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BACKGROUND: Video games have rapidly become mainstream in recent decades, with over half of the US population involved in some form of digital gaming. However, concerns regarding the potential harms of excessive, disordered gaming have also risen. Internet gaming disorder (IGD) has been proposed as a tentative psychiatric disorder that requires further study by the American Psychological Association (APA) and is recognized as a behavioral addiction by the World Health Organization. Substance use among gamers has also become a concern, with caffeinated or energy drinks and prescription stimulants commonly used for performance enhancement. OBJECTIVE: This study aimed to identify substance use patterns and health-related concerns among gamers among a population of Reddit users. METHODS: We used the public streaming Reddit application programming interface to collect and analyze all posts from the popular subreddit, r/StopGaming. From this corpus of posts, we filtered the dataset for keywords associated with common substances that may be used to enhance gaming performance. We then applied an inductive coding approach to characterize substance use behaviors, gaming genres, and physical and mental health concerns. Potential disordered gaming behavior was also identified using the tentative IGD guidelines proposed by the APA. A chi-square test of independence was used to assess the association between gaming disorder and substance use characteristics, and multivariable logistic regression was used to analyze whether mental health discussion or the mention of any substance with sufficient sample size was significantly associated with IGD. RESULTS: In total, 10,551 posts were collected from Reddit from June 2017 to December 2022. After filtering the dataset for substance-related keywords, 1057 were included for further analysis, of which 286 mentioned both gaming and the use of ≥1 substances. Among the 286 posts that discussed both gaming and substance use, the most mentioned substances were alcohol (n=132), cannabis (n=104), and nicotine (n=48), while the most mentioned genres were role-playing games (n=120), shooters (n=90), and multiplayer online battle arenas (n=43). Self-reported behavior that aligned with the tentative guidelines for IGD was identified in 66.8% (191/286) posts. More than half, 62.9% (180/286) of the posts, discussed a health issue, with the majority (n=144) cited mental health concerns. Common mental health concerns discussed were depression and anxiety. There was a significant association between IGD and substance use (P

Introduction: Medical and nonmedical ketamine use is increasing in the United States. This will likely lead to an increase in emergency department (ED) visits in individuals experiencing associated side effects. Physicians will need to be able to effectively recognize and manage ketamine-related complications.Case Report: A 31-year-old male with a three-year history of inhalational, intramuscular, and intravenous nonmedical ketamine use presented to the ED twice within a week with symptoms of severe atraumatic back pain, abdominal pain, and dyspepsia. A comprehensive workup, including advanced imaging, was unrevealing for identifiable causes, and the patient was discharged with instructions for primary care follow-up for further evaluation. The patient used information shared on Reddit, an online forum and social network, to identify that the cause of his pain was related to chronic ketamine use. Subsequently, upon discontinuation of ketamine, the pain improved in 24 hours. The patient self-navigated to addiction treatment.Conclusion: Emergency physicians should consider sequelae of chronic ketamine use as a possible cause for gastrointestinal and urologic symptoms in the ED. In addition to thorough examination and assessment for other acute medical problems, patients should be offered education, symptomatic treatment, and linkage to harm reduction and substance use disorder treatment services.

BackgroundThe growing availability of big data spontaneously generated by social media platforms allows us to leverage natural language processing (NLP) methods as valuable tools to understand the opioid crisis.ObjectiveWe aimed to understand how NLP has been applied to Reddit (Reddit Inc) data to study opioid use.MethodsWe systematically searched for peer-reviewed studies and conference abstracts in PubMed, Scopus, PsycINFO, ACL Anthology, IEEE Xplore, and Association for Computing Machinery data repositories up to July 19, 2022. Inclusion criteria were studies investigating opioid use, using NLP techniques to analyze the textual corpora, and using Reddit as the social media data source. We were specifically interested in mapping studies' overarching goals and findings, methodologies and software used, and main limitations.ResultsIn total, 30 studies were included, which were classified into 4 nonmutually exclusive overarching goal categories: methodological (n=6, 20% studies), infodemiology (n=22, 73% studies), infoveillance (n=7, 23% studies), and pharmacovigilance (n=3, 10% studies). NLP methods were used to identify content relevant to opioid use among vast quantities of textual data, to establish potential relationships between opioid use patterns or profiles and contextual factors or comorbidities, and to anticipate individuals' transitions between different opioid-related subreddits, likely revealing progression through opioid use stages. Most studies used an embedding technique (12/30, 40%), prediction or classification approach (12/30, 40%), topic modeling (9/30, 30%), and sentiment analysis (6/30, 20%). The most frequently used programming languages were Python (20/30, 67%) and R (2/30, 7%). Among the studies that reported limitations (20/30, 67%), the most cited was the uncertainty regarding whether redditors participating in these forums were representative of people who use opioids (8/20, 40%). The papers were very recent (28/30, 93%), from 2019 to 2022, with authors from a range of disciplines.ConclusionsThis scoping review identified a wide variety of NLP techniques and applications used to support surveillance and social media interventions addressing the opioid crisis. Despite the clear potential of these methods to enable the identification of opioid-relevant content in Reddit and its analysis, there are limits to the degree of interpretive meaning that they can provide. Moreover, we identified the need for standardized ethical guidelines to govern the use of Reddit data to safeguard the anonymity and privacy of people using these forums.

Alzheimers disease and related dementias (ADRD) are a spectrum of disorders characterized by cognitive decline, which pose significant challenges for both affected individuals and their caregivers. Previous literature has focused on patient family surveys which do not always capture the breadth of authentic experiences of the caregiver. Online social media platforms provide a space for individuals to share their experiences and obtain advice toward caring for those with ADRD. This study leverages Reddit, a platform frequented by caregivers seeking advice for caring for a family member with advice for ADRD. To identify the topics of discussion or advice that most caregivers seek and sought after, we employed structured topic modeling techniques such as BERTopic to analyze the content of these posts and use an intertopic distance map to discern the variation in themes across different Reddit categories. In addition, we analyze the sentiment of the Reddit postings using Valence Aware Dictionary and Sentiment Reasoner to deduce the degree of negative, positive, and neutral sentiment of the discussion posts. Our findings reveal that the topics that caregivers most frequently discuss and seek advice for were related to caregiver stories, community support, and concerns ADRD. Specifically, we aimed to reproduce an organic Reddit search of caregiving of abuse on family member, financial struggles, symptoms of hallucinations, and repetition in ADRD family members. These results underscore the importance of online communities for gaining a comprehensive understanding of the multifaceted experiences and challenges faced by ADRD caregivers.

Objective Methods Results Significance This study was undertaken to explore the experiences and concerns of people living with epilepsy by analyzing discussions in an online epilepsy community, using large language models (LLMs) to identify themes, demographic patterns, and associations with emotional distress, substance use, and suicidal ideation.We analyzed 56 970 posts and responses to them from 21 906 users on the epilepsy forum (subreddit) of Reddit and 768 504 posts from the same users in other subreddits, between 2010 and 2023. LLMs, validated against human labeling, were used to identify 23 recurring themes, assess demographic differences, and examine cross‐posting to depression‐ and suicide‐related subreddits. Hazard ratios (HRs) were calculated to assess the association between specific themes and activity in mental health forums.Prominent topics included seizure descriptions, medication management, stigma, drug and alcohol use, and emotional well‐being. The posts on topics less likely to be discussed in clinical settings had the highest engagement. Younger users focused on stigma and emotional issues, whereas older users discussed medical treatments. Posts about emotional distress (HR = 1.3), postictal state (HR = 1.4), surgical treatment (HR = .7), and work challenges (HR = 1.6) predicted activity in a subreddit associated with suicidal ideation, whereas emotional distress (HR = 1.5), surgical treatment (HR = .6), and stigma (HR = 1.3) predicted activity in the depression subreddit. Substance use discussions showed a temporal pattern of association with seizure descriptions, implying possible opportunities for intervention.LLM analysis of online epilepsy communities provides novel insights into patient concerns often overlooked in clinical settings. These findings may improve patient–provider communication, inform personalized interventions, and support the development of patient‐reported outcome measures. Additionally, hazard models can help identify at‐risk individuals, offering opportunities for early mental health interventions. [ABSTRACT FROM AUTHOR]

Use machine learning to characterize the content of endometriosis online community posts and comments. Retrospective Descriptive Study. Endometriosis online health communities (OHCs) on the platform Reddit. Users of the endometriosis OHCs r/Endo and r/endometriosis. Machine learning was used to analyze thousands of posts made to endometriosis OHCs. Content of posts and comments was interpreted using topic modeling, persona identification, and intent labeling. Measurements included baseline characteristics of users, posts, and comments to the OHCs. Machine-learning techniques; topic modeling, intent labeling, and persona identification were used to identify the most common topics of conversation, the intents behind the posts, and the subjects of people discussed in posts. System performance was assessed via accuracy at F 1 -score. A total of 34 715 posts and 353 162 comments responding to posts were evaluated. The topics most likely to be a subject of a post were menstruation (8%), sharing symptoms (8%), medical appointments (8%), medical story (9%), and empathy (7%). The majority of posts were written with the intent of seeking information about endometriosis (49%) or seeking the experiences of others with endometriosis (29%). Users expressed a strong preference for surgeons performing excision rather than ablation of endometriosis. Endometriosis OHCs are mostly used to learn about symptoms of endometriosis and share one's medical experiences. Posts and comments from users highlight the need for more empathy in the clinical care of endometriosis and easier access for patients to high-quality information about endometriosis. [ABSTRACT FROM AUTHOR]

Abstract\nPlain language summaryWith drastic changes to abortion policy, the months following the Dobbs leak and subsequent decision in 2022 were a uniquely uncertain and difficult time for abortion access in the US. To understand experiences of challenges to abortion access during that time, we used a hybrid inductive and deductive thematic coding approach to analyse descriptions of barriers and their impacts shared in an abortion subreddit (r/abortion). A simple random sample of 10% of posts was obtained from those shared from 02 May 2022 through 23 December 2022; comments were purposively sampled during the coding process. In this sample of submissions (n = 523 posts, 88 comments), people described structural barriers identified in past research, including state abortion bans and gestational limits, high costs, limited appointment availability, and long travel required. Posters also commonly described known social barriers, including limited social support and abortion stigma. Several impactful barriers not well-described in past research emerged inductively, including wait time for receiving mail-ordered abortion medication, low credibility of online ordering platforms, and concerns about legal risks of accessing abortion or related medical care. The most common consequences of experiencing barriers were adverse mental health outcomes, delayed access to care, and being compelled to self-manage their abortion because of access barriers. This analysis provides timely insights into the experiences and impacts of abortion access barriers in a group of people with a range of engagement with clinical abortion care, lived experiences, and points in their abortion processes, with public health implications for mental health and abortion access.The 2022 Dobbs decision and overturn of Roe v Wade significantly changed abortion policy in the US. Following this, many people faced new and severe challenges to accessing abortion. We analysed 523 posts and 88 comments from an abortion support subreddit (r/abortion) from May 2022 to December 2022. We used a qualitative analysis approach to identify themes in discussions of barriers and their impacts. We found that common barriers included state bans, high abortion costs, limited appointment availability, and long travel distances. Additionally, people faced social challenges like lack of support and stigma around abortion. New, less-researched issues were also noted. These included 1) delays with mail-ordered medications 2) concerns about the reliability of online services; and 3) fears about legal risks related to abortion. Frequent consequences of these barriers were poor mental health, and delays in getting care. People also needed to manage abortions themselves because of access issues. This study highlights the urgent need to address these barriers and their impact on mental health and abortion access. [ABSTRACT FROM AUTHOR]

Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social stigma associated with suicide presents a challenge to suicidal ideation detection, as existing methods require patients to explicitly communicate their suicidal propensities. In contrast, progressively more at-risk people choose online platforms—such as Reddit—as their preferred avenues for sharing their suicidal experiences and seeking emotional support. As a result, these online platforms have become an unobtrusive source of user-generated textual data that can be used to detect suicidality with supervised machine learning and natural language processing techniques. In this paper, we proposed a suicidal ideation detection approach that combines textual and psycholinguistic features extracted from the Reddit forum. Subsequently, we selected the most informative features using the Boruta algorithm and employed four classifiers: logistic regression, naïve Bayes, support vector machines, and random forest. The naïve Bayes models trained with the combination of term frequency-inverse document frequency (TF-IDF) and National Research Council (NRC) features demonstrated the highest performance, obtaining a F1 score of 70.99%. Our experimental results illustrate that a combination of textual and psycholinguistic features yields better classification performance compared to using those features separately. [ABSTRACT FROM AUTHOR]

AbstractThe months following the Dobbs v. Jackson Women’s Health Organization leak and subsequent decision in 2022, which removed federal legal protections of abortion, presented a challenging period for abortion in the United States and a time when sources of and experiences with social support likely changed rapidly. We used thematic analysis of randomly selected relevant posts from an abortion subreddit (r/abortion) during 2022 following the Dobbs leak (n = 503) to identify forms of interpersonal abortion social support accessed, support gaps, and support sought and shared in r/abortion. Findings reflect people’s needs for more and different support, particularly those using medication abortion. [ABSTRACT FROM AUTHOR]

The superhero film genre has both hypersexualized women and offered potentially empowering social and cultural narratives. Drawing on feminist media studies and critical discourse analysis, this study analyzes themes in online Reddit discussions surrounding female superheroes in Black Widow to better understand how conversations about media representations maintain and subvert dominant gendered discourses. Throughout her portrayal in multiple movies in the Marvel Cinematic Universe, Black Widow's transformation highlights the shift from a hypersexualized female superhero to a complex character that breaks away from gendered stereotypes. Mirroring an incrementalist approach to social change, Redditors discursively constructed and invoked "realism" as a heuristic for their evaluations of diversified media representations. First, Reddit users centered White, Western women's experiences in their posts about the film, confronting the trope that women exist as objects to be saved and sexualized by men. However, as they assumed that White, Western women's struggles are universal, they ignored the intersectional relationship to oppression. Second, Redditors engaged in the male gaze to understand, perpetuate, and disrupt Black Widow's historic sexualization. The study supports the claims that gender is performative and seemingly mundane conversations can function politically. We conclude with implications for theorizing future post-feminist discourse and methodological implications for researchers using Reddit data. [ABSTRACT FROM AUTHOR]

Through 19 semistructured interviews, we explore why people join and participate in "alternative health" Facebook groups and Reddit subs. Participation in these groups creates an ontological circle where people's feelings of fear, desperation, and distrust in the systems and actors that comprise our government, health, and news systems inspire alternative information and support-seeking in these groups and subs. While participation assuages those feelings and stabilizes people's sense of ontological security, it also destabilizes it, reinforcing and legitimizing feelings of fear, desperation, and distrust. This circle contributes to favorable receptive conditions for spreading unproven and dangerous health misinformation. We argue that it will be difficult to address misinformation (1) without considering and rectifying people's often valid reasons for feelings of fear, desperation, distrust, and their desires for information and (2) without considering the relationships between alternative health social media groups and subs and the (de)stabilization of ontological security. [ABSTRACT FROM AUTHOR]

Social chatbot (SC) applications offering social companionship and basic therapy tools have grown in popularity for emotional, social, and psychological support. While use appears to offer mental health benefits, few studies unpack the potential for harms. Our grounded theory study analyzes mental health experiences with the popular SC application Replika. We identified mental health relevant posts made in the r/Replika Reddit community between 2017 and 2021 (n = 582). We find evidence of harms, facilitated via emotional dependence on Replika that resembles patterns seen in human–human relationships. Unlike other forms of technology dependency, this dependency is marked by role-taking, whereby users felt that Replika had its own needs and emotions to which the user must attend. While prior research suggests human–chatbot and human–human interactions may not resemble each other, we identify social and technological factors that promote parallels and suggest ways to balance the benefits and risks of SCs. [ABSTRACT FROM AUTHOR]

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Purpose: Social media is increasingly vital for educators to extend their professional networks and engage in meaningful conversations. This study aims to explore how Reddit facilitates these interactions as a platform for generative dialogical exchange. For English educators, generative dialogue is paramount for professional development and classroom practice. Design/methodology/approach: To better understand this dynamic, we outline Hermans and Hermans-Konopka's (2010) framework of generative dialogical exchange, review relevant literature related to the implications of social media use by educators and argue for the importance of Reddit as a site of exploration into dialogical exchange. After describing our artifact selection, we offer relevant data and conclude with understanding and implications. Findings: We argue that although online educator communities, particularly those on Reddit, offer opportunities for broad dialogical exchange for English educators, these can be marked by a push and pull between the expansion of the dialogical space and a tendency to rein it back in through reinforcing traditional power dynamics. Originality/value: Reddit is an understudied site for extending professional networks and fostering conversations among educators, providing a novel approach to understanding these dynamics through dialogical theory. [ABSTRACT FROM AUTHOR]

Language provides significant insights into an individual's emotional state, social status, and personality traits. This research aims to enhance depression detection through the analysis of linguistic features and various dataset attributes. The dataset, sourced from the social networking platform Reddit, comprises posts and comments from individuals diagnosed with depression. Logistic regression with term frequency-inverse document frequency (TFIDF) is employed as the primary model for text classification. To improve model performance, a novel feature--the average time interval between consecutive posts or comments--is introduced, contributing to a marginal but noteworthy improvement in accuracy. The proposed model demonstrates superior F1 scores compared to other models applied to the same dataset. Given the increasing recognition of mental health's significance, accurately diagnosing mental disorders is of paramount importance. This study underscores the potential of leveraging linguistic analysis and advanced machine learning techniques to identify depressive symptoms, thereby contributing to more effective mental health diagnostics and interventions. [ABSTRACT FROM AUTHOR]

Abstract Millions of people use online social networks to reinforce their sense of belonging, for example by giving and asking for feedback as a form of social validation and self-recognition. It is common to observe disagreement among people beliefs and points of view when expressing this feedback. Modeling and analyzing such interactions is crucial to understand social phenomena that happen when people face different opinions while expressing and discussing their values. In this work, we study a Reddit community in which people participate to judge or be judged with respect to some behavior, as it represents a valuable source to study how users express judgments online. We model threads of this community as complex networks of user interactions growing in time, and we analyze the evolution of their structural properties. We show that the evolution of Reddit networks differ from other real social networks, despite falling in the same category. This happens because their global clustering coefficient is extremely small and the average shortest path length increases over time. Such properties reveal how users discuss in threads, i.e. with mostly one other user and often by a single message. We strengthen such result by analyzing the role that disagreement and reciprocity play in such conversations. We also show that Reddit thread’s evolution over time is governed by two subgraphs growing at different speeds. We discover that, in the studied community, the difference of such speed is higher than in other communities because of the user guidelines enforcing specific user interactions. Finally, we interpret the obtained results on user behavior drawing back to Social Judgment Theory.

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin’s closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation.

In this study, I examine how users of an online Reddit community, r/IntellectualDarkWeb, forged an anti-establishment collective identity through practices of "heterodox scientific" reasoning. I do so through a discursive analysis of comments and posts made to r/IntellectualDarkWeb during the COVID-19 pandemic. First, I deploy the BERTopic algorithm to cluster my corpus and surface topics pertaining to COVID-19. Second, I engage in a qualitative content analysis of the relevant clusters to understand how discourses about COVID-19 were mobilized by subreddit users. I show that discussions about COVID-19 were polarized along "contrarian" and "anti-contrarian" lines, with significant implications for the subreddit's process of collective identity. Overwhelmingly, contrarian content that expressed skepticism towards vaccines, mistrust towards experts, and cynicism about the medical establishment was affirmed by r/IntellectualDarkWeb users. By contrast, anti-contrarian content that sought to counter anti-vaccine rhetoric, defend expertise, or criticize subreddit users for their contrarianism was penalized. A key factor in this dynamic was Reddit's scoring mechanism, which empowered users to publicly upvote contrarian affirming content while simultaneously downvoting anti-contrarian content. As users participated in sense making about COVID-19, they deployed Reddit's scoring mechanism to reinforce a contrarian collective identity oriented around a practice of heterodox science. My research shows the continued relevance of the concept of collective identity in the digital age and its utility for understanding contemporary reactionary social movements. [ABSTRACT FROM AUTHOR]

Based on an analysis of 300k opening posts to the popular image-based discussion forum r/PoliticalCompassMemes, in this paper we offer an empirical analysis of how the 'political compass' meme template is used to playfully work out speculative political imaginaries that reconfigure existing ideologies into niche political subcultures. We observe various dynamics over time, including how participants self-identify with different 'quadrants' of the compass via meme characters and 'flair' metadata. To analyze these dynamics, we revisit a classic argument of the 'Birmingham School' of culture studies, concerning the latent relationship between revolutionary politics and youth subcultural styles. While finding value in the latter, we identify a fundamental shift in digital political subcultures from style as a proxy for politics to politics as a kind of style. [ABSTRACT FROM AUTHOR]

In recent years, the unprecedented explosion of artificial intelligence (AI), particularly generative AI, has dramatically and drastically altered many human fields, posing queries about how generative AI can imitate human language. Given the newness of generative AI as a controversial phenomenon, there is an urgency to closely examine how its linguistic outputs could mimic human language produced in natural contexts. Hence, in this short report, we discuss the observed similarities and differences in the linguistic features of the subreddit r/Marriage spouse appreciatory posts and ChatGPT-4 outputs. These results were the offshoot of our genre analysis on these two linguistic data sets. Our analysis revealed that ChatGPT-4 generated texts contain impeccable grammar, while the Reddit appreciatory posts have grammatical discrepancies, such as errors in subject-verb agreement, improper punctuation marks, and erroneous capitalization; ChatGPT-4 generated texts have more complex syntactical structure; Reddit dataset utilized more internet jargon, slang, and profanities and seems to be unpredictable and arbitrary in terms of textual length; and ChatGPT-4 outputs appear to overuse emojis while underuse emoticons and tend to use these digital linguistic elements without regard to their proper contexts. In light of these results, we claim that AI-generated texts, although they can mimic human language, this is on a mere surface level, and a closer inspection could uncover distinct variations. We recommend that future studies use more comprehensive and different datasets and continuously employ comparative and contrastive linguistic analysis to further investigate AI's imitative capabilities. [ABSTRACT FROM AUTHOR]

This exploratory research investigated the sufficiency of existing infrastructure to adapt to high temperatures and explored the perceptions of heat stress from students in on‐campus residences at the U15 Group of Universities in Canada. The prevalence of air conditioning in student residences was used to estimate the adaptive capacity of existing infrastructure, and posts and comments on Reddit relevant to the perceptions of heat stress were collected in January 2023 through a query of relevant key words within each institution's subreddit. Most institutions (80%) had some residences with air conditioning. However, four main themes emerged through the thematic analysis of 409 posts and comments on Reddit: (1) complaints, (2) impacts on wellbeing, (3) adaptation strategies, and (4) climate change. The perceptions of heat stress from students suggest that existing available cooling strategies do not provide sufficient adaptation to high indoor temperatures. Recognizing student perceptions and experiences is necessary in designing and implementing future adaptation strategies to promote the health and wellbeing of postsecondary students in Canada. Key messages: Extreme heat is a threat to global health and is of increasing concern, even in traditionally cold countries like Canada.The importance of extreme temperatures in the indoor environment is often overlooked despite its impact on health and wellbeing, and its significance in climate change adaptation.Postsecondary students are vulnerable to heat stress, with evidence suggesting that Canadian postsecondary students currently experience adverse impacts on wellbeing as a result of high indoor temperatures in on‐campus residences. [ABSTRACT FROM AUTHOR]

There is a distinct lack of criminological research examining victimisation experiences in emerging cryptocurrency frauds. At the same time, online cryptocurrency communities have become a key part of the social milieu of the cryptocurrency ecosystem where scams are commonplace. Using Reddit forum data from the subreddit r/ CryptoCurrency, this exploratory qualitative study investigates how users in an online cryptocurrency community share knowledge and experiences of cryptocurrency scams. Thematic analysis revealed how online cryptocurrency communities discuss scams by (1) arming the community (e.g. newcomer guides, personal disclosures of scam victimisation, and reflections on the technological affordances in scams); and (2) establishing community norms in response to cryptocurrency scams (e.g. protecting the community, 'scambaiting' practices, normalising scams as an outcome of 'decentralisation'). Gaining a deeper understanding of cryptocurrency scam experiences provides timely insights into the intersections between victims/offenders in digital environments, how we can respond to the recent growth in cryptocurrency scams, and the variegated ways that victims seek assistance following experiences. [ABSTRACT FROM AUTHOR]

This study examines the relationship between Bitcoin market dynamics and user activity on the r/cryptocurrency subreddit. The purpose of this research is to understand how social media activity correlates with Bitcoin price and trading volume, and to explore the sentiment and topical focus of Reddit discussions. We collected data on Bitcoin's closing price and trading volume from January 2021 to December 2022, alongside the most popular posts and comments from the subreddit during the same period. Our analysis revealed significant correlations between Bitcoin market metrics and Reddit activity, with user discussions often reacting to market changes. Additionally, user activity on Reddit may indirectly influence the market through broader social and economic factors. Sentiment analysis showed that positive comments were more prevalent during price surges, while negative comments increased during downturns. Topic modeling identified four main discussion themes, which varied over time, particularly during market dips. These findings suggest that social media activity on Reddit can provide valuable insights into market trends and investor sentiment. Overall, our study highlights the influential role of online communities in shaping cryptocurrency market dynamics, offering potential tools for market prediction and regulation. [ABSTRACT FROM AUTHOR]

Social media platforms such as Reddit allow users to share information and find support about different topics. The present research investigated the topics, sentiments, and emotions discussed in a subreddit about social anxiety prior to and during the COVID-19 pandemic. Latent Dirichlet Allocation (LDA) was used to discover latent topics from the data. Sentiment and emotion analyses were performed on the posts and comments associated with each topic. Two-proportions z-tests were computed to investigate whether the percentage of positive, negative, and neutral sentiments expressed in posts and comments for each topic differed between the period prior to and during COVID-19. Thirteen topics about social interactions, coping mechanisms, and physiological and cognitive aspects of social anxiety emerged: (1) Miscellaneous, (2) Interacting with others, (3) Family and time, (4) Medication and receiving help, (5) Physical features and appearance, (6) At work, (7) Physical sensations and cognitive aspects, (8) School activities, (9) Dating, (10) Communicating and asking questions, (11) Social media, (12) Stressful behaviors, and (13) Substances. There was no difference in the sentiment of posts and comments between the two time periods, with the exception of a few topics for which there were more neutral comments and fewer positive comments prior to the pandemic. The three most prominent emotions expressed were anticipation, trust, and fear. These findings inform on the topics discussed in an online community about social anxiety, and on differences in the sentiment expressed about those topics prior to and during the pandemic. Future research can investigate causal associations between the COVID-19 pandemic and online discussions about social anxiety, as well as the clinical implications of interacting in such online communities for individuals struggling with social anxiety. [ABSTRACT FROM AUTHOR]

This study explores sentiments towards the World ESports Association (WESA) within a Counter-Strike Reddit community. Esports communities have not embraced the various self-proclaimed domestic and international esports governing organisations and are notoriously hostile to any organisation perceived as exploiting them. Our sentiment analysis was based on a review of 5359 comments (distributed across 29 threads and five subreddits) made by Counter-Strike Redditors. We retained 861 of these comments in our final data set. Provisional themes were revised until the main categories were finalised. The key finding was that negative sentiments outweigh positive sentiments. Counter-Strike Redditors criticised WESA for subsequent monopolisation and unnecessary interference in an otherwise functional system, corruption, a lack of transparency and power, and its opportunistic financial behaviour. More positively, WESA is credited with standardising rules and setting standards, enhancing player welfare and protection, growing esports, and offsetting publisher power and incompetence. Despite these sentiments, we propose that esports communities' influence on institutional considerations is likely to be limited. There is an ongoing tension between the initial understanding of gaming and its institutionalisation process. Acquiring player/fan acceptance is an ongoing challenge for these organisations. [ABSTRACT FROM AUTHOR]

Stalking, a widespread and distressing phenomenon, has recently garnered considerable attention. The advent of digital platforms has revolutionized the landscape of stalking, presenting new avenues and challenges for research. However, the impact of the coronavirus disease (COVID)-19 pandemic on stalking remains underexplored, despite extensive studies on similar crimes such as intimate partner violence and domestic violence. To address this gap, our study focused on Reddit, a prominent online platform with a diverse user base and open discussion. Through an analysis of posts from the subreddit (https://www.reddit.com/r/Stalking/), we sought to compare the discourse on stalking before and after the COVID-19 pandemic. We found notable shifts in stalking-related posts before and after the COVID-19 pandemic, particularly with the emergence of new topics centered on cyberstalking. We also observed that the experiences of stalking victims have significantly changed following the COVID-19 pandemic. Based on our findings, we discussed the implications for policies to help stalking victims. [ABSTRACT FROM AUTHOR]

Social media platforms are highly interconnected because many users maintain a presence across multiple platforms. Consequently, efforts to limit the spread of misinformation taken by individual platforms can have complex consequences on misinformation diffusion across the social media ecosystem. This is further complicated by the diverse social structures, platform standards, and moderation mechanisms provided on each platform. We study this issue by extending our previous model of Reddit interactions and community-specific moderation measures. By adding a followership-based model of Twitter interactions and facilitating cross-platform user participation, we simulate information diffusion across heterogeneous social media platforms. While incorporating platform-specific moderation mechanisms, we simulate interactions at the user level and specify user-specific attributes. This allows practitioners to conduct experiments with various types of actors and different combinations of moderation. We show how the model can simulate the impacts of such features on discussions facilitated by Reddit and Twitter and the cross-platform spread of misinformation. To validate this model, we use a combination of empirical datasets from three U.S. political events and prior findings from user surveys and studies. [ABSTRACT FROM AUTHOR]

Research suggests that marginalized social media users face disproportionate content moderation and removal. However, when content is removed or accounts suspended, the processes governing content moderation are largely invisible, making assessing content moderation bias difficult. To study this bias, we conducted a digital ethnography of marginalized users on Reddit's /r/FTM subreddit and Twitch's "Just Chatting" and "Pools, Hot Tubs, and Beaches" categories, observing content moderation visibility in real time. We found that on Reddit, a text-based platform, platform tools make content moderation practices invisible to users, but moderators make their practices visible through communication with users. Yet on Twitch, a live chat and streaming platform, content moderation practices are visible in channel live chats, "unban appeal" streams, and "back from my ban" streams. Our ethnography shows how content moderation visibility differs in important ways between social media platforms, harming those who must see offensive content, and at other times, allowing for increased platform accountability. [ABSTRACT FROM AUTHOR]

Misuse of prescription and non-prescription stimulants and related overdose deaths represent a growing public health crisis that warrants immediate intervention. We examined 100 posts and their respective comments from a public, recovery-oriented Reddit community in January 2021 to explore content related to DSM-V stimulant use disorder symptoms, access and barriers to recovery, and peer support. Using inductive and deductive methods, a codebook was developed with the following primary themes: 1) DSM-V Symptoms and Risk Factors, 2) Stigma/Shame, 3) Seeking Advice or Information, 4) Supportive or Unsupportive Comments. In 37% of posts community members reported taking high doses and engaging in prolonged misuse of stimulants. Nearly half of posts in the sample (46%) were seeking advice for recovery, but 42% noted fear of withdrawal symptoms or a loss of productivity (18%) as barriers to abstinence or a reduction in use. Concerns related to stigma, shame, hiding use from others (30%), and comorbid mental health conditions (34%) were also noted. Social media content analysis allows for insight into information about lived experiences of individuals struggling with substance use disorders. Future online interventions should address recovery barriers related to stigma and shame as well as fears associated with the physical and psychological impact of quitting stimulant misuse. [ABSTRACT FROM AUTHOR]

Since its onset, scholars have characterized social media as a valuable source for data collection since it presents several benefits (e.g. exploring research questions with hard-to-reach populations). Nonetheless, methods of online data collection are riddled with ethical and methodological challenges that researchers must consider if they want to adopt good practices when collecting and analyzing online data. Drawing from our primary research project, where we collected passive online data on Reddit, we explore and detail the steps that researchers must consider before collecting online data: (1) planning online data collection; (2) ethical considerations; and (3) data collection. We also discuss two atypical questions that researchers should also consider: (1) how to handle deleted user-generated content; and (2) how to quote user-generated content. Moving on from the dichotomous discussion between what is public and private data, we present recommendations for good practices when collecting and analyzing qualitative online data. [ABSTRACT FROM AUTHOR]

Can engagement with far-right social media communities socialize users into a new political identity? This study addresses concerns about the spread of far-right groups on mainstream social media platforms by examining how newcomers are affected by their engagement with these groups. I theorize that changes in social identity expression indicative of socialization will be measurable in the language users use to express themselves on social media, and that the magnitude of this linguistic change will intensify with more frequent far-right engagement. I develop a custom dictionary of far-right-relevant terms sourced from communities like The Daily Stormer and Stormfront. Using an original dataset of Reddit user posting histories from 2015–2017, I test for increases in the frequency of this far-right vocabulary. I find that users who engage often with a far-right community like r/The_Donald begin to sound more like white nationalists within three months. Socialized users also use far-right vocabulary more frequently in other spaces on their platform, contributing to the spread and normalization of far-right rhetoric. [ABSTRACT FROM AUTHOR]

Generative AI finds itself at the peak of Gartner's Emerging Technology hype cycle. New users of AI frequently turn to social media to not only share their experience but also explore how the new technology could be used. This exploratory analysis seeks to better understand how social media is being used for the sharing of this knowledge through an analysis of topics as well as the relationships among the virtual communities that these users post in. We extract and analyze 5.4M user submissions related to AI across 12 subcommunities of discourse from Reddit. Through topic analysis and sentiment analysis we look to understand what is being discussed both positively and negatively about generative AI. This analysis provides insights into the types of conversations about generative AI on Reddit and serves as a foundation for future research into social media discourse on this topic. [ABSTRACT FROM AUTHOR]

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The emergence of cryptocurrency markets has drastically changed how online transactions are conducted and provide a new investment opportunity. This study contributes to the literature on directional cryptocurrency price returns prediction by expanding the set of meaningful features extracted from textual data with sentiment analysis and comparing their usefulness across multiple data sources. In contrast to previous studies, we use fine-grained topic-sentiment features. More specifically, aspect-based sentiment analysis models, JST and TS-LDA, are implemented to incorporate joint topical-sentiment features and the degree of text subjectivity. We collected, and make available, a dataset, which consists of data scraped from Reddit, Bitcointalk and CryptoCompare sources, to demonstrate that proposed features lead to interpretable topics and an improvement in predictive performance. [ABSTRACT FROM AUTHOR]

Despite the growing demand for accessible tourism, there remains a lack of research on the travel perspectives and experiences of people with disabilities. Social media platforms, such as Reddit, provide unique opportunities to understand the perspectives and experiences of people with disabilities. This study aimed to investigate travel‐related perspectives and experiences of physical disability communities, including wheelchair, deaf, and blind communities, to provide implications for travel service providers and policymakers to improve the accessibility and inclusivity of travel services. Using structural topic modeling and network analysis, 12 prominent topics related to travel perspectives and experiences among physical disability communities were identified to map their relationships. Our study highlights the significance of understanding the unique perspectives, experiences, and needs of individuals with disabilities in the hospitality and tourism industry. It provides implications for improving the accessibility and inclusivity of travel services. [ABSTRACT FROM AUTHOR]

This article provides findings from our dual-computational/qualitative analysis of r/Stoicism, a large subreddit in which self-presenting boys and men seek Stoic philosophical advice on various life matters. In choosing to investigate this decidedly (hetero)masculinized online space in which users share their anxieties and grievances, we expected to find substantial evidence of "toxic" manosphere-style discourse, while also hoping to uncover counter patterns which, like Maloney et al.'s study of 4chan, complicate assumptions around the discursive practices of boys and men in online spaces such as these. Rather, what we found was a complete absence of toxic discourse, and instead the presence of patterns which complicate the logics underpinning efforts at deradicalization and wider socio-positive masculinity agendas. Thorburn's work has been important in foregrounding how the "neoliberal emphasis on individualism and a capitalist 'hustle-culture'" underpin manosphere logics. Here, we see similar, albeit more palatable (to mainstream sensibilities), neoliberal tenets at work across counter logics, and reflect on why economic-structural explanations for boys' and men's anxieties are sidelined in such mainstream responses to the manosphere. [ABSTRACT FROM AUTHOR]

Many feel uncomfortable discussing self-directed violence with individuals in their social circle, often leading to concerned posts on social media. Previous studies address self-harm, suicide, and nonsuicidal self-injury; however, minimal literature combines suicide and nonsuicidal self-injury to explore common themes representing self-directed violence. This study identified themes from suicide and self-harm subreddit communities, with themes common to each subreddit comprising themes of self-directed violence. Using 1,989 Reddit posts from suicide and self-harm communities, we employed an inductive thematic analysis to explore how users discussed self-directed violence within anonymous online communities. Ultimately, the impact of using Reddit for discussion of self-directed violence depends on the individual. We generated six themes, with two from self-harm data (i.e., graphic descriptions of self-harm behavior and continued self-harm to manage emotions), two from suicide data (i.e., support-seeking to replace social connections and triggering moments initiating suicide-related thoughts, plans, and behavior), and two themes embodying self-directed violence (i.e., prevention through creative expression and safety for discussion mainly exists online). Despite attempts to provide help-seeking resources, the use of Reddit for connection and inquiry about coping skills for self-directed violence cessation could increase users' risk for detrimental mental health impacts from the inclusion of detailed descriptions of self-harm, suicide plans, past attempts, suicide or nonsuicidal self-injury means, and suicide notes left for loved ones. Given the delicate balance of seeking support and increasing exposure to graphic content, it is crucial to discuss the nuances of Reddit exchanges with Reddit users, practitioners, moderators, and developers. [ABSTRACT FROM AUTHOR]

With drastic changes to abortion policy, the months following the Dobbs leak and subsequent decision in 2022 were a uniquely uncertain and difficult time for abortion access in the United States. To understand experiences of challenges to abortion access during that time, we used a hybrid inductive and deductive thematic coding approach to analyse descriptions of barriers and their impacts shared in an abortion subreddit (r/abortion). A simple random sample of 10% of posts was obtained from those shared from 02 May 2022 through 23 December 2022; comments were purposively sampled during the coding process. In this sample of submissions (n = 523 posts, 88 comments), people described structural barriers identified in past research, including state abortion bans and gestational limits, high costs, limited appointment availability, and long travel required. Posters also commonly described known social barriers, including limited social support and abortion stigma. Several impactful barriers not well-described in past research emerged inductively, including wait time for receiving mail-ordered abortion medication, low credibility of online ordering platforms, and concerns about legal risks of accessing abortion or related medical care. The most common consequences of experiencing barriers were adverse mental health outcomes, delayed access to care, and being compelled to self-manage their abortion because of access barriers. This analysis provides timely insights into the experiences and impacts of abortion access barriers in a group of people with a range of engagement with clinical abortion care, lived experiences, and points in their abortion processes, with public health implications for mental health and abortion access.

With retail investors playing a significant role in driving market adoption, cryptocurrency investment has gained widespread popularity recently. However, the perceived value of cryptocurrency investments and the perceived risk associated with investments have not been thoroughly examined. To address this gap in this research field, we surveyed 200 social media users active on social networking paltform - Reddit to unravel the intricate interplay of perceived value, perceived risk, and demographic factors that shape the decision-making process among social media users engaged in cryptocurrency investments. Our findings suggest that the acceptance of cryptocurrency investments is positively influenced by perceived value, whereas perceived risk exerts a negative influence. We also found that certain demographic elements which include age, education, gender, monthly income, and investment experience can moderate the relationship between perceived value, perceived risk, and the adaptation of cryptocurrency investments. The findings from our study offer valuable perspectives for retail investors and industry stakeholders aiming to enhance their understanding of the determinants that impact the acceptance of cryptocurrency investments among social media users.

The present study was aimed at identifying key topics in online discussions about the use of ChatGPT by examining a large dataset extracted from Reddit social media using natural language processing. A corpus of 159,971 posts about ChatGPT were extracted from a custom-made python-coded Reddit content scraper for posts in the r/ChatGPT subreddit discussions. After cleaning the data, the sample was reduced to 119,853 posts which was subjected to cluster analysis using the open-source IRaMuTeQ software to identify main topics based on the cooccurrence of texts. These clusters were named by a panel of social psychology experts (n=3) by reading typical text segments within each cluster. Four thematic clusters emerged, categorized into two main topics: “Society and AI Integration”, focusing on ethical concerns (32.1%), and “Operational Aspects and Applications”, which delves into technical and practical facets (67.9%). The latter includes clusters like “AI Technical Framework”, “Casual AI Interactions”, and “Human-AI Etiquette”. The Reddit discourse provides a comprehensive understanding of ChatGPT, revealing user priorities like system capabilities and ethical considerations. Notably, the “Human-AI Etiquette” cluster is a new topic less covered in existing literature. The findings underscore the importance of effective prompting for meaningful user engagement with ChatGPT.

Our study aims to collect data to understand ideological and extreme bias in text articles shared across various online communities, particularly focusing on the language used in subreddits associated with extremism and targeted violence.Initially, we gathered data from related online communities, specifically the r/Liberal and r/Conservative communities on Reddit, utilizing the Reddit Pushshift API to collect URLs shared within these subreddits. Our aim was to gather news, opinion, and feature articles, resulting in a corpus of 226,010 articles. We also curated a balanced subset of 45,108 articles and annotated 4000 articles to validate their relevance, facilitating understanding of language usage within ideological Reddit communities and insights into ideological bias in media content.Expanding beyond binary ideologies, we introduced a new category termed “Restricted” to encompass articles shared in private or banned subreddits. This third category encompasses articles shared in restricted, privatized, quarantined, or banned subreddits characterized by radicalized and extremist ideologies. This expansion yielded a large dataset of 377,144 articles. Additionally, we included articles from subreddits with unspecified ideologies, creating a holdout set of 922,522 articles. In total, our combined dataset of 1.3 million articles collected from 55 different subreddits will assist in examining radicalized communities and providing discourse analysis in associated subreddits, enhancing understanding of the language used in articles shared within radicalized Reddit communities and offering insights into extreme bias in media content.In summary, we collected 1.52 million articles to understand ideological and extreme bias, providing a comprehensive dataset that aids in understanding language usage within text articles posted in ideological and extreme Reddit communities.