Social media for early characterization of pandemic symptoms: A qualitative analysis of patient-reported COVID-19 experiences.

BACKGROUND: Patients use social media forums to discuss their medical history and healthcare experiences, providing early insight into real-world patient experiences. We analyzed COVID-19 patient experiences from Reddit social media posts. METHODS: We extracted Reddit Application Programming Interface data for the subreddit/COVID-19 positive from March to August 2020 and selected users tagged as "Tested Positive" or "Tested Positive- Me" flair and who posted at least thirty times in any calendar month, excluding users who explicitly stated location outside of the U.S. For tested-positive patients (users), we created and reviewed individual case profiles summarizing their COVID-19 symptoms, testing, and medications or treatments. Data were imported to Nvivo qualitative analysis software and qualitative coding was conducted. FINDING: There were 31 759 posts and comments from 720 users in March to May 2020 (Q1) and 40 446 posts and comments from 1649 users from June to August 2020 (Q2). Final count of "Tested Positive" was 1296 users (280 in Q1 and 1016 in Q2). Across both quarters, frequently reported symptoms included sore throat, headaches, fevers, or chills. Loss of sense of smell or taste were reported by users in early March, prior to the inclusion of this symptom to the CDC list in April and GI-related symptoms and fatigue were reported in the March to May data, before they were added as a COVID-19 associated symptom in July 2020. Users also reported in-depth descriptions of their symptoms, motivations for testing, and long-term impacts such as post-viral fatigue. INTERPRETATION: Social media data can potentially serve as an early surveillance data source in a pandemic and offer preliminary insights into patient disease experiences.

Semantic Network Analysis of COVID-19 Vaccine Related Text from Reddit

Vaccinations are critical and effective in resolving the current pandemic. With the highly transmissible and deadly SARS-CoV-2 virus (COVID-19), a delay in acceptance, or refusal of vaccines despite the availability of vaccine services poses a significant public health threat. Moreover, vaccine-related hesitancy, mis/disinformation, and anti-vaccination discourse are hindering the rapid uptake of the COVID-19 vaccine. It is urgent to examine how anti-vaccine sentiment and behavior spread online to influence vaccine acceptance. Therefore, this study aimed to investigate the COVID-19 vaccine hesitancy diffusion networks in an online Reddit community within the initial phase of the COVID-19 pandemic. We also sought to assess the anti-vaccine discourse evolution in language content and style. Overall, our study findings could help facilitate and promote efficient messaging strategies/campaigns to improve vaccination rates. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Linguistic Analysis of Online Domestic Violence Testimonies in the Context of COVID-19.

Worldwide, an increase in cases and severity of domestic violence (DV) has been reported as a result of social distancing measures implemented to decrease the spreading of the Coronavirus Disease (COVID-19). As one's language can provide insight in one's mental health, this pre-registered study analyzed word use in a DV online support group, aiming to investigate the impact of the COVID-19 pandemic on DV victims in an ex post facto research design. Words reflecting social support and leisure activities were investigated as protective factors against linguistic indicators of depression in 5,856 posts from the r/domesticviolence subreddit and two neutral comparison subreddits (r/changemyview & r/femalefashionadvice). In the DV support group, the average number of daily posts increased significantly by 22% from pre- to mid-pandemic. Confirmatory analysis was conducted following a registered pre-analysis plan. DV victims used significantly more linguistic indicators of depression than individuals in the comparison groups. This did not change with the onset of COVID-19. The use of negative emotion words was negatively related to the use of social support words (Spearman's rho correlation coefficient [rho] = -0.110) and words referring to leisure activities (rho = -0.137). Pre-occupation with COVID-19 was associated with the use of negative emotion words (rho = 0.148). We conclude that language of DV victims is characterized by indicators of depression and this characteristic is stable over time. Concerns with COVID-19 could contribute to negative emotions, whereas social support and leisure activities could function to some degree as protective factors. A potential weakness of this study is its cross-sectional design and the lack of experimental control. Future studies could make use of natural language processing and other advanced methods of linguistic analysis to learn about the mental health of DV victims.

Web-Based Conversations Regarding Fathers Before and During the COVID-19 Pandemic: Qualitative Content Analysis.

BACKGROUND: Studies of new and expecting parents largely focus on the mother, leaving a gap in knowledge about fathers. OBJECTIVE: This study aimed to understand web-based conversations regarding new and expecting fathers on social media and to explore whether the COVID-19 pandemic has changed the web-based conversation. METHODS: A social media analysis was conducted. Brandwatch (Cision) captured social posts related to new and expecting fathers between February 1, 2019, and February 12, 2021. Overall, 2 periods were studied: 1 year before and 1 year during the pandemic. SAS Text Miner analyzed the data and produced 47% (9/19) of the topics in the first period and 53% (10/19) of the topics in the second period. The 19 topics were organized into 6 broad themes. RESULTS: Overall, 26% (5/19) of the topics obtained during each period were the same, showing consistency in conversation. In total, 6 broad themes were created: fatherhood thoughts, fatherhood celebrations, advice seeking, fatherhood announcements, external parties targeting fathers, and miscellaneous. CONCLUSIONS: Fathers use social media to make announcements, celebrate fatherhood, seek advice, and interact with other fathers. Others used social media to advertise baby products and promote baby-related resources for fathers. Overall, the arrival of the COVID-19 pandemic appeared to have little impact on the excitement and resiliency of new fathers as they transition to parenthood. Altogether, these findings provide insight and guidance on the ways in which public health professionals can rapidly gather information about special populations-such as new and expecting fathers via the web-to monitor their beliefs, attitudes, emotional reactions, and unique lived experiences in context (ie, throughout a global pandemic).

Social Media and Dermatology During the COVID-19 Pandemic: Analyzing User-Submitted Posts Seeking Dermatologic Advice on Reddit.

INTRODUCTION: Reddit, a popular social media website, has numerous forums where users may discuss healthcare-related topics and request diagnostic and treatment advice for dermatologic conditions. We sought to analyze and grade user-submitted requests for dermatologic advice and their top responses on Reddit. METHODS: User-submitted posts requesting diagnostic advice and their respective responses on two popular Reddit forums, SkinCareAddiction (ScA) and DermatologyQuestions (DQ), were reviewed by three board-certified dermatologists using a grading rubric designed for this study. RESULTS: 300 posts and comments were reviewed. Diagnoses among all graders matched in 52.3% of posts with a mean grader confidence score of 4/5 (95% CI 3.89-4.11). 31% of responder's comments recommended a diagnosis not included by any reviewer. Mean scores for the top comment's accuracy, appropriateness, and potential to be misleading/dangerous were 3.28/5 (95% CI 3.12-3.45), 3.3/5 (95% CI 3.14-3.45), and 2.33/5 (95% CI 2.18-2.48), respectively. CONCLUSION: Reddit may be informative to patients requesting dermatologic advice. However, responses should be taken with caution as the information provided may be inaccurate or insufficient for treatment recommendations. Dermatologists should be aware of these resources used by patients.

COVID-19 Impact on Mental Health Analysis based on Reddit Comments

As the COVID-19 outbreak continues to change crucial aspects of daily life, many suspect that the virus has also had a considerable impact on mental health. This study uses natural language processing (NLP) and machine learning on comments from the website Reddit to determine the effects of the COVID-19 pandemic on 5 mental health communities: r/anxiety, r/depression, r/SuicideWatch, r/mentalhealth, and r/COVID19_support. By applying a support vector machine, we extracted features from the data to determine the issues that these subreddits were struggling with the most during the COVID-19 pandemic. We then used a long short-term memory (LSTM) recurrent neural network to study the change in sentiment of each subreddit over the course of the pandemic. Results indicated that, out of the potential factors studied, feelings of isolation had the most impact on mental health during COVID-19. Additionally, the average sentiment of users from r/COVID19_support has an inverse relationship with the number of new COVID-19 cases per month in the United States. Through this research, we revealed the effectiveness of support vector machines and LSTM neural networks in analyzing mental health in social media comments related to COVID-19. As the COVID-19 pandemic progresses and more data becomes available, processes like the one presented in this research can provide insight into the mental health communities that are most influenced by COVID-19 and the effects of the pandemic that cause the most mental health issues. These findings may produce valuable information for policymakers and mental health physicians. © 2022 IEEE.

