The impact of coronavirus on business: Developing service research agenda for a post-coronavirus world

Purpose: The coronavirus (COVID-19) has had a tremendous impact on companies worldwide. However, researchers have no clear idea of the key issues requiring their attention. This paper aims to close this gap by analysing all business-related posts on a coronavirus subreddit ("r/coronavirus") and identifying the main research streams that are guiding the research agenda for a post-coronavirus world. Design/methodology/approach: We use data from reddit, particularly the subreddit "r/coronavirus" to identify posts that reveal the impact of coronavirus on business. Our dataset has more than 200,000 posts. We used an artificial intelligence-based algorithm to scrape the data with business-related search terms, clean it and analyse the discussion topics. Findings: We show the key topics that address the impact of coronavirus on business, combining them into four themes: essential service provision, bricolage service innovation, responsible shopping practices and market shaping amid crisis. We discuss these themes and use them to develop a service research agenda. The results are reported against the backdrop of service research priorities. Originality/value: The study identifies four key themes that have emerged from the impact of coronavirus on business and that require scholarly attention. Our findings can guide service research with unique insights provided immediately after the coronavirus outbreak to conduct research that matters to business and helps people in vulnerable positions in a post-coronavirus world. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Know an Emotion by the Company It Keeps: Word Embeddings from Reddit/Coronavirus

Social media is a crucial communication tool (e.g., with 430 million monthly active users in online forums such as Reddit), being an objective of Natural Language Processing (NLP) techniques. One of them (word embeddings) is based on the quotation, "You shall know a word by the company it keeps,” highlighting the importance of context in NLP. Meanwhile, "Context is everything in Emotion Research.” Therefore, we aimed to train a model (W2V) for generating word associations (also known as embeddings) using a popular Coronavirus Reddit forum, validate them using public evidence and apply them to the discovery of context for specific emotions previously reported as related to psychological resilience. We used Pushshiftr, quanteda, broom, wordVectors, and superheat R packages. We collected all 374,421 posts submitted by 104,351 users to Reddit/Coronavirus forum between January 2020 and July 2021. W2V identified 64 terms representing the context for seven positive emotions (gratitude, compassion, love, relief, hope, calm, and admiration) and 52 terms for seven negative emotions (anger, loneliness, boredom, fear, anxiety, confusion, sadness) all from valid experienced situations. We clustered them visually, highlighting contextual similarity. Although trained on a "small” dataset, W2V can be used for context discovery to expand on concepts such as psychological resilience.

Reddit and Google Activity Related to Non-COVID Epidemic Diseases Surged at Start of COVID-19 Pandemic: Retrospective Study.

BACKGROUND: Resources such as Google Trends and Reddit provide opportunities to gauge real-time popular interest in public health issues. Despite the potential for these publicly available and free resources to help optimize public health campaigns, use for this purpose has been limited. OBJECTIVE: The purpose of this study is to determine whether early public awareness of COVID-19 correlated with elevated public interest in other infectious diseases of public health importance. METHODS: Google Trends search data and Reddit comment data were analyzed from 2018 through 2020 for the frequency of keywords "chikungunya," "Ebola," "H1N1," "MERS," "SARS," and "Zika," 6 highly publicized epidemic diseases in recent decades. After collecting Google Trends relative popularity scores for each of these 6 terms, unpaired 2-tailed t tests were used to compare the 2020 weekly scores for each term to their average level over the 3-year study period. The number of Reddit comments per month with each of these 6 terms was collected and then adjusted for the total estimated Reddit monthly comment volume to derive a measure of relative use, analogous to the Google Trends popularity score. The relative monthly incidence of comments with each search term was then compared to the corresponding search term's pre-COVID monthly comment data, again using unpaired 2-tailed t tests. P value cutoffs for statistical significance were determined a priori with a Bonferroni correction. RESULTS: Google Trends and Reddit data both demonstrate large and statistically significant increases in the usage of each evaluated disease term through at least the initial months of the pandemic. Google searches and Reddit comments that included any of the evaluated infectious disease search terms rose significantly in the first months of 2020 above their baseline usage, peaking in March 2020. Google searches for "SARS" and "MERS" remained elevated for the entirety of the 2020 calendar year, as did Reddit comments with the words "Ebola," "H1N1," "MERS," and "SARS" (P<.001, for each weekly or monthly comparison, respectively). CONCLUSIONS: Google Trends and Reddit can readily be used to evaluate real-time general interest levels in public health-related topics, providing a tool to better time and direct public health initiatives that require a receptive target audience. The start of the COVID-19 pandemic correlated with increased public interest in other epidemic infectious diseases. We have demonstrated that for 6 distinct infectious causes of epidemics over the last 2 decades, public interest rose substantially and rapidly with the outbreak of COVID-19. Our data suggests that for at least several months after the initial outbreak, the public may have been particularly receptive to dialogue on these topics. Public health officials should consider using Google Trends and social media data to identify patterns of engagement with public health topics in real time and to optimize the timing of public health campaigns.

