain information. The social media-based science communication campaign Dear Pandemic was established to address the COVID-19 infodemic, in part by soliciting submissions from readers to an online question box. Our study characterized the information needs of Dear Pandemic's readers by identifying themes and longitudinal trends among question box submissions. METHODS: We conducted a retrospective analysis of questions submitted from August 24, 2020, to August 24, 2021. We used Latent Dirichlet Allocation topic modeling to identify 25 topics among the submissions, then used thematic analysis to interpret the topics based on their top words and submissions. We used t-Distributed Stochastic Neighbor Embedding to visualize the relationship between topics, and we used generalized additive models to describe trends in topic prevalence over time. RESULTS: We analyzed 3839 submissions, 90% from United States-based readers. We classified the 25 topics into 6 overarching themes: 'Scientific and Medical Basis of COVID-19,' 'COVID-19 Vaccine,' 'COVID-19 Mitigation Strategies,' 'Society and Institutions,' 'Family and Personal Relationships,' and 'Navigating the COVID-19 Infodemic.' Trends in topics about viral variants, vaccination, COVID-19 mitigation strategies, and children aligned with the news cycle and reflected the anticipation of future events. Over time, vaccine-related submissions became increasingly related to those surrounding social interaction. CONCLUSIONS: Question box submissions represented distinct themes that varied in prominence over time. Dear Pandemic's readers sought information that would not only clarify novel scientific concepts, but would also be timely and practical to their personal lives. Our question box format and topic modeling approach offers science communicators a robust methodology for tracking, understanding, and responding to the information needs of online audiences.

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.

Effect of Integrating Patient-Generated Digital Data Into Mental Health Therapy: A Randomized Controlled Trial.

OBJECTIVE: The authors sought to determine whether providing summaries of patients' social media and other digital data to patients and their clinicians improves patients' health-related quality of life (HRQoL) measured by the RAND 36-Item Short Form Health Survey (SF-36). METHODS: The authors randomly assigned 115 adults receiving outpatient mental health therapy to usual care or to periodic sharing of summaries of their digital data with their clinician providing psychosocial therapy. The study was conducted October 2020-December 2021. RESULTS: Patients' mean±SD age was 31.3±10.5 years, and 82% were women. At 60 days after enrollment, no statistically significant change was detected in SF-36 scores for patients randomly allocated to the intervention (mean difference=-0.39, 95% CI=-4.17, 3.39) or to usual care (mean difference=-1.98, 95% CI=-5.74, 1.77), and no significant between-arm difference was observed (between-arm difference=1.60, 95% CI=-3.67, 6.86). CONCLUSIONS: Collecting and summarizing digital data for use in mental health treatment was feasible for patients but did not significantly improve their HRQoL or other measures of mental health.

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.

Understanding the expression of loneliness on Twitter across age groups and genders.

Some individuals seek support around loneliness on social media forums. In this work, we aim to determine differences in the use of language by users-in different age groups and genders (female, male), who publish posts on Twitter expressing loneliness. We hypothesize that these differences in the use of language will reflect how these users express themselves and some of their support needs. Interventions may vary depending on the age and gender of an individual, hence, in order to identify high-risk individuals who express loneliness on Twitter and provide appropriate interventions for these users, it is important to understand the variations in language use by users who belong to different age groups and genders and post about loneliness on Twitter. We discuss the findings from this work and how they can help guide the design of online loneliness interventions.

COVID-19 contact tracing app reviews reveal concerns and motivations around adoption.

BACKGROUND: Google and Apple's Exposure Notifications System (ENS) was developed early in the COVID-19 pandemic to complement existing contact tracing efforts while protecting user privacy. An analysis by the Associated Press released in December 2020 estimated approximately 1 in 14 people had downloaded apps in states one was available. In this study, we assessed the motivation and experience of individuals who downloaded ENS apps from the Google Play and Apple App Stores. METHODS: We collected review text, star rating, and date of rating for all the reviews on ENS apps in the Google Play and Apple App stores. We extracted the relative frequency of single words and phrases from reviews and created an open vocabulary language, with themes categorized by the research team, to study the salient themes around reviews with high (3-5 stars), neutral (3 stars), and negative (1-2 stars) ratings using logistic regression. RESULTS: Of 7622 reviews obtained from 26 states between 04/07/2020 to 03/31/2021, 6364 were from Google Play Store, and 1258 were from Apple App Store. We obtained reviews for a total of 38 apps, with 25 apps from the Google Play Store and 13 apps from the Apple Play Store. 78% of the reviews are either 1 star or 5 stars. Positive reviews were driven by ease of use, support for the state government in creating the app, and encouragement for others to download, as well as engage in other COVID-19 precautions. Negative and neutral reviews focused on issues with app functionality (i.e., installation and tracking errors). CONCLUSIONS: Uptake was the largest barrier to success for ENS apps, but states can use insight from app store reviews to better position themselves if they choose to develop further public health apps.

Patient Experience and Satisfaction in Online Reviews of Obstetric Care: Observational Study.

BACKGROUND: The quality of care in labor and delivery is traditionally measured through the Hospital Consumer Assessment of Healthcare Providers and Systems but less is known about the experiences of care reported by patients and caregivers on online sites that are more easily accessed by the public. OBJECTIVE: The aim of this study was to generate insight into the labor and delivery experience using hospital reviews on Yelp. METHODS: We identified all Yelp reviews of US hospitals posted online from May 2005 to March 2017. We used a machine learning tool, latent Dirichlet allocation, to identify 100 topics or themes within these reviews and used Pearson r to identify statistically significant correlations between topics and high (5-star) and low (1-star) ratings. RESULTS: A total of 1569 hospitals listed in the American Hospital Association directory had at least one Yelp posting, contributing a total of 41,095 Yelp reviews. Among those hospitals, 919 (59%) had at least one Yelp rating for labor and delivery services (median of 9 reviews), contributing a total of 6523 labor and delivery reviews. Reviews concentrated among 5-star (n=2643, 41%) and 1-star reviews (n=1934, 30%). Themes strongly associated with favorable ratings included the following: top-notch care (r=0.45, P<.001), describing staff as comforting (r=0.52, P<.001), the delivery experience (r=0.46, P<.001), modern and clean facilities (r=0.44, P<.001), and hospital food (r=0.38, P<.001). Themes strongly correlated with 1-star labor and delivery reviews included complaints to management (r=0.30, P<.001), a lack of agency among patients (r=0.47, P<.001), and issues with discharging from the hospital (r=0.32, P<.001). CONCLUSIONS: Online review content about labor and delivery can provide meaningful information about patient satisfaction and experiences. Narratives from these reviews that are not otherwise captured in traditional surveys can direct efforts to improve the experience of obstetrical care.

Perceptually Unimportant Information Reduction and Cosine Similarity-Based Quality Assessment of 3D-Synthesized Images.

Quality assessment of 3D-synthesized images has traditionally been based on detecting specific categories of distortions such as stretching, black-holes, blurring, etc. However, such approaches have limitations in accurately detecting distortions entirely in 3D synthesized images affecting their performance. This work proposes an algorithm to efficiently detect the distortions and subsequently evaluate the perceptual quality of 3D synthesized images. The process of generation of 3D synthesized images produces a few pixel shift between reference and 3D synthesized image, and hence they are not properly aligned with each other. To address this, we propose using morphological operation (opening) in the residual image to reduce perceptually unimportant information between the reference and the distorted 3D synthesized image. The residual image suppresses the perceptually unimportant information and highlights the geometric distortions which significantly affect the overall quality of 3D synthesized images. We utilized the information present in the residual image to quantify the perceptual quality measure and named this algorithm as Perceptually Unimportant Information Reduction (PU-IR) algorithm. At the same time, the residual image cannot capture the minor structural and geometric distortions due to the usage of erosion operation. To address this, we extract the perceptually important deep features from the pre-trained VGG-16 architectures on the Laplacian pyramid. The distortions in 3D synthesized images are present in patches, and the human visual system perceives even the small levels of these distortions. With this view, to compare these deep features between reference and distorted image, we propose using cosine similarity and named this algorithm as Deep Features extraction and comparison using Cosine Similarity (DF-CS) algorithm. The cosine similarity is based upon their similarity rather than computing the magnitude of the difference of deep features. Finally, the pooling is done to obtain the objective quality scores using simple multiplication to both PU-IR and DF-CS algorithms. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment.

Stretching Artifacts Identification for Quality Assessment of 3D-Synthesized Views.

Existing Quality Assessment (QA) algorithms consider identifying "black-holes" to assess perceptual quality of 3D-synthesized views. However, advancements in rendering and inpainting techniques have made black-hole artifacts near obsolete. Further, 3D-synthesized views frequently suffer from stretching artifacts due to occlusion that in turn affect perceptual quality. Existing QA algorithms are found to be inefficient in identifying these artifacts, as has been seen by their performance on the IETR dataset. We found, empirically, that there is a relationship between the number of blocks with stretching artifacts in view and the overall perceptual quality. Building on this observation, we propose a Convolutional Neural Network (CNN) based algorithm that identifies the blocks with stretching artifacts and incorporates the number of blocks with the stretching artifacts to predict the quality of 3D-synthesized views. To address the challenge with existing 3D-synthesized views dataset, which has few samples, we collect images from other related datasets to increase the sample size and increase generalization while training our proposed CNN-based algorithm. The proposed algorithm identifies blocks with stretching distortions and subsequently fuses them to predict perceptual quality without reference, achieving improvement in performance compared to existing no-reference QA algorithms that are not trained on the IETR dataset. The proposed algorithm can also identify the blocks with stretching artifacts efficiently, which can further be used in downstream applications to improve the quality of 3D views. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment.

Head versus heart: social media reveals differential language of loneliness from depression.

We study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors. Compared to depression, the language associated with loneliness reflects a stronger cognitive focus, including more references to cognitive processes (i.e., differentiation and tentative language, thoughts, and the observation of irregularities), and cognitive activities like reading and writing. As might be expected, less lonely users were more likely to reference social relationships (e.g., friends and family, romantic relationships), and use first-person plural pronouns. Our findings suggest that the mechanisms of loneliness include self-oriented cognitive activities (i.e., reading) and an overattention to the interpretation of information in the environment. These data-driven ecological findings suggest interventions for loneliness that target maladaptive social cognitions (e.g., through reframing the perception of social environments), strengthen social relationships, and treat other affective distress (i.e., depression).

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.

Twitter discourse reveals geographical and temporal variation in concerns about COVID-19 vaccines in the United States.

The speed at which social media is propagating COVID-19 misinformation and its potential reach and impact is growing, yet little work has focused on the potential applications of these data for informing public health communication about COVID-19 vaccines. We used Twitter to access a random sample of over 78 million vaccine-related tweets posted between December 1, 2020 and February 28, 2021 to describe the geographical and temporal variation in COVID-19 vaccine discourse. Urban suburbs posted about equitable distribution in communities, college towns talked about in-clinic vaccinations near universities, evangelical hubs posted about operation warp speed and thanking God, exurbs posted about the 2020 election, Hispanic centers posted about concerns around food and water, and counties in the ACP African American South posted about issues of trust, hesitancy, and history. The graying America ACP community posted about the federal government's failures; rural middle American counties posted about news press conferences. Topics related to allergic and adverse reactions, misinformation around Bill Gates and China, and issues of trust among Black Americans in the healthcare system were more prevalent in December, topics related to questions about mask wearing, reaching herd immunity and natural infection, and concerns about nursing home residents and workers increased in January, and themes around access to black communities, waiting for appointments, keeping family safe by vaccinating and fighting online misinformation campaigns were more prevalent in February. Twitter discourse around COVID-19 vaccines in the United States varied significantly across different communities and changed over time; these insights could inform targeted messaging and mitigation strategies.

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.

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.

Social media language of healthcare super-utilizers.

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.

Improving Mood Through Community Connection and Resources Using an Interactive Digital Platform: Development and Usability Study.

BACKGROUND: COVID-19 continues to disrupt global health and well-being. In April-May 2020, we generated a digital, remote interactive tool to provide health and well-being resources and foster connectivity among community members through a text messaging platform. OBJECTIVE: This study aimed to prospectively investigate the ability of a health system-based digital, remote, interactive tool to provide health and well-being resources to local community participants and to foster connectivity among them during the early phases of COVID-19. METHODS: We performed descriptive and nonparametric longitudinal statistical analyses to describe and compare the participants' mood ratings over time and thematic analysis of their responses to text messages to further assess mood. RESULTS: From among 393 individuals seeking care in an urban emergency department in an academic setting, engaged in a two-way text messaging platform, we recorded 287 mood ratings and 368 qualitative responses. We observed no difference in the initial mood rating by week of enrollment [Kruskal-Wallis chi-square H(5)=1.34; P=.93], and the average mood rating did not change for participants taken together [Friedman chi-square Q(3)=0.32; P=.96]. However, of participants providing mood ratings at baseline, mood improved significantly among participants who reported a low mood rating at baseline [n=25, 14.97%; Q(3)=20.68; P<.001] but remained stable among those who reported a high mood rating at baseline [n=142, 85.03%; Q(3)=2.84; P=.42]. Positive mood elaborations most frequently included words related to sentiments of thankfulness and gratitude, mostly for a sense of connection and communication; in contrast, negative mood elaborations most frequently included words related to anxiety. CONCLUSIONS: Our findings suggest the feasibility of engaging individuals in a digital community with an emergency department facilitation. Specifically, for those who opt to engage in a text messaging platform during COVID-19, it is feasible to assess and respond to mood-related queries with vetted health and well-being resources.