Analyzing Reddit Forums Specific to Abortion That Yield Diverse Dialogues Pertaining to Medical Information Seeking and Personal Worldviews: Data Mining and Natural Language Processing Comparative Study.

BACKGROUND: Attitudes toward abortion have historically been characterized via dichotomized labels, yet research suggests that these labels do not appropriately encapsulate beliefs on abortion. Rather, contexts, circumstances, and lived experiences often shape views on abortion into more nuanced and complex perspectives. Qualitative data have also been shown to underpin belief systems regarding abortion. Social media, as a form of qualitative data, could reveal how attitudes toward abortion are communicated publicly in web-based spaces. Furthermore, in some cases, social media can also be leveraged to seek health information. OBJECTIVE: This study applies natural language processing and social media mining to analyze Reddit (Reddit, Inc) forums specific to abortion, including r/Abortion (the largest subreddit about abortion) and r/AbortionDebate (a subreddit designed to discuss and debate worldviews on abortion). Our analytical pipeline intends to identify potential themes within the data and the affect from each post. METHODS: We applied a neural network-based topic modeling pipeline (BERTopic) to uncover themes in the r/Abortion (n=2151) and r/AbortionDebate (n=2815) subreddits. After deriving the optimal number of topics per subreddit using an iterative coherence score calculation, we performed a sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner to assess positive, neutral, and negative affect and an emotion analysis using the Text2Emotion lexicon to identify potential emotionality per post. Differences in affect and emotion by subreddit were compared. RESULTS: The iterative coherence score calculation revealed 10 topics for both r/Abortion (coherence=0.42) and r/AbortionDebate (coherence=0.35). Topics in the r/Abortion subreddit primarily centered on information sharing or offering a source of social support; in contrast, topics in the r/AbortionDebate subreddit centered on contextualizing shifting or evolving views on abortion across various ethical, moral, and legal domains. The average compound Valence Aware Dictionary and Sentiment Reasoner scores for the r/Abortion and r/AbortionDebate subreddits were 0.01 (SD 0.44) and -0.06 (SD 0.41), respectively. Emotionality scores were consistent across the r/Abortion and r/AbortionDebate subreddits; however, r/Abortion had a marginally higher average fear score of 0.36 (SD 0.39). CONCLUSIONS: Our findings suggest that people posting on abortion forums on Reddit are willing to share their beliefs, which manifested in diverse ways, such as sharing abortion stories including how their worldview changed, which critiques the value of dichotomized abortion identity labels, and information seeking. Notably, the style of discourse varied significantly by subreddit. r/Abortion was principally leveraged as an information and outreach source; r/AbortionDebate largely centered on debating across various legal, ethical, and moral abortion domains. Collectively, our findings suggest that abortion remains an opaque yet politically charged issue for people and that social media can be leveraged to understand views and circumstances surrounding abortion.

User Dynamics and Thematic Exploration in r/Depression During the COVID-19 Pandemic: Insights From Overlapping r/SuicideWatch Users.

BACKGROUND: In 2023, the United States experienced its highest- recorded number of suicides, exceeding 50,000 deaths. In the realm of psychiatric disorders, major depressive disorder stands out as the most common issue, affecting 15% to 17% of the population and carrying a notable suicide risk of approximately 15%. However, not everyone with depression has suicidal thoughts. While "suicidal depression" is not a clinical diagnosis, it may be observed in daily life, emphasizing the need for awareness. OBJECTIVE: This study aims to examine the dynamics, emotional tones, and topics discussed in posts within the r/Depression subreddit, with a specific focus on users who had also engaged in the r/SuicideWatch community. The objective was to use natural language processing techniques and models to better understand the complexities of depression among users with potential suicide ideation, with the goal of improving intervention and prevention strategies for suicide. METHODS: Archived posts were extracted from the r/Depression and r/SuicideWatch Reddit communities in English spanning from 2019 to 2022, resulting in a final data set of over 150,000 posts contributed by approximately 25,000 unique overlapping users. A broad and comprehensive mix of methods was conducted on these posts, including trend and survival analysis, to explore the dynamic of users in the 2 subreddits. The BERT family of models extracted features from data for sentiment and thematic analysis. RESULTS: On August 16, 2020, the post count in r/SuicideWatch surpassed that of r/Depression. The transition from r/Depression to r/SuicideWatch in 2020 was the shortest, lasting only 26 days. Sadness emerged as the most prevalent emotion among overlapping users in the r/Depression community. In addition, physical activity changes, negative self-view, and suicidal thoughts were identified as the most common depression symptoms, all showing strong positive correlations with the emotion tone of disappointment. Furthermore, the topic "struggles with depression and motivation in school and work" (12%) emerged as the most discussed topic aside from suicidal thoughts, categorizing users based on their inclination toward suicide ideation. CONCLUSIONS: Our study underscores the effectiveness of using natural language processing techniques to explore language markers and patterns associated with mental health challenges in online communities like r/Depression and r/SuicideWatch. These insights offer novel perspectives distinct from previous research. In the future, there will be potential for further refinement and optimization of machine classifications using these techniques, which could lead to more effective intervention and prevention strategies.