Understanding the Use of e-Prints on Reddit and 4chan's Politically Incorrect Board

The dissemination and reach of scientific knowledge have increased at a blistering pace. In this context, e-Print servers have played a central role by providing scientists with a rapid and open mechanism for disseminating research without waiting for the (lengthy) peer review process. While helping the scientific community in several ways, e-Print servers also provide scientific communicators and the general public with access to a wealth of knowledge without paying hefty subscription fees. This motivates us to study how e-Prints are positioned within Web community discussions. In this paper, we analyze data from two Web communities: 14 years of Reddit data and over 4 from 4chan's Politically Incorrect board. Our findings highlight the presence of e-Prints in both science-enthusiast and general-audience communities. Real-world events and distinct factors influence the e-Prints people's discussions;e.g., there was a surge of COVID-19-related research publications during the early months of the outbreak and increased references to e-Prints in online discussions. Text in e-Prints and in online discussions referencing them has a low similarity, suggesting that the latter are not exclusively talking about the findings in the former. Further, our analysis of a sample of threads highlights: 1) misinterpretation and generalization of research findings, 2) early research findings being amplified as a source for future predictions, and 3) questioning findings from a pseudoscientific e-Print. Overall, our work emphasizes the need to quickly and effectively validate non-peer-reviewed e-Prints that get substantial press/social media coverage to help mitigate wrongful interpretations of scientific outputs. © 2023 ACM.

Emojis and stock returns

Purpose: The authors explore how the sentiment expressed by emojis in comments on stocks is associated with the stocks' subsequent returns. Design/methodology/approach: By applying our own analyzer, the authors find a sentiment effect of emojis on stocks returns separately to the plain text-expressed sentiment in Reddit posts about meme stocks such as Gamestop during the Covid-19 pandemic. Findings: The authors document that a one-standard deviation change in emoji sentiment magnitude measured as the quantity of positive emoji sentiment posts over the previous hour is associated with an 0.06% (annualized: 109.2%) one-hour abnormal stock return compared to a mean of 0.03% (annualized: 54.6%). If the stock exhibits a higher intra-hour volatility, a proxy for uninformed noise trading, this relation is more pronounced and even stronger compared to stock return's relation to plain text sentiment. Research limitations/implications: The authors are not able to show causation that is open to future research. It also remains an open question how emojis impact market price efficiency. Practical implications: Emojis are positively related to stock returns in addition to plain text-expressed content if they are discussed heavily by retail investors in Internet boards such as Reddit. Social implications: Shared emotions expressed by emojis might have an influence on how disconnected individuals make homogeneous decisions. This argument might explain our found relation of emojis and stock returns. Originality/value: So, the study findings provide empirical evidence that emojis in Reddit posts convey information on future short-term stocks returns distinct from information expressed in plain text, in the case of volatile stocks, with a higher magnitude. © 2023, Emerald Publishing Limited.

Investigating Canadian Public Attitudes Toward COVID-19 Vaccine Mandates with a Nested Analysis Framework.

BACKGROUND: Social media is an important medium for studying public attitudes toward COVID-19 vaccine mandates in Canada, and Reddit network communities are a good source for this. METHODS: This study applied a "nested analysis" framework. We collected 20378 Reddit comments via the Pushshift API and developed a BERT-based binary classification model to screen for relevance to COVID-19 vaccine mandates. We then used a Guided Latent Dirichlet Allocation (LDA) model on relevant comments to extract key topics and assign each comment to its most relevant topic. RESULTS: There were 3179 (15.6%) relevant and 17199 (84.4%) irrelevant comments. Our BERT-based model achieved 91% accuracy trained with 300 Reddit comments after 60 epochs. The Guided LDA model had an optimal coherence score of 0.471 with four topics: travel, government, certification, and institutions. Human evaluation of the Guided LDA model showed an 83% accuracy in assigning samples to their topic groups. CONCLUSION: We develop a screening tool for filtering and analyzing Reddit comments on COVID-19 vaccine mandates through topic modelling. Future research could develop more effective seed word-choosing and evaluation methods to reduce the need for human judgment.