Analysis Of COVID-19 Effects On Wellbeing - Study Of Reddit Posts Using Natural Language Processing Techniques

The COVID-19 pandemic continues to negatively impact people's mental health worldwide. Due to the rise in unemployment, loss of income, and lack of social interaction, people are now more likely to feel lonely, go on fewer outings, and dread the unexpected nature of viral transmission. Meanwhile, Public Health authorities are interested in monitoring people's mental and emotional well-being. In this paper, natural language processing is used to analyze human sentiments concerning the COVID-19 pandemic that has been dangerously affecting individuals' mental and physical well-being for more than two years now. Even though several waves of COVID-19 have passed, of which the first and third waves i.e., the initial pandemic period from 20th March 2020 and the rise of the Delta variant from January 2020 had the most impact on the mental health of individuals, this is further evident by the results of this paper. This research focuses on how severely this virus has affected people's mental health and emotions. After processing the data i.e., cleaning, formatting, and removing irregularities from the data, feature engineering models are applied to acquire the results. The results through VADER (valence-aware dictionary and sentiment reasoning) indicate an increase in overall negative sentiments between two mentioned periods. Additionally, the NRC-EIL (National Research Council of Canada - Emotion Intensity Lexicon) analysis showed that 'fear' and 'sadness' occurred during those times. © 2022 IEEE.

More than Two but not Right Now: Interpretations and Responses to Covid-19 Measures on Reddit for Two Communities of Consensual Non-Monogamy.

Using Owen's Thematic Analysis, we reviewed the Reddit posts of participants in two online communities regarding consensual non-monogamy (CNM) during the January 2021 peak of the Covid-19 pandemic. In 5,209 comments, 465 unique users in the /polyamory and /swinging forums on the social media platform Reddit referred to the pandemic with two themes emerging as most salient. In the first theme, participants described, interpreted, and responded to the social limitations of the Covid-19 era, with particular attention to limitations on CNM identity and behavior during the pandemic. In the second theme, participants articulated concerns about individual and social health. In addition to strictly personal concerns about physical and mental health, participants described challenges to the well-being of relationships and communities and ways to manage risk and mitigate social damage. We discuss the implication of these findings in light of the unique social structure of CNM communities.

Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia.

Background: Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging. Objective: The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit. Methods: We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19-related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country. Results: Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19-related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits. Conclusions: This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.

Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing.

BACKGROUND: Since the beginning of the COVID-19 pandemic, over 480 million people have been infected and more than 6 million people have died from COVID-19 worldwide. In some patients with acute COVID-19, symptoms manifest over a longer period, which is also called "long-COVID." Unmet medical needs related to long-COVID are high, since there are no treatments approved. Patients experiment with various medications and supplements hoping to alleviate their suffering. They often share their experiences on social media. OBJECTIVE: The aim of this study was to explore the feasibility of social media mining methods to extract important compounds from the perspective of patients. The goal is to provide an overview of different medication strategies and important agents mentioned in Reddit users' self-reports to support hypothesis generation for drug repurposing, by incorporating patients' experiences. METHODS: We used named-entity recognition to extract substances representing medications or supplements used to treat long-COVID from almost 70,000 posts on the "/r/covidlonghaulers" subreddit. We analyzed substances by frequency, co-occurrences, and network analysis to identify important substances and substance clusters. RESULTS: The named-entity recognition algorithm achieved an F1 score of 0.67. A total of 28,447 substance entities and 5789 word co-occurrence pairs were extracted. "Histamine antagonists," "famotidine," "magnesium," "vitamins," and "steroids" were the most frequently mentioned substances. Network analysis revealed three clusters of substances, indicating certain medication patterns. CONCLUSIONS: This feasibility study indicates that network analysis can be used to characterize the medication strategies discussed in social media. Comparison with existing literature shows that this approach identifies substances that are promising candidates for drug repurposing, such as antihistamines, steroids, or antidepressants. In the context of a pandemic, the proposed method could be used to support drug repurposing hypothesis development by prioritizing substances that are important to users.

Sexually Transmitted Disease-Related Reddit Posts During the COVID-19 Pandemic: Latent Dirichlet Allocation Analysis.

BACKGROUND: Sexually transmitted diseases (STDs) are common and costly, impacting approximately 1 in 5 people annually. Reddit, the sixth most used internet site in the world, is a user-generated social media discussion platform that may be useful in monitoring discussion about STD symptoms and exposure. OBJECTIVE: This study sought to define and identify patterns and insights into STD-related discussions on Reddit over the course of the COVID-19 pandemic. METHODS: We extracted posts from Reddit from March 2019 through July 2021. We used a topic modeling method, Latent Dirichlet Allocation, to identify the most common topics discussed in the Reddit posts. We then used word clouds, qualitative topic labeling, and spline regression to characterize the content and distribution of the topics observed. RESULTS: Our extraction resulted in 24,311 total posts. Latent Dirichlet Allocation topic modeling showed that with 8 topics for each time period, we achieved high coherence values (pre-COVID-19=0.41, prevaccination=0.42, and postvaccination=0.44). Although most topic categories remained the same over time, the relative proportion of topics changed and new topics emerged. Spline regression revealed that some key terms had variability in the percentage of posts that coincided with pre-COVID-19 and post-COVID-19 periods, whereas others were uniform across the study periods. CONCLUSIONS: Our study's use of Reddit is a novel way to gain insights into STD symptoms experienced, potential exposures, testing decisions, common questions, and behavior patterns (eg, during lockdown periods). For example, reduction in STD screening may result in observed negative health outcomes due to missed cases, which also impacts onward transmission. As Reddit use is anonymous, users may discuss sensitive topics with greater detail and more freely than in clinical encounters. Data from anonymous Reddit posts may be leveraged to enhance the understanding of the distribution of disease and need for targeted outreach or screening programs. This study provides evidence in favor of establishing Reddit as having feasibility and utility to enhance the understanding of sexual behaviors, STD experiences, and needed health engagement with the public.

Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study.

BACKGROUND: The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. OBJECTIVE: Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms-Reddit and Twitter-harvested from January 1, 2020, to March 1, 2022. METHODS: To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. RESULTS: Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. CONCLUSIONS: Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.

Effects of Substance Use, Recovery, and Non-Drug-Related Online Community Participation on the Risk of a Use Episode During Remission From Opioid Use Disorder: Longitudinal Observational Study.

BACKGROUND: Opioid addiction is currently one of the most pressing public health issues. Despite several treatment options for opioid addiction, the recurrence of use episodes during remission remains high. Research indicates that meaningful membership in various social groups underpins the successful transition from addiction to long-term remission. However, much of the current literature focuses on online peer-support groups for individuals in remission from substance use, sometimes also called recovery groups, a term we will use in line with the terminology used by the online community we studied. In contrast, online group memberships that promote substance use and groups that are unrelated to substance use and remission (non-drug-related groups) are rarely studied. OBJECTIVE: This study aims to understand whether engagement with a variety of Reddit subforums (subreddits) provides those in remission from opioid use disorder (OUD) with social capital, thereby reducing their risk of a use episode over several years. More specifically, it aims to examine the different effects of engagement with substance use, recovery, and non-drug-related subreddits. METHODS: A data set of 457 individuals in remission from OUD who posted their remission start date on Reddit was collected, of whom 219 (47.9%) indicated at least one use episode during the remission period. Using a Cox proportional hazards model, the effects of the number of non-drug-related, recovery, and substance use subreddits an individual had engaged with on the risk of a use episode were tested. Group engagement was assessed both in terms of the absolute number of subreddits and as a proportion of the total number of subreddits in which an individual had posted. RESULTS: Engagement with a larger number of non-drug-related online communities reduced the likelihood of a use episode irrespective of the number of posts and comments made in these forums. This was true for both the absolute number of non-drug-related communities (P<.001) and the proportion of communities with which a person engaged (P<.001). The findings were less conclusive for recovery support and substance use groups; although participating in more recovery support subreddits reduced the risk of a use episode (P<.001), being part of a higher proportion of recovery support groups relative to other subreddits increased the risk (P=.01). A higher proportion of substance use subreddits marginally increased the risk of a use episode (P=.06); however, the absolute number of substance use subreddits significantly reduced the risk of a use episode (P=.002). CONCLUSIONS: Our work indicates that even minimal regular engagement with several non-drug-related online forums may provide those in remission from OUD with an opportunity to grow their social capital and reduce the risk of a use episode over several years.

Adjusting sails for changing winds: exploring Reddit use for professional purposes in higher education.

Emerging practices of social media for professional purposes in higher education merit further attention. Reddit, a social media platform, is under-studied despite its significant presence. This study explores participation patterns on Reddit for two summer periods during 2019-2020, before and during COVID-19. We collected a total of 82,494 contributions from two subreddits, r/highereducation and r/Professors. Results show changes in contributions and interactions, with more consistent growth in r/Professors. Major topics discussed in both subreddits during summer 2020 had shifted from 2019, largely related to COVID-19. Findings are discussed with a community of practice lens, noting changes in participation and adjustment to the crisis. Additionally, we present implications for supporting and sustaining higher education professionals through Reddit during and after massive disruptions like those experienced during the COVID-19 pandemic.