'That Sounds to Me Like You Are Making This Too Complicated ': Reflections on a Social Media Recruitment Effort for a Study on Masculinities and Contraception.

This reflection focuses on the methodological and ethical challenges encountered during the recruitment stage in a focus group and interview study that sought to investigate how men (co-)construct (their) masculinity in the context of contraception and family planning. We critically engage with the comments sections of our social media recruitment announcements and draw lessons in regard to (i) the 'correct' and socially acceptable terminology when trying to recruit men in an inclusive manner and (ii) the feasibility of our method more generally. We asked what we may learn about ourselves, our specific research project, and the conduct of research more generally when we view the ethical challenges arising during the recruitment stage as an integral part of reflexive research ethics.

Trends in Fentanyl Content on Reddit Substance Use Forums, 2013-2021.

BACKGROUND: Fentanyl is a pressing concern in the current drug supply. Social media data can provide access to near real-time understanding of drug trends that may complement official mortality data. DESIGN: The total number of fentanyl-related posts and the total number of posts for eight drug subreddit categories (alcohol, cannabis, hallucinogens, multi-drug, opioids, over the counter, sedatives, stimulants) were collected from 2013 to 2021 using the Pushshift Reddit dataset. The proportion of fentanyl-related posts as a fragment of total subreddit posts was examined. Linear regressions described the rate of change in post volume over time. RESULTS: Overall, fentanyl-related content increased across drug-related subreddits from 2013 to 2021 (1292% increase, linear trend p ≤ 0.001). Opioid subreddits (30.62 per 1000 posts, linear trend p ≤ 0.001) had the most fentanyl-related content during the examined time period. Multi-drug (5.95 per 1000; p ≤ 0.01), sedative (3.23 per 1000, p ≤ 0.01), and stimulant (1.60 per 1000, p ≤ 0.01) subreddits also had substantial increases in fentanyl-related content. The greatest increases occurred in the multi-drug (1067% 2013:2021) and stimulant (1862% 2014:2021) subreddits. CONCLUSION: Fentanyl-related posts on Reddit trended upward, with the fastest rate of change for multi-substance and stimulant subreddits. Beyond opioids, harm reduction and public health messaging should ensure inclusion of individuals who use other drugs.

A qualitative analysis of female Reddit users' experiences with low libido: how do women perceive their changes in sexual desire?

BACKGROUND: The true prevalence of low sexual desire among women is disputed among researchers due to the complex nature and presentation of women's sexual problems. AIM: To qualitatively analyze the aspects of libido/sexual desire frequently discussed by Reddit users and compare them with the current understanding of female sexual well-being and sexual desire disorders. METHODS: By using the Reddit application programming interface, the TwoXChromosomes subreddit was queried for posts with the keywords libido and sex drive. Posts that were deleted or unrelated to themes of libido/sex drive were excluded. A total of 85 threads-63 queried from the keyword libido and 22 from sex drive-and approximately 2900 comments were qualitatively analyzed per the grounded theory approach. Five independent researchers read and coded each thread to identify dominant themes and emergent concepts. OUTCOMES: Outcomes of interest included codes related to sexual dysfunction, libido, orgasm, masturbation, types of sex, psychology, relationships, intimacy, treatment, medications, and health care. RESULTS: Posters were primarily heterosexual women in their 20s and 30s. The code categories with the highest frequency were relationships (22.7%, n = 272), libido (22.2%, n = 210), psychological (20.2%, n = 191), medications (7.29%, n = 69), and intimacy (6.0%, n = 57). Users frequently described a decrease in libido secondary to medications, particularly antidepressants and hormonal birth control. Many users discussed the challenges of navigating a relationship with low sexual desire and the resulting sexual distress. Posters described feelings of sadness, anxiety, and guilt due to their low desire. Additionally, users discussed the role that sex plays in relationships, whether as a way to develop intimacy between partners or to achieve orgasm. Finally, posters expressed dissatisfaction with health care addressing their concerns surrounding sexual desire. CLINICAL IMPLICATIONS: The study findings-namely, the impact of medications on sexual health, the interaction of sexual desire and mental health, and cited examples of inadequate sexual health care-can help guide sexual well-being research, diagnosis, and public policy. STRENGTHS AND LIMITATIONS: Using Reddit as a data source allowed for the analysis of women's experiences outside the preestablished concepts of female sexual desire. Limitations to the study include the potential for posts to be deleted by moderator guidelines, the young demographic distribution of Reddit users, and the popularity-based structure of subreddit threads. CONCLUSION: Our results emphasize the psychosocial aspects of sexual desire and the need to redefine sexual problems to encompass the complex nature of female sexual well-being.

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.

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.

Exploring Language Used in Posts on r/birthcontrol: Case Study Using Data From Reddit Posts and Natural Language Processing to Advance Contraception Research.

BACKGROUND: Contraceptive choice is central to reproductive autonomy. The internet, including social networking sites like Reddit, is an important resource for people seeking contraceptive information and support. A subreddit dedicated to contraception, r/birthcontrol, provides a platform for people to post about contraception. OBJECTIVE: This study explored the use of r/birthcontrol, from the inception of the subreddit through the end of 2020. We describe the web-based community, identify distinctive interests and themes based upon the textual content of posts, and explore the content of posts with the most user engagement (ie, "popular" posts). METHODS: Data were obtained from the PushShift Reddit application programming interface from the establishment of r/birthcontrol to the start date of analysis (July 21, 2011, to December 31, 2020). User interactions within the subreddit were analyzed to describe community use over time, specifically the commonality of use based on the volume of posts, the length of posts (character count), and the proportion of posts with any and each flair applied. "Popular" posts on r/birthcontrol were determined based on the number of comments and "scores," or upvotes minus downvotes; popular posts had 9 comments and a score of ≥3. Term Frequency-Inverse Document Frequency (TF-IDF) analyses were run on all posts with flairs applied, posts within each flair group, and popular posts within each flair group to characterize and compare the distinctive language used in each group. RESULTS: There were 105,485 posts to r/birthcontrol during the study period, with the volume of posts increasing over time. Within the time frame for which flairs were available on r/birthcontrol (after February 4, 2016), users applied flairs to 78% (n=73,426) of posts. Most posts contained exclusively textual content (n=66,071, 96%), had comments (n=59,189, 86%), and had a score (n=66,071, 96%). Posts averaged 731 characters in length (median 555). "SideEffects!?" was the most frequently used flair overall (n=27,530, 40%), while "Experience" (n=719, 31%) and "SideEffects!?" (n=672, 29%) were most common among popular posts. TF-IDF analyses of all posts showed interest in contraceptive methods, menstrual experiences, timing, feelings, and unprotected sex. While TF-IDF results for posts with each flair varied, the contraceptive pill, menstrual experiences, and timing were discussed across flair groups. Among popular posts, intrauterine devices and contraceptive use experiences were often discussed. CONCLUSIONS: People commonly wrote about contraceptive side effects and experiences using methods, highlighting the value of r/birthcontrol as a space to post about aspects of contraceptive use that are not well addressed by clinical contraceptive counseling. The value of real-time, open-access data on contraceptive users' interests is especially high given the shifting landscape of and increasing constraints on reproductive health care in the United States.

Users' Concerns About Endometriosis on Social Media: Sentiment Analysis and Topic Modeling Study.

BACKGROUND: Endometriosis is a debilitating and difficult-to-diagnose gynecological disease. Owing to limited information and awareness, women often rely on social media platforms as a support system to engage in discussions regarding their disease-related concerns. OBJECTIVE: This study aimed to apply computational techniques to social media posts to identify discussion topics about endometriosis and to identify themes that require more attention from health care professionals and researchers. We also aimed to explore whether, amid the challenging nature of the disease, there are themes within the endometriosis community that gather posts with positive sentiments. METHODS: We retrospectively extracted posts from the subreddits r/Endo and r/endometriosis from January 2011 to April 2022. We analyzed 45,693 Reddit posts using sentiment analysis and topic modeling-based methods in machine learning. RESULTS: Since 2011, the number of posts and comments has increased steadily. The posts were categorized into 11 categories, and the highest number of posts were related to either asking for information (Question); sharing the experiences (Rant/Vent); or diagnosing and treating endometriosis, especially surgery (Surgery related). Sentiment analysis revealed that 92.09% (42,077/45,693) of posts were associated with negative sentiments, only 2.3% (1053/45,693) expressed positive feelings, and there were no categories with more positive than negative posts. Topic modeling revealed 27 major topics, and the most popular topics were Surgery, Questions/Advice, Diagnosis, and Pain. The Survey/Research topic, which brought together most research-related posts, was the last in terms of posts. CONCLUSIONS: Our study shows that posts on social media platforms can provide insights into the concerns of women with endometriosis symptoms. The analysis of the posts confirmed that women with endometriosis have to face negative emotions and pain daily. The large number of posts related to asking questions shows that women do not receive sufficient information from physicians and need community support to cope with the disease. Health care professionals should pay more attention to the symptoms and diagnosis of endometriosis, discuss these topics with patients to reduce their dissatisfaction with doctors, and contribute more to the overall well-being of women with endometriosis. Researchers should also become more involved in social media and share new science-based knowledge regarding endometriosis.

Investigating Substance Use via Reddit: Systematic Scoping Review.

BACKGROUND: Reddit's (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research. OBJECTIVE: This review aims to investigate the use of Reddit for studying substance use by examining previous studies' objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention. METHODS: A total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions. RESULTS: The use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform's anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform's low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources. CONCLUSIONS: This systematic scoping review provides an overview of Reddit's use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research.

Investigating COVID-19's Impact on Mental Health: Trend and Thematic Analysis of Reddit Users' Discourse.

BACKGROUND: The COVID-19 pandemic has resulted in heightened levels of depression, anxiety, and other mental health issues due to sudden changes in daily life, such as economic stress, social isolation, and educational irregularity. Accurately assessing emotional and behavioral changes in response to the pandemic can be challenging, but it is essential to understand the evolving emotions, themes, and discussions surrounding the impact of COVID-19 on mental health. OBJECTIVE: This study aims to understand the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression and r/Anxiety) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods. METHODS: This study used data from the r/Depression and r/Anxiety Reddit communities, which consisted of posts contributed by 351,409 distinct users over a period spanning from 2019 to 2022. Topic modeling and Word2Vec embedding models were used to identify key terms associated with the targeted themes within the data set. A range of trend and thematic analysis techniques, including time-to-event analysis, heat map analysis, factor analysis, regression analysis, and k-means clustering analysis, were used to analyze the data. RESULTS: The time-to-event analysis revealed that the first 28 days following a major event could be considered a critical window for mental health concerns to become more prominent. The theme trend analysis revealed key themes such as economic stress, social stress, suicide, and substance use, with varying trends and impacts in each community. The factor analysis highlighted pandemic-related stress, economic concerns, and social factors as primary themes during the analyzed period. Regression analysis showed that economic stress consistently demonstrated the strongest association with the suicide theme, whereas the substance theme had a notable association in both data sets. Finally, the k-means clustering analysis showed that in r/Depression, the number of posts related to the "depression, anxiety, and medication" cluster decreased after 2020, whereas the "social relationships and friendship" cluster showed a steady decrease. In r/Anxiety, the "general anxiety and feelings of unease" cluster peaked in April 2020 and remained high, whereas the "physical symptoms of anxiety" cluster showed a slight increase. CONCLUSIONS: This study sheds light on the impact of COVID-19 on mental health and the related themes discussed in 2 web-based communities during the pandemic. The results offer valuable insights for developing targeted interventions and policies to support individuals and communities in similar crises.

Scoping the Priorities and Concerns of Parents: Infodemiology Study of Posts on Mumsnet and Reddit.

BACKGROUND: Health technology innovation is increasingly supported by a bottom-up approach to priority setting, aiming to better reflect the concerns of its intended beneficiaries. Web-based forums provide parents with an outlet to share concerns, advice, and information related to parenting and the health and well-being of their children. They provide a rich source of data on parenting concerns and priorities that could inform future child health research and innovation. OBJECTIVE: The aim of the study is to identify common concerns expressed on 2 major web-based forums and cluster these to identify potential family health concern topics as indicative priority areas for future research and innovation. METHODS: We text-mined the r/Parenting subreddit (69,846 posts) and the parenting section of Mumsnet (99,848 posts) to create a large corpus of posts. A generative statistical model (latent Dirichlet allocation) was used to identify the most discussed topics in the corpus, and content analysis was applied to identify the parenting concerns found in a subset of posts. RESULTS: A model with 25 topics produced the highest coherence and a wide range of meaningful parenting concern topics. The most frequently expressed parenting concerns are related to their child's sleep, self-care, eating (and food), behavior, childcare context, and the parental context including parental conflict. Topics directly associated with infants, such as potty training and bottle feeding, were more common on Mumsnet, while parental context and screen time were more common on r/Parenting. CONCLUSIONS: Latent Dirichlet allocation topic modeling can be applied to gain a rapid, yet meaningful overview of parent concerns expressed on a large and diverse set of social media posts and used to complement traditional insight gathering methods. Parents framed their concerns in terms of children's everyday health concerns, generating topics that overlap significantly with established family health concern topics. We provide evidence of the range of family health concerns found at these sources and hope this can be used to generate material for use alongside traditional insight gathering methods.

Web-Based Social Networks of Individuals With Adverse Childhood Experiences: Quantitative Study.

BACKGROUND: Adverse childhood experiences (ACEs), which include abuse and neglect and various household challenges such as exposure to intimate partner violence and substance use in the home, can have negative impacts on the lifelong health of affected individuals. Among various strategies for mitigating the adverse effects of ACEs is to enhance connectedness and social support for those who have experienced them. However, how the social networks of those who experienced ACEs differ from the social networks of those who did not is poorly understood. OBJECTIVE: In this study, we used Reddit and Twitter data to investigate and compare social networks between individuals with and without ACE exposure. METHODS: We first used a neural network classifier to identify the presence or absence of public ACE disclosures in social media posts. We then analyzed egocentric social networks comparing individuals with self-reported ACEs with those with no reported history. RESULTS: We found that, although individuals reporting ACEs had fewer total followers in web-based social networks, they had higher reciprocity in following behavior (ie, mutual following with other users), a higher tendency to follow and be followed by other individuals with ACEs, and a higher tendency to follow back individuals with ACEs rather than individuals without ACEs. CONCLUSIONS: These results imply that individuals with ACEs may try to actively connect with others who have similar previous traumatic experiences as a positive connection and coping strategy. Supportive interpersonal connections on the web for individuals with ACEs appear to be a prevalent behavior and may be a way to enhance social connectedness and resilience in those who have experienced ACEs.

Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.

BACKGROUND: While scientific knowledge of post-COVID-19 condition (PCC) is growing, there remains significant uncertainty in the definition of the disease, its expected clinical course, and its impact on daily functioning. Social media platforms can generate valuable insights into patient-reported health outcomes as the content is produced at high resolution by patients and caregivers, representing experiences that may be unavailable to most clinicians. OBJECTIVE: In this study, we aimed to determine the validity and effectiveness of advanced natural language processing approaches built to derive insight into PCC-related patient-reported health outcomes from social media platforms Twitter and Reddit. We extracted PCC-related terms, including symptoms and conditions, and measured their occurrence frequency. We compared the outputs with human annotations and clinical outcomes and tracked symptom and condition term occurrences over time and locations to explore the pipeline's potential as a surveillance tool. METHODS: We used bidirectional encoder representations from transformers (BERT) models to extract and normalize PCC symptom and condition terms from English posts on Twitter and Reddit. We compared 2 named entity recognition models and implemented a 2-step normalization task to map extracted terms to unique concepts in standardized terminology. The normalization steps were done using a semantic search approach with BERT biencoders. We evaluated the effectiveness of BERT models in extracting the terms using a human-annotated corpus and a proximity-based score. We also compared the validity and reliability of the extracted and normalized terms to a web-based survey with more than 3000 participants from several countries. RESULTS: UmlsBERT-Clinical had the highest accuracy in predicting entities closest to those extracted by human annotators. Based on our findings, the top 3 most commonly occurring groups of PCC symptom and condition terms were systemic (such as fatigue), neuropsychiatric (such as anxiety and brain fog), and respiratory (such as shortness of breath). In addition, we also found novel symptom and condition terms that had not been categorized in previous studies, such as infection and pain. Regarding the co-occurring symptoms, the pair of fatigue and headaches was among the most co-occurring term pairs across both platforms. Based on the temporal analysis, the neuropsychiatric terms were the most prevalent, followed by the systemic category, on both social media platforms. Our spatial analysis concluded that 42% (10,938/26,247) of the analyzed terms included location information, with the majority coming from the United States, United Kingdom, and Canada. CONCLUSIONS: The outcome of our social media-derived pipeline is comparable with the results of peer-reviewed articles relevant to PCC symptoms. Overall, this study provides unique insights into patient-reported health outcomes of PCC and valuable information about the patient's journey that can help health care providers anticipate future needs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2022.12.14.22283419.

Kava (Piper methysticum) in the United States: the quiet rise of a substance with often subtle effects.

Background: Piper methysticum, commonly called kava, has long been consumed in beverage form in the Pacific Islands. Kava use in the US has slowly increased since the 1990s, but is not assessed in major epidemiological surveys.Objectives: To analyze social-media posts about kava from current, past, and prospective users, for motivations, patterns of co-use, and effects.Methods: Text from Reddit posts, and accompanying metadata, were collected and thematically coded by two independent raters.Results: 423 posts were collected, spanning January 2006 through December 2021. Of the 1,211 thematic codes applied, 1,098 (90. 7%) were concordant. Motivations for use bifurcated into self-treatment (for psychiatric or physical health conditions) and recreation; these were not mutually exclusive. Kava was rarely considered strongly euphoriant, but was valued as an anxiolytic. Kava was frequently used with other substances, most commonly kratom. Kava was used at lower doses for self-treatment than for other purposes (pseudo-R2 = 0.11). Undesirable effects (gastrointestinal upset, fatigue) were mentioned, though less often than benefits. Hepatotoxicity, reported elsewhere as a rare, non-dose-related risk, was disputed on the basis of its not having been experienced by those posting.Conclusion: Kava appears to be conceptualized among Reddit posters as an anxiolytic with few risks or adverse effects. As it grows in popularity, especially among people who use other drugs that are more liable to misuse or addiction, it should be assessed in probability samples (i.e. in the major national drug surveys) and clinical practice for its risks, potential benefits, and possible drug-drug interactions.

Using Social Media to Examine Themes Surrounding Fentanyl Misuse and Risk Indicators.

Background: Opioid misuse is a crisis in the United States, and synthetic opioids such as fentanyl pose risks for overdose and mortality. Individuals who misuse substances commonly seek information and support online due to stigma and legal concerns, and this online networking may provide insight for substance misuse prevention and treatment. We aimed to characterize topics in substance-misuse related discourse among members of an online fentanyl community. Method: We investigated posts on a fentanyl-specific forum on the platform Reddit to identify emergent substance misuse-related themes potentially indicative of heightened risk for overdose and other adverse health outcomes. We analyzed 27 posts and 338 comments with a qualitative codebook established using a subset of user posts via inductive and deductive methods. Posts and comments were independently reviewed by two coders with a third coder resolving discrepancies. The top 200 subreddits with the most activity by r/fentanyl members were also inductively analyzed to understand interests of r/fentanyl users. Results: Functional/quality of life impairments due to substance misuse (29%) was the most commonly occurring theme, followed by polysubstance use (27%) and tolerance/dependence/withdrawal (20%). Additional themes included drug identification with photos, substances cut with other drugs, injection drugs, and past overdoses. Media-focused subreddits and other drug focused communities were among the communities most often followed by r/fentanyl users. Conclusion: Themes closely align with DSM-V substance use disorder symptoms for fentanyl and other substances. High involvement in media-focused subreddits and other substance-misuse-related communities suggests digital platforms as acceptable for overdose prevention and recovery support interventions.

"COVID-19 spreads round the planet, and so do paranoid thoughts". A qualitative investigation into personal experiences of psychosis during the COVID-19 pandemic.

The COVID-19 pandemic is likely to affect people who have had previous experiences of psychosis - either positively or negatively. A research gap exists in looking at qualitative experiences of the pandemic. In the present study, we address the research gap in those who self-identified as having psychosis via Reddit discussion forum posts, collecting data from a popular online community. Sixty-five posts were analysed using inductive thematic analysis. Five overarching themes were identifie; declining mental health, changed psychosis experiences, personal coping experiences, social connectedness and disconnectedness, and COVID-19 as a metaphor. The data show that there are varied experiences associated with the pandemic. People who have experiences of psychosis do not only have vulnerabilities but may also perceive themselves as having strengths that allow them to cope better.

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.

Reddit on PrEP: Posts About Pre-exposure Prophylaxis for HIV from Reddit Users, 2014-2019.

Social media forums provide a window into how gay, bisexual, and other men who have sex with men talk about pre-exposure prophylaxis for HIV prevention (PrEP) outside of research contexts. To examine information exchange about this important pillar of HIV prevention, discussions around PrEP were collected from the r/askgaybros subreddit of the social media site Reddit (2014-2019). Post titles and asks were qualitatively coded to identify themes describing the primary purpose of the post. In all, 1163 PrEP posts were identified, and a 23.3-fold increase in post volume was seen from 2014 (n = 20) to 2019 (n = 466). The most common post type was a mention of PrEP in a post primarily discussing: an assessment of risk after a sexual encounter (19.2%); a sexual or romantic relationship (6.0%); or other (29.3%). Access challenges (19.1%), information seeking (17.5%), and the cultural effects of PrEP on the gay community (16.3%) were other common themes. Posts regarding the initiation of PrEP (11.8%) and PrEP side-effects (9.4%) were moderately represented. Posts addressing promotion, shade, stigma, and usage were infrequent (≤ 5.5% of posts, respectively). Over time, discussion of PrEP has increased exponentially on r/askgaybros, which may reflect the normalization of PrEP. Qualitative analysis of these posts can be a rich source of data for scientists, practitioners, and healthcare providers interested in increasing uptake of PrEP and decreasing barriers to its use.

COVID-19 vaccine perceptions in the initial phases of US vaccine roll-out: an observational study on reddit.

BACKGROUND: Open online forums like Reddit provide an opportunity to quantitatively examine COVID-19 vaccine perceptions early in the vaccine timeline. We examine COVID-19 misinformation on Reddit following vaccine scientific announcements, in the initial phases of the vaccine timeline. METHODS: We collected all posts on Reddit (reddit.com) from January 1 2020 - December 14 2020 (n=266,840) that contained both COVID-19 and vaccine-related keywords. We used topic modeling to understand changes in word prevalence within topics after the release of vaccine trial data. Social network analysis was also conducted to determine the relationship between Reddit communities (subreddits) that shared COVID-19 vaccine posts, and the movement of posts between subreddits. RESULTS: There was an association between a Pfizer press release reporting 90% efficacy and increased discussion on vaccine misinformation. We observed an association between Johnson and Johnson temporarily halting its vaccine trials and reduced misinformation. We found that information skeptical of vaccination was first posted in a subreddit (r/Coronavirus) which favored accurate information and then reposted in subreddits associated with antivaccine beliefs and conspiracy theories (e.g. conspiracy, NoNewNormal). CONCLUSIONS: Our findings can inform the development of interventions where individuals determine the accuracy of vaccine information, and communications campaigns to improve COVID-19 vaccine perceptions, early in the vaccine timeline. Such efforts can increase individual- and population-level awareness of accurate and scientifically sound information regarding vaccines and thereby improve attitudes about vaccines, especially in the early phases of vaccine roll-out. Further research is needed to understand how social media can contribute to COVID-19 vaccination services.