Social Media is not a Health Proxy: Differences Between Social Media and Electronic Health Record Reports of Post-COVID Symptoms

The COVID-19 pandemic transformed many aspects of health and daily life. A subset of people who were infected with the virus have ongoing chronic health issues that range in type of symptom and severity. In this study, we conducted a qualitative assessment of self-reported post-COVID symptoms from patients' electronic health records (EHR, n=564) and a randomized collection of Reddit and Twitter posts (n=500 for each). We show the inconsistencies in what types of symptoms are shared between platforms in addition to assessing the severity of the symptoms and how social media characterizations of post-COVID do not tell a complete story of this phenomenon. This research contributes to CSCW health literature by connecting digital traces of post-COVID with EHR data, critiquing the use of social media as a health proxy and points to its potential to add context to the analysis of traditional health data extracted from the EHR. © 2023 ACM.

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.

Time Series Analysis of Sentiment: A Comparison of the US and UK Coronavirus Subreddits

In this paper, we investigate the dynamics of the social media response on Reddit to the COVID-19 pandemic during its first year (February 2020-2021). The emergence of region-specific subreddits allows us to compare the reactions of individuals posting their opinions on social media about the global pandemic from two perspectives - the UK and the US.In particular, we look at the volume of posts and comments on these two subreddits, and at the sentiment expressed in these posts and comments over time as a measure of the public level of engagement and response. Whilst an analysis of volume allows us to quantify how interested people are about the pandemic as it unfolds, sentiment analysis goes beyond this and informs us about how people respond towards the pandemic based on the textual content in the posts and comments. The research looks to develop a framework for analyzing the social response on Reddit to a large-scale event in terms of the level of engagement measured through post and comment volumes, and opinion measured through an analysis of sentiment applied to the post content. In order to compare the subreddits, we show the trend in the time series through the application of moving average methods. We also show how to identify the lag between time series and align them using cross-correlation. Moreover, once aligned, we apply moving correlations to the time series to measure their degree of correspondence to see if there is a similar response to the pandemic across the two groups (UK and US). The results indicate that both subreddits were posting in high volumes at specific points during the pandemic, and that, despite the generally negative sentiment in the posts and comments, a gradual decrease in negativity leading up to the start of 2021 is observed as measures are put in place by governments and organizations to contain the virus and provide necessary support the affected populations.

Chinese Anti-Westernism on Social Media

The Chinese Communist Party and its supporters are increasingly using social media platforms to shape China's public image. This online image is a means of strengthening domestic nationalism and of projecting "soft power” abroad. This paper examines various forms of anti-Westernism that are central to this image-making. It analyzes several recent topics—the Belt and Road Initiative, climate change, the COVID-19 vaccine, the Beijing Olympics, and the conflict in Ukraine—on the r/Sino subreddit page of Reddit and compares them with two online news outlets, the South China Morning Post and China Daily. The paper focuses on how these media frame the contest between a rising China and a failing West, so creating a discourse that competes with the negative portrayals of China outside the country. The paper contrasts the aggressive strengthening of China's image against the West on social media with more sober accounts of the same topics in China's official media and in commercial news outlets. The contribution of the paper is to document an emerging online anti-Westernism that is playing an increasing role in the changing geopolitical landscape. © The Author(s) 2023.

Disruptions in the Cystic Fibrosis Community's Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments.

BACKGROUND: The COVID-19 pandemic disrupted the needs and concerns of the cystic fibrosis community. Patients with cystic fibrosis were particularly vulnerable during the pandemic due to overlapping symptoms in addition to the challenges patients with rare diseases face, such as the need for constant medical aid and limited information regarding their disease or treatments. Even before the pandemic, patients vocalized these concerns on social media platforms like Reddit and formed communities and networks to share insight and information. This data can be used as a quick and efficient source of information about the experiences and concerns of patients with cystic fibrosis in contrast to traditional survey- or clinical-based methods. OBJECTIVE: This study applies topic modeling and time series analysis to identify the disruption caused by the COVID-19 pandemic and its impact on the cystic fibrosis community's experiences and concerns. This study illustrates the utility of social media data in gaining insight into the experiences and concerns of patients with rare diseases. METHODS: We collected comments from the subreddit r/CysticFibrosis to represent the experiences and concerns of the cystic fibrosis community. The comments were preprocessed before being used to train the BERTopic model to assign each comment to a topic. The number of comments and active users for each data set was aggregated monthly per topic and then fitted with an autoregressive integrated moving average (ARIMA) model to study the trends in activity. To verify the disruption in trends during the COVID-19 pandemic, we assigned a dummy variable in the model where a value of "1" was assigned to months in 2020 and "0" otherwise and tested for its statistical significance. RESULTS: A total of 120,738 comments from 5827 users were collected from March 24, 2011, until August 31, 2022. We found 22 topics representing the cystic fibrosis community's experiences and concerns. Our time series analysis showed that for 9 topics, the COVID-19 pandemic was a statistically significant event that disrupted the trends in user activity. Of the 9 topics, only 1 showed significantly increased activity during this period, while the other 8 showed decreased activity. This mixture of increased and decreased activity for these topics indicates a shift in attention or focus on discussion topics during this period. CONCLUSIONS: There was a disruption in the experiences and concerns the cystic fibrosis community faced during the COVID-19 pandemic. By studying social media data, we were able to quickly and efficiently study the impact on the lived experiences and daily struggles of patients with cystic fibrosis. This study shows how social media data can be used as an alternative source of information to gain insight into the needs of patients with rare diseases and how external factors disrupt them.

Sharing experiences and seeking informal justice online: A grounded theory analysis of zoombombing victimization on reddit

During the COVID-19 pandemic, Zoombombing emerged as a new form of online disruption/harassment characterized by unintended and unsolicited virtual visits by both strangers and known individuals via Zoom. The current study utilizes a grounded theory-based qualitative analysis of over 1,000 posts on Reddit to explore discussions around Zoombombing victimization incidents. This paper reveals how Zoombombing victimization subreddits function as communities for sharing victimization stories, user perceptions, and support while further developing a space that promotes informal justice online. The implications include an enhanced understanding of how Zoombombing occurs and the role of online forums and cyberpolicing tools in preventing and discussing victimization. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Analysis of Social Media Impact on Stock Price Movements Using Machine Learning Anomaly Detection

The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspec-tives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors' discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models are used to try and predict the stock price movements of two meme stocks: GameStop ($GME) and AMC Entertainment ($AMC). Two sentiment metrics of the daily social media discussions about these stocks on Red-dit are generated and used together with 85 other fundamental and technical indicators as the feature set for the machine learning models. It is demonstrated that through the use of a carefully chosen mix of a meme stock's fundamental indica-tors, technical indicators, and social media sentiment scores, it is possible to predict the stocks' next-day closing prices. Also, using an anomaly detection model, and the daily Reddit discussions about a meme stock, it was possible to identify potential market manipulators. © 2023, Tech Science Press. All rights reserved.

Characterizing the Differences in Descriptions of Violence on Reddit During the COVID-19 Pandemic.

Concerns have been raised over the experiences of violence such as domestic violence (DV) and intimate partner violence (IPV) during the COVID-19 pandemic. Social media such as Reddit represent an alternative outlet for reporting experiences of violence where healthcare access has been limited. This study analyzed seven violence-related subreddits to investigate the trends of different violence patterns from January 2018 to February 2022 to enhance the health-service providers' existing service or provide some new perspective for existing violence research. Specifically, we collected violence-related texts from Reddit using keyword searching and identified six major types with supervised machine learning classifiers: DV, IPV, physical violence, sexual violence, emotional violence, and nonspecific violence or others. The increase rate (IR) of each violence type was calculated and temporally compared in five phases of the pandemic. The phases include one pre-pandemic phase (Phase 0, the date before February 26, 2020) and four pandemic phases (Phases 1-4) with separation dates of June 17, 2020, September 7, 2020, and June 4, 2021. We found that the number of IPV-related posts increased most in the earliest phase; however, that for COVID-citing IPV was highest in the mid-pandemic phase. IRs for DV, IPV, and emotional violence also showed increases across all pandemic phases, with IRs of 26.9%, 58.8%, and 28.8%, respectively, from the pre-pandemic to the first pandemic phase. In the other three pandemic phases, all the IRs for these three types of violence were positive, though lower than the IRs in the first pandemic phase. The findings highlight the importance of identifying and providing help to those who suffer from such violent experiences and support the role of social media site monitoring as a means of informative surveillance for help-providing authorities and violence research groups.

The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study.

Data: This study looks at the content on Reddit's COVID-19 community, r/Coronavirus, to capture and understand the main themes and discussions around the global pandemic, and their evolution over the first year of the pandemic. It studies 356,690 submissions (posts) and 9,413,331 comments associated with the submissions, corresponding to the period of 20th January 2020 and 31st January 2021. Methodology: On each of these datasets we carried out analysis based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics, particularly "fake" or misleading news. Results: Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provides a clear overview on the dominating topics and popular sentiments pertaining the pandemic during the first year. Conclusion: Our methodology provides an invaluable tool for governments and health decision makers and authorities to obtain a deeper understanding of the dominant public concerns and attitudes, which is vital for understanding, designing and implementing interventions for a global pandemic.

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.

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.