Identity Management and Mental Health Discourse in Social Media

Disclosure in psychotherapy versus in anonymous and non-anonymous online spaces

Objective The primary aim of this study was to investigate the factors affecting individuals' decisions to discuss specific personal issues in psychotherapy vs on social media, either non-anonymously or pseudonymously/anonymously.Method A heterogeneous sample of participants (N = 443) completed an online survey that included assessments of their therapy experience, attachment style, attitudes towards seeking mental healthcare, and the extent of their disclosures about personally distressing topics in therapy and online under different conditions.Results Results suggest that attachment style plays a significant role in determining individuals' likelihood of discussing personally distressing topics online and in determining the extent to which they find disclosures in therapy and in anonymous and non-anonymous online spaces to be helpful.Conclusion Clinicians may find it helpful to monitor the extent to which patients disclose personal issues online, checking as to whether patients, especially younger patients and those with avoidant and ambivalent attachment styles, view psychotherapy as an appropriate domain to disclose specific personally distressful issues.

The Role of Social Media in the Experiences of COVID-19 Among Long-Hauler Women: Qualitative Study

BACKGROUND

The extant literature suggests that women are more vulnerable to COVID-19 infection and at higher risk for developing long COVID. Due to pandemic mitigation recommendations, social media was relied upon for various aspects of daily life, likely with differences of usage between genders.

OBJECTIVE

This study aimed to explore the role and functions of social media in the lives of long-hauler women.

METHODS

Participants were purposively snowball-sampled from an online health promotion intervention for long-hauler women with COVID-19 from March to June 2021. During this time, one-on-one, semistructured interviews were conducted online until data saturation was agreed to have been achieved (ie, 15 interviews). Interview transcripts and field notes were analyzed using an emergent, inductive approach.

RESULTS

In total, 15 women were enrolled. The main roles of social media included facilitating support group participation, experience sharing, interpersonal connections, and media consumption. Emergent themes demonstrated that participants rely on social media to fulfill needs of emotional support, social engagement, spirituality, health planning, information gathering, professional support, and recreationally for relaxation. As long-hauler women turn to social media to discuss symptom and health management as well as the intention to vaccinate, this study demonstrates both the associated benefits (ie, decreased isolation) and challenges (ie, misinformation, rumination, resentment, jealousy).

CONCLUSIONS

The public health implications of these findings support the development of gender-tailored health promotion interventions that leverage the benefits of social media, while mitigating the negative impacts, for women with long COVID.

How People With a Bipolar Disorder Diagnosis Talk About Personal Recovery in Peer Online Support Forums: Corpus Framework Analysis Using the POETIC Framework

Background

Personal recovery is of particular value in bipolar disorder, where symptoms often persist despite treatment. We previously defined the POETIC (Purpose and Meaning, Optimism and Hope, Empowerment, Tensions, Identity, Connectedness) framework for personal recovery in bipolar disorder. So far, personal recovery has only been studied in researcher-constructed environments (eg, interviews and focus groups). Support forum posts can serve as a complementary naturalistic data resource to understand the lived experience of personal recovery.

Objective

This study aimed to answer the question "What can online support forum posts reveal about the experience of personal recovery in bipolar disorder in relation to the POETIC framework?"

Methods

By integrating natural language processing, corpus linguistics, and health research methods, this study analyzed public, bipolar disorder support forum posts relevant to the lived experience of personal recovery. By comparing 4462 personal recovery-relevant posts by 1982 users to 25,197 posts not relevant to personal recovery, we identified 130 significantly overused key lemmas. Key lemmas were coded according to the POETIC framework.

Results

Personal recovery-related discussions primarily focused on 3 domains: "Purpose and meaning" (particularly reproductive decisions and work), "Connectedness" (romantic relationships and social support), and "Empowerment" (self-management and personal responsibility). This study confirmed the validity of the POETIC framework to capture personal recovery experiences shared on the web and highlighted new aspects beyond previous studies using interviews and focus groups.

Conclusions

This study is the first to analyze naturalistic data on personal recovery in bipolar disorder. By indicating the key areas that people focus on in personal recovery when posting freely and the language they use, this study provides helpful starting points for formal and informal carers to understand the concerns of people diagnosed with a bipolar disorder and to consider how to best offer support.

Adolescent Social Communication Through Smartphones: Linguistic Features of Internalizing Symptoms and Daily Mood

The increasing use of smartphone technology by adolescents has led to unprecedented opportunities to identify early indicators of shifting mental health. This intensive longitudinal study examined the extent to which differences in mental health and daily mood are associated with digital social communication in adolescence. In a sample of 30 adolescents (ages 11-15 years), we analyzed 22,152 messages from social media, email, and texting across one month. Lower daily mood was associated with linguistic features reflecting self-focus and reduced temporal distance. Adolescents with lower daily mood tended to send fewer positive emotion words on a daily basis, and more total words on low mood days. Adolescents with lower daily mood and higher depression symptoms tended to use more future focus words. Dynamic linguistic features of digital social communication that relate to changes in mental states may represent a novel target for passive detection of risk and early intervention in adolescence.

Corpus-Based Discourse Analysis of a Reddit Community of Users of Crystal Methamphetamine: Mixed Methods Study

BACKGROUND

Methamphetamine is a highly addictive stimulant that affects the central nervous system. Crystal methamphetamine is a form of the drug resembling glass fragments or shiny bluish-white rocks that can be taken through smoking, swallowing, snorting, or injecting the powder once it has been dissolved in water or alcohol.

OBJECTIVE

The objective of this study is to examine how identities are socially (discursively) constructed by people who use methamphetamine within a subreddit for people who regularly use crystal meth.

METHODS

Using a mixed methods approach, we analyzed 1000 threads (318,422 words) from a subreddit for regular crystal meth users. The qualitative component of the analysis used concordancing and corpus-based discourse analysis to identify discursive themes informed by assemblage theory. The quantitative portion of the analysis used corpus linguistic techniques including keyword analysis to identify words occurring with statistically marked frequency in the corpus and collocation analysis to analyze their discursive context.

RESULTS

Our findings reveal that the subreddit contributors use a rich and varied lexicon to describe crystal meth and other substances, ranging from a neuroscientific register (eg, methamphetamine and dopamine) to informal vernacular (eg, meth, dope, and fent) and commercial appellations (eg, Adderall and Seroquel). They also use linguistic resources to construct symbolic boundaries between different types of methamphetamine users, differentiating between the esteemed category of "functional addicts" and relegating others to the stigmatized category of "tweakers." In addition, contributors contest the dominant view that methamphetamine use inevitably leads to psychosis, arguing instead for a more nuanced understanding that considers the interplay of factors such as sleep deprivation, poor nutrition, and neglected hygiene.

CONCLUSIONS

The subreddit contributors' discourse offers a "set and setting" perspective, which provides a fresh viewpoint on drug-induced psychosis and can guide future harm reduction strategies and research. In contrast to this view, many previous studies overlook the real-world complexities of methamphetamine use, perhaps due to the use of controlled experimental settings. Actual drug use, intoxication, and addiction are complex, multifaceted, and elusive phenomena that defy straightforward characterization.

Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study

BACKGROUND

The optimal treatment for gender dysphoria is medical intervention, but many transgender and nonbinary people face significant treatment barriers when seeking help for gender dysphoria. When untreated, gender dysphoria is associated with depression, anxiety, suicidality, and substance misuse. Technology-delivered interventions for transgender and nonbinary people can be used discretely, safely, and flexibly, thereby reducing treatment barriers and increasing access to psychological interventions to manage distress that accompanies gender dysphoria. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model clinical constructs.

OBJECTIVE

This study aimed to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP, using transgender and nonbinary people's social media data.

METHODS

Overall, 6 ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1573 Reddit (Reddit Inc) posts created on transgender- and nonbinary-specific web-based forums. After developing a codebook grounded in clinical science, a research team of clinicians and students experienced in working with transgender and nonbinary clients used qualitative content analysis to determine whether gender dysphoria was present in each Reddit post (ie, the dependent variable). NLP (eg, n-grams, Linguistic Inquiry and Word Count, word embedding, sentiment, and transfer learning) was used to transform the linguistic content of each post into predictors for ML algorithms. A k-fold cross-validation was performed. Hyperparameters were tuned with random search. Feature selection was performed to demonstrate the relative importance of each NLP-generated independent variable in predicting gender dysphoria. Misclassified posts were analyzed to improve future modeling of gender dysphoria.

RESULTS

Results indicated that a supervised ML algorithm (ie, optimized extreme gradient boosting [XGBoost]) modeled gender dysphoria with a high degree of accuracy (0.84), precision (0.83), and speed (1.23 seconds). Of the NLP-generated independent variables, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) clinical keywords (eg, dysphoria and disorder) were most predictive of gender dysphoria. Misclassifications of gender dysphoria were common in posts that expressed uncertainty, featured a stressful experience unrelated to gender dysphoria, were incorrectly coded, expressed insufficient linguistic markers of gender dysphoria, described past experiences of gender dysphoria, showed evidence of identity exploration, expressed aspects of human sexuality unrelated to gender dysphoria, described socially based gender dysphoria, expressed strong affective or cognitive reactions unrelated to gender dysphoria, or discussed body image.

CONCLUSIONS

Findings suggest that ML- and NLP-based models of gender dysphoria have significant potential to be integrated into technology-delivered interventions. The results contribute to the growing evidence on the importance of incorporating ML and NLP designs in clinical science, especially when studying marginalized populations.

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.

The Influence of Social Media Interactions and Behaviors on Depressive Symptoms Among Sexual and Gender Minority Young Adults in the United States: Protocol for a Mixed Methods Longitudinal Study

BACKGROUND

Sexual and gender minority (SGM; ie, lesbian, gay, bisexual, transgender, and otherwise queer) young adults experience disparities in depression and other internalizing psychopathology. Although social media use is widespread and SGM people have more social media accounts and are more socially active on them than non-SGM individuals, few studies have examined the impact of social media on depression in this group.

OBJECTIVE

The PRIDE iM study will be the first longitudinal, mixed methods research conducted to determine the impact of social media interactions and behaviors as pathways to depressive symptoms among SGM young adults living in the United States.

METHODS

PRIDE iM uses a bookends variation of the longitudinal sequential mixed methods design. Participants will be recruited nationally from social media. First, between July 2019 and February 2020, we conducted a qualitative phase (T1) comprising web-based individual interviews (N=58) to inform the building and content of the quantitative survey. Second, from February 2022 to September 2022, we will conduct a series of web-based surveys (N=1000 at baseline) with 4 data points (T2-T5), each one collected every 6 to 8 weeks. Third, from October 2022 to December 2022, we will conduct a second qualitative phase (T6) of web-based interviews using outcome trajectories found in the longitudinal survey analyses to purposively sample survey participants and conduct web-based interviews to contextualize and explain survey findings. Qualitative data from T1 and T6 will be analyzed using a reflexive thematic analysis approach. As we sought to capture change over time in the association between the main predictors (ie, social media interactions and behaviors) and depressive symptoms, we propose analyzing T2 to T5 data using latent growth models with a structural equation modeling framework. Data integration at the method, interpretation, and reporting levels will be achieved through building and connecting and the use of a staged approach and joint displays, respectively. At all stages, we will assess the fit of data integration as recommended by the principles of best practice for mixed methods research in psychology.

RESULTS

Data collection will be completed by December 2022. Qualitative data analyses will be completed by March 2023, and quantitative analyses of the primary outcome of interest will be completed by June 2023.

CONCLUSIONS

PRIDE iM will confirm, reject, or uncover the presence of potential relationships between social media interactions and behaviors and depressive symptoms among SGM people. This study represents fundamental groundwork to develop social media-based interventions that target modifiable interactions and behaviors that are most likely to influence mental health outcomes, thus seizing the opportunity to merge the popularity of this medium among SGM people with evidence-based approaches.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/43627.

Emotional and cognitive changes surrounding online depression identity claims

As social media has proliferated, a key aspect to making meaningful connections with people online has been revealing important parts of one's identity. In this work, we study changes that occur in people's language use after they share a specific piece of their identity: a depression diagnosis. To do so, we collect data from over five thousand users who have made such a statement, which we refer to as an identity claim. Prior to making a depression identity claim, the Reddit user's language displays evidence of increasingly higher rates of anxiety, sadness, and cognitive processing language compared to matched controls. After the identity claim, these language markers decrease and more closely match the controls. Similarly, first person singular pronoun usage decreases following the identity claim, which was previously previously found to be indicative of self-focus and associated with depression. By further considering how and to whom people express their identity, we find that the observed longitudinal changes are larger for those who do so in ways that are more correlated with seeking help (sharing in a post instead of a comment; sharing in a mental health support forum). This work suggests that there may be benefits to sharing one's depression diagnosis, especially in a semi-anonymous forum where others are likely to be empathetic.

Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study

Background

Effective suicide risk assessments and interventions are vital for suicide prevention. Although assessing such risks is best done by health care professionals, people experiencing suicidal ideation may not seek help. Hence, machine learning (ML) and computational linguistics can provide analytical tools for understanding and analyzing risks. This, therefore, facilitates suicide intervention and prevention.

Objective

This study aims to explore, using statistical analyses and ML, whether computerized language analysis could be applied to assess and better understand a person’s suicide risk on social media.

Methods

We used the University of Maryland Suicidality Dataset comprising text posts written by users (N=866) of mental health–related forums on Reddit. Each user was classified with a suicide risk rating (no, low, moderate, or severe) by either medical experts or crowdsourced annotators, denoting their estimated likelihood of dying by suicide. In language analysis, the Linguistic Inquiry and Word Count lexicon assessed sentiment, thinking styles, and part of speech, whereas readability was explored using the TextStat library. The Mann-Whitney U test identified differences between at-risk (low, moderate, and severe risk) and no-risk users. Meanwhile, the Kruskal-Wallis test and Spearman correlation coefficient were used for granular analysis between risk levels and to identify redundancy, respectively. In the ML experiments, gradient boost, random forest, and support vector machine models were trained using 10-fold cross validation. The area under the receiver operator curve and F₁-score were the primary measures. Finally, permutation importance uncovered the features that contributed the most to each model’s decision-making.

Results

Statistically significant differences (P<.05) were identified between the at-risk (671/866, 77.5%) and no-risk groups (195/866, 22.5%). This was true for both the crowd- and expert-annotated samples. Overall, at-risk users had higher median values for most variables (authenticity, first-person pronouns, and negation), with a notable exception of clout, which indicated that at-risk users were less likely to engage in social posturing. A high positive correlation (ρ>0.84) was present between the part of speech variables, which implied redundancy and demonstrated the utility of aggregate features. All ML models performed similarly in their area under the curve (0.66-0.68); however, the random forest and gradient boost models were noticeably better in their F₁-score (0.65 and 0.62) than the support vector machine (0.52). The features that contributed the most to the ML models were authenticity, clout, and negative emotions.

Conclusions

In summary, our statistical analyses found linguistic features associated with suicide risk, such as social posturing (eg, authenticity and clout), first-person singular pronouns, and negation. This increased our understanding of the behavioral and thought patterns of social media users and provided insights into the mechanisms behind ML models. We also demonstrated the applicative potential of ML in assisting health care professionals to assess and manage individuals experiencing suicide risk.

Psycholinguistic changes in the communication of adolescent users in a suicidal ideation online community during the COVID-19 pandemic

Since the outbreak of the COVID-19 pandemic, increases in suicidal ideation and suicide attempts in adolescents have been registered. Many adolescents experiencing suicidal ideation turn to online communities for social support. In this retrospective observational study, we investigated the communication-language style, contents and user activity-in 7975 unique posts and 51,119 comments by N = 2862 active adolescent users in a large suicidal ideation support community (SISC) on the social media website reddit.com in the onset period of the COVID-19 pandemic. We found significant relative changes in language style markers for hopelessness such as negative emotion words (+ 10.00%) and positive emotion words (- 3.45%) as well as for social disengagement such as social references (- 8.63%) and 2nd person pronouns (- 33.97%) since the outbreak of the pandemic. Using topic modeling with Latent Dirichlet Allocation (LDA), we identified significant changes in content for the topics Hopelessness (+ 23.98%), Suicide Methods (+ 17.11%), Social Support (- 14.91%), and Reaching Out to users (- 28.97%). Changes in user activity point to an increased expression of mental health issues and decreased engagement with other users. The results indicate a potential shift in communication patterns with more adolescent users expressing their suicidal ideation rather than relating with or supporting other users during the COVID-19 pandemic.

Exploring the Posting of Nude Photographs on Reddit in Relation to Self-Esteem, Perceived Attractiveness, Narcissism, and Sensation Seeking

While many scholars have explored the sharing of nude photographs one-to-one (i.e., sexting), few have examined the sharing of nudity in a one-to-many context. The current study examined the sharing of nude photographs on Reddit, framing the practice as an act of disinhibited online behavior. A survey (n = 628) was conducted to assess whether Redditors levels of sensation seeking, self-esteem, perceived attractiveness, and narcissism would be related to whether or not they posted nude photographs on the site. Results indicated that posting nudity on Reddit was significantly associated with higher perceived attractiveness and narcissism, but not sensation seeking or self-esteem. The role of gender and sexual orientation in the posting of nudity online was also assessed, and an overrepresentation of nude content produced by females and bisexual persons, as well as an underrepresentation of nude content produced by males and heterosexuals, was found. Findings are discussed in relation to self-concept, sexual health, and the online disinhibition effect.

Ethical Issues in Social Media Recruitment for Clinical Studies: Ethical Analysis and Framework

BACKGROUND

Social media recruitment for clinical studies holds the promise of being a cost-effective way of attracting traditionally marginalized populations and promoting patient engagement with researchers and a particular study. However, using social media for recruiting clinical study participants also poses a range of ethical issues.

OBJECTIVE

This study aims to provide a comprehensive overview of the ethical benefits and risks to be considered for social media recruitment in clinical studies and develop practical recommendations on how to implement these considerations.

METHODS

On the basis of established principles of clinical ethics and research ethics, we reviewed the conceptual and empirical literature for ethical benefits and challenges related to social media recruitment. From these, we derived a conceptual framework to evaluate the eligibility of social media use for recruitment for a specific clinical study.

RESULTS

We identified three eligibility criteria for social media recruitment for clinical studies: information and consent, risks for target groups, and recruitment effectiveness. These criteria can be used to evaluate the implementation of a social media recruitment strategy at its planning stage. We have discussed the practical implications of these criteria for researchers.

CONCLUSIONS

The ethical challenges related to social media recruitment are context sensitive. Therefore, social media recruitment should be planned rigorously, taking into account the target group, the appropriateness of social media as a recruitment channel, and the resources available to execute the strategy.

A systematic literature review of how and whether social media data can complement traditional survey data to study public opinion

In this article, we review existing research on the complementarity of social media data and survey data for the study of public opinion. We start by situating our review in the extensive literature (N = 187) about the uses, challenges, and frameworks related to the use of social media for studying public opinion. Based on 187 relevant articles (141 empirical and 46 theoretical) - we identify within the 141 empircal ones six main research approaches concerning the complementarity of both data sources. Results show that the biggest share of the research has focused on how social media can be used to confirm survey findings, especially for election predictions. The main contribution of our review is to detail and classify other growing complementarity approaches, such as comparing both data sources on a given phenomenon, using survey measures as a proxy in social media research, enriching surveys with SMD, recruiting individuals on social media to conduct a second survey phase, and generating new insight on "old" or "under-investigated" topics or theories using SMD. We discuss the advantages and disadvantages associated with each of these approaches in relation to four main research purposes, namely the improvement of validity, sustainability, reliability, and interpretability. We conclude by discussing some limitations of our study and highlighting future paths for research.

Understanding the Impact of COVID-19 on Online Mental Health Forums

Like many of the disasters that have preceded it, the COVID-19 pandemic is likely to have a profound impact on people’s mental health. Understanding its impact can inform strategies for mitigating negative consequences. This work seeks to better understand the impacts of COVID-19 on mental health by examining how discussions on mental health subreddits have changed in the three months following the WHO’s declaration of a global pandemic. First, the rate at which the pandemic is discussed in each community is quantified. Then, volume of activity is measured to determine whether the number of people with mental health concerns has risen, and user interactions are analyzed to determine how they have changed during the pandemic. Finally, the content of the discussions is analyzed. Each of these metrics is considered with respect to a set of control subreddits to better understand if the changes present are specific to mental health subreddits or are representative of Reddit as a whole. There are numerous changes in the three mental health subreddits that we consider, r/Anxiety, r/depression, r/SuicideWatch; there is reduced posting activity in most cases, and there are significant changes in discussion of some topics such as work and anxiety. The results suggest that there is not an overwhelming increase in online mental health support-seeking on Reddit during the pandemic, but that discussion content related to mental health has changed.

Detecting risk level in individuals misusing fentanyl utilizing posts from an online community on Reddit

INTRODUCTION

Opioid misuse is a public health crisis in the US, and misuse of synthetic opioids such as fentanyl have driven the most recent waves of opioid-related deaths. Because those who misuse fentanyl are often a hidden and high-risk group, innovative methods for identifying individuals at risk for fentanyl misuse are needed. Machine learning has been used in the past to investigate discussions surrounding substance use on Reddit, and this study leverages similar techniques to identify risky content from discussions of fentanyl on this platform.

METHODS

A codebook was developed by clinical domain experts with 12 categories indicative of fentanyl misuse risk, and this was used to manually label 391 Reddit posts and comments. Using this data, we built machine learning classification models to identify fentanyl risk.

RESULTS

Our machine learning risk model was able to detect posts or comments labeled as risky by our clinical experts with 76% accuracy and 76% sensitivity. Furthermore, we provide a vocabulary of community-specific, colloquial words for fentanyl and its analogues.

DISCUSSION

This study uses an interdisciplinary approach leveraging machine learning techniques and clinical domain expertise to automatically detect risky discourse, which may elicit and benefit from timely intervention. Moreover, our vocabulary of online terms for fentanyl and its analogues expands our understanding of online "street" nomenclature for opiates. Through an improved understanding of substance misuse risk factors, these findings allow for identification of risk concepts among those misusing fentanyl to inform outreach and intervention strategies tailored to this at-risk group.

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

BACKGROUND

The study of depression and anxiety using publicly available social media data is a research activity that has grown considerably over the past decade. The discussion platform Reddit has become a popular social media data source in this nascent area of study, in part because of the unique ways in which the platform is facilitative of research. To date, no work has been done to synthesize existing studies on depression and anxiety using Reddit.

OBJECTIVE

The objective of this review is to understand the scope and nature of research using Reddit as a primary data source for studying depression and anxiety.

METHODS

A scoping review was conducted using the Arksey and O'Malley framework. MEDLINE, Embase, CINAHL, PsycINFO, PsycARTICLES, Scopus, ScienceDirect, IEEE Xplore, and ACM academic databases were searched. Inclusion criteria were developed using the participants, concept, and context framework outlined by the Joanna Briggs Institute Scoping Review Methodology Group. Eligible studies featured an analytic focus on depression or anxiety and used naturalistic written expressions from Reddit users as a primary data source.

RESULTS

A total of 54 studies were included in the review. Tables and corresponding analyses delineate the key methodological features, including a comparatively larger focus on depression versus anxiety, an even split of original and premade data sets, a widespread analytic focus on classifying the mental health states of Reddit users, and practical implications that often recommend new methods of professionally delivered monitoring and outreach for Reddit users.

CONCLUSIONS

Studies of depression and anxiety using Reddit data are currently driven by a prevailing methodology that favors a technical, solution-based orientation. Researchers interested in advancing this research area will benefit from further consideration of conceptual issues surrounding the interpretation of Reddit data with the medical model of mental health. Further efforts are also needed to locate accountability and autonomy within practice implications, suggesting new forms of engagement with Reddit users.

Looking for Razors and Needles in a Haystack: Multifaceted Analysis of Suicidal Declarations on Social Media-A Pragmalinguistic Approach

In this paper, we study language used by suicidal users on Reddit social media platform. To do that, we firstly collect a large-scale dataset of Reddit posts and annotate it with highly trained and expert annotators under a rigorous annotation scheme. Next, we perform a multifaceted analysis of the dataset, including: (1) the analysis of user activity before and after posting a suicidal message, and (2) a pragmalinguistic study on the vocabulary used by suicidal users. In the second part of the analysis, we apply LIWC, a dictionary-based toolset widely used in psychology and linguistic research, which provides a wide range of linguistic category annotations on text. However, since raw LIWC scores are not sufficiently reliable, or informative, we propose a procedure to decrease the possibility of unreliable and misleading LIWC scores leading to misleading conclusions by analyzing not each category separately, but in pairs with other categories. The analysis of the results supported the validity of the proposed approach by revealing a number of valuable information on the vocabulary used by suicidal users and helped to pin-point false predictors. For example, we were able to specify that death-related words, typically associated with suicidal posts in the majority of the literature, become false predictors, when they co-occur with apostrophes, even in high-risk subreddits. On the other hand, the category-pair based disambiguation helped to specify that death becomes a predictor only when co-occurring with future-focused language, informal language, discrepancy, or 1st person pronouns. The promising applicability of the approach was additionally analyzed for its limitations, where we found out that although LIWC is a useful and easily applicable tool, the lack of any contextual processing makes it unsuitable for application in psychological and linguistic studies. We conclude that disadvantages of LIWC can be easily overcome by creating a number of high-performance AI-based classifiers trained for annotation of similar categories as LIWC, which we plan to pursue in future work.

Understanding the Role of Social Media-Based Mental Health Support Among College Students: Survey and Semistructured Interviews

BACKGROUND

Mental illness is a growing concern within many college campuses. Limited access to therapy resources, along with the fear of stigma, often prevents students from seeking help. Introducing supportive interventions, coping strategies, and mitigation programs might decrease the negative effects of mental illness among college students.

OBJECTIVE

Many college students find social support for a variety of needs through social media platforms. With the pervasive adoption of social media sites in college populations, in this study, we examine whether and how these platforms may help meet college students' mental health needs.

METHODS

We first conducted a survey among 101 students, followed by semistructured interviews (n=11), of a large public university in the southeast region of the United States to understand whether, to what extent, and how students appropriate social media platforms to suit their struggle with mental health concerns. The interviews were intended to provide comprehensive information on students' attitudes and their perceived benefits and limitations of social media as platforms for mental health support.

RESULTS

Our survey revealed that a large number of participating students (71/101, 70.3%) had recently experienced some form of stress, anxiety, or other mental health challenges related to college life. Half of them (52/101, 51.5%) also reported having appropriated some social media platforms for self-disclosure or help, indicating the pervasiveness of this practice. Through our interviews, we obtained deeper insights into these initial observations. We identified specific academic, personal, and social life stressors; motivations behind social media use for mental health needs; and specific platform affordances that helped or hindered this use.

CONCLUSIONS

Students recognized the benefits of social media in helping connect with peers on campus and promoting informal and candid disclosures. However, they argued against complete anonymity in platforms for mental health help and advocated the need for privacy and boundary regulation mechanisms in social media platforms supporting this use. Our findings bear implications for informing campus counseling efforts and in designing social media-based mental health support tools for college students.

Using social media data for assessing children's exposure to violence during the COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic brought unforeseen challenges that could forever change the way societies prioritize and deal with public health issues. The approaches to contain the spread of the virus have entailed governments issuing recommendations on social distancing, lockdowns to restrict movements, and suspension of services.

OBJECTIVE

There are concerns that the COVID-19 crisis and the measures adopted by countries in response to the pandemic may have led to an upsurge in violence against children. Added stressors placed on caregivers, economic uncertainty, job loss or disruption to livelihoods and social isolation may have led to a rise in children's experience of violence in the home. Extended online presence by children may have resulted in increased exposure to abusive content and cyberbullying.

PARTICIPANTS AND SETTING

This study uses testimonial-based and conversational-based data collected from social media users.

METHODS

Conversations on Twitter were reviewed to measure increases in abusive or hateful content, and cyberbullying, while testimonials from Reddit forums were examined to monitor changes in references to family violence before and after the start of the stay-at-home restrictions.

RESULTS

Violence-related subreddits were among the topics with the highest growth after the COVID-19 outbreak. The analysis of Twitter data shows a significant increase in abusive content generated during the stay-at-home restrictions.

CONCLUSIONS

The collective experience of the COVID-19 pandemic and related containment measures offers insights into the wide-ranging risks that children are exposed to in times of crisis. As societies shift towards a new normal, which places emerging technology, remote working and online learning at its center, and in anticipation of similar future threats, governments and other stakeholders need to put in place measures to protect children from violence.

Temporal and diurnal variation in social media posts to a suicide support forum

BACKGROUND

Rates of suicide attempts and deaths are highest on Mondays and these occur more frequently in the morning or early afternoon, suggesting weekly temporal and diurnal variation in suicidal behaviour. It is unknown whether there are similar time trends on social media, of posts relevant to suicide. We aimed to determine temporal and diurnal variation in posting patterns on the Reddit forum SuicideWatch, an online community for individuals who might be at risk of, or who know someone at risk of suicide.

METHODS

We used time series analysis to compare date and time stamps of 90,518 SuicideWatch posts from 1st December 2008 to 31st August 2015 to (i) 6,616,431 posts on the most commonly subscribed general subreddit, AskReddit and (ii) 66,934 of these AskReddit posts, which were posted by the SuicideWatch authors.

RESULTS

Mondays showed the highest proportion of posts on SuicideWatch. Clear diurnal variation was observed, with a peak in the early morning (2:00-5:00 h), and a subsequent decrease to a trough in late morning/early afternoon (11:00-14:00 h). Conversely, the highest volume of posts in the control data was between 20:00-23:00 h.

CONCLUSIONS

Posts on SuicideWatch occurred most frequently on Mondays: the day most associated with suicide risk. The early morning peak in SuicideWatch posts precedes the time of day during which suicide attempts and deaths most commonly occur. Further research of these weekly and diurnal rhythms should help target populations with support and suicide prevention interventions when needed most.

Mental Health Discourses on Twitter during Mental Health Awareness Week

Promoting health-related campaigns on Twitter has increasingly become a world-wide choice to raise awareness and disseminate health information. Data retrieved from Twitter are now being used to explore how users express their views, attitudes and personal experiences of health-related issues. We focused on Twitter discourse reproduced during Mental Health Awareness Week 2017 by examining 1,200 tweets containing the keywords 'mental health', 'mental illness', 'mental disorders' and '#MHAW'. The analysis revealed 'awareness and advocacy', 'stigmatization', and 'personal experience of mental health/illness' as the central discourses within the sample. The article concludes with some recommendations for future research on digitally-mediated health communication.

A Social Media Study on Mental Health Status Transitions Surrounding Psychiatric Hospitalizations

For people diagnosed with a mental illness, psychiatric hospitalization is one step in a long journey, consisting of clinical recovery such as removal of symptoms, and social reintegration involving resuming social roles and responsibilities, overcoming stigma and self-maintenance of the condition. Both clinical recovery and social reintegration need to go hand-in-hand for the overall well-being of individuals. However, research exploring social media for mental health has considered narrower, disjoint conceptualizations of people with mental illness - either as a patient or as a support-seeker. In this paper, we combine medical records with social media data of 254 consented individuals who have experienced a psychiatric hospitalization to address this gap. Adopting a theory-driven, Gaussian Mixture modeling approach, we provide a taxonomy of six heterogeneous behavioral patterns characterizing peoples' mental health status transitions around hospitalizations. Then we present an empirically derived framework, based on feedback from clinical researchers, to understand peoples' trajectories around clinical recovery and social reintegration. Finally, to demonstrate the utility of this taxonomy and the empirical framework, we assess social media signals that are indicative of individuals' reintegration trajectories post-hospitalization. We discuss the implications of combining peoples' clinical and social experiences in mental health care and the opportunities this intersection presents to post-discharge support and technology-based interventions for mental health.

"This show hits really close to home on so many levels": An analysis of Reddit comments about HBO's Euphoria to understand viewers' experiences of and reactions to substance use and mental illness

BACKGROUND

Public health has begun using social media forums such as Reddit to enhance surveillance and modernize interventions for young people. The current study's objective was to examine Reddit posts about the HBO series Euphoria to identify show themes that resonate with adolescent and young adult viewers in order to inform future social media interventions.

METHODS

Reddit comments in the r/television community from June to August 2019 were downloaded. Following filtering, 725 comments were analyzed and coded using a codebook and ATLAS.ti. Coded comments were analyzed for themes relevant to Redditor substance use, reactions to Euphoria and the main character (Rue), and mental health concerns.

RESULTS

During their discussion of the show, Redditors disclosed both personal recreational and prescription drug use, including substance use to cope with mental illness symptoms. There were approximately equal numbers of comments with positive and negative reactions to the show overall and to the main character, Rue. Redditors often found Euphoria's storyline and portrayed events to be relatable and realistic to the experience of young people who use drugs, as well as sometimes triggering. Overall, Redditors thought Rue accurately depicted an individual's struggle with a substance use disorder.

CONCLUSIONS

This exploratory study highlights how television and social media can contribute to young peoples' understanding of substance use disorders and mental health. Findings could inform the design of social media interventions for adolescents and young adults on a variety of substance use issues, including stigma and the interconnectedness of substance use and mental health challenges.

Examining the Relationship Between the Use of a Mobile Peer-Support App and Self-Injury Outcomes: Longitudinal Mixed Methods Study

BACKGROUND

Many individuals who self-injure seek support and information through online communities and mobile peer-support apps. Although researchers have identified risks and benefits of participation, empirical work linking participation in these web-based spaces to self-injury behaviors and thoughts is limited.

OBJECTIVE

This study aims to investigate the relationship between behavioral and linguistic traces on a mobile peer support app and self-injury outcomes.

METHODS

Natural use data and web-based surveys (N=697) assessing self-injury outcomes were collected from 268 users (aged 13-38 years; median 19; 149/268, 55.6% female) of a mobile peer-support app for 4 months. Participants were identified as having posted self-injury content using an internal classifier. Natural log data was used to predict self-injury outcomes in a series of multilevel logistic and linear regressions.

RESULTS

Greater engagement on a mobile peer-support app was associated with a decreased likelihood of self-injury thoughts (odds ratio [OR] 0.25, 95% CI 0.09-0.73) and fewer intentions to self-injure (b=-0.37, SE 0.09), whereas posting triggering content was associated with an increased likelihood of engaging in behaviors (OR 5.37, 95% CI 1.25-23.05) and having self-injury thoughts (OR 17.87, 95% CI 1.64-194.15). Moreover, viewing triggering content was related to both a greater ability to resist (b=1.39, SE 0.66) and a greater intention to self-injure (b=1.50, SE 0.06).

CONCLUSIONS

To our knowledge, this is the first study to connect naturally occurring log data to survey data assessing self-injury outcomes over time. This work provides empirical support for the relationship between participation in online forums and self-injury outcomes, and it articulates mechanisms contributing to this relationship.

Ethics and Privacy in Social Media Research for Mental Health

PURPOSE OF REVIEW

This review provides an overview of recent research which uses social media data in the context of mental health. It also provides an overview of challenges in relation to consent, privacy, and usage of such data.

RECENT FINDINGS

A broad range of research has been conducted in recent years, using text-based and visual data from social media platforms, for purposes such as risk detection at the individual level, providing crisis outreach, and developing a better understanding of the lived experience of mental ill-health. Challenges remain in relation to obtaining truly informed consent for research using social media data-however platforms allowing data donation may address these concerns. There is an imperative need to ensure that privacy is preserved at all stages of the research process, from data collection, to analysis, and the responsible use of raw data in publications.

Analysis of potential associations of JUUL flavours with health symptoms based on user-generated data from Reddit

BACKGROUND

The rise of the popular e-cigarette, JUUL, has been partly attributed to various teen-friendly e-liquid flavours offered. However, the possible health risks associated with each e-liquid flavour still remain unclear. This research focuses on the possible associations between JUUL flavours and health symptoms using social media data from Reddit.

METHODS

Keyword filtering was used to obtain 5,746 JUUL flavour-related posts and 7927 health symptom-related posts from June 2015 to April 2019 from Reddit. Posts from September 2016 to April 2019 were used to conduct temporal analysis for nine health symptom categories and the 8 JUUL flavours. Finally, associations between the JUUL flavours and health symptom categories were examined on the user level using generalised estimating equation models.

RESULTS

According to our temporal analysis, Mango and Mint were the most discussed JUUL flavours on Reddit. Respiratory and throat symptoms were the most discussed health issues together with JUUL on Reddit over time. Respiratory symptoms had potential associations with the Mango, Mint and Fruit JUUL flavours. Digestive symptoms had a potential association with the Crème flavour, psychological symptoms had a potential association with the Cucumber flavour, and cardiovascular symptoms had a potential association with the tobacco flavours.

CONCLUSIONS

Mango and Mint were the two most mentioned JUUL flavours on Reddit. Certain JUUL flavours were more likely to be mentioned together with certain categories of health symptoms by the same Reddit users. Our findings could prompt further medical research into the health symptoms associated with different e-liquid flavours.

Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis

BACKGROUND

Mental disorders have become a major concern in public health, and they are one of the main causes of the overall disease burden worldwide. Social media platforms allow us to observe the activities, thoughts, and feelings of people's daily lives, including those of patients suffering from mental disorders. There are studies that have analyzed the influence of mental disorders, including depression, in the behavior of social media users, but they have been usually focused on messages written in English.

OBJECTIVE

The study aimed to identify the linguistic features of tweets in Spanish and the behavioral patterns of Twitter users who generate them, which could suggest signs of depression.

METHODS

This study was developed in 2 steps. In the first step, the selection of users and the compilation of tweets were performed. A total of 3 datasets of tweets were created, a depressive users dataset (made up of the timeline of 90 users who explicitly mentioned that they suffer from depression), a depressive tweets dataset (a manual selection of tweets from the previous users, which included expressions indicative of depression), and a control dataset (made up of the timeline of 450 randomly selected users). In the second step, the comparison and analysis of the 3 datasets of tweets were carried out.

RESULTS

In comparison with the control dataset, the depressive users are less active in posting tweets, doing it more frequently between 23:00 and 6:00 (P<.001). The percentage of nouns used by the control dataset almost doubles that of the depressive users (P<.001). By contrast, the use of verbs is more common in the depressive users dataset (P<.001). The first-person singular pronoun was by far the most used in the depressive users dataset (80%), and the first- and the second-person plural pronouns were the least frequent (0.4% in both cases), this distribution being different from that of the control dataset (P<.001). Emotions related to sadness, anger, and disgust were more common in the depressive users and depressive tweets datasets, with significant differences when comparing these datasets with the control dataset (P<.001). As for negation words, they were detected in 34% and 46% of tweets in among depressive users and in depressive tweets, respectively, which are significantly different from the control dataset (P<.001). Negative polarity was more frequent in the depressive users (54%) and depressive tweets (65%) datasets than in the control dataset (43.5%; P<.001).

CONCLUSIONS

Twitter users who are potentially suffering from depression modify the general characteristics of their language and the way they interact on social media. On the basis of these changes, these users can be monitored and supported, thus introducing new opportunities for studying depression and providing additional health care services to people with this disorder.

The Channel Matters: Self-disclosure, Reciprocity and Social Support in Online Cancer Support Groups

People with health concerns go to online health support groups to obtain help and advice. To do so, they frequently disclose personal details, many times in public. Although research in non-health settings suggests that people self-disclose less in public than in private, this pattern may not apply to health support groups where people want to get relevant help. Our work examines how the use of private and public channels influences members' self-disclosure in an online cancer support group, and how channels moderate the influence of self-disclosure on reciprocity and receiving support. By automatically measuring people's self-disclosure at scale, we found that members of cancer support groups revealed more negative self-disclosure in the public channels compared to the private channels. Although one's self-disclosure leads others to self-disclose and to provide support, these effects were generally stronger in the private channel. These channel effects probably occur because the public channels are the primary venue for support exchange, while the private channels are mainly used for follow-up conversations. We discuss theoretical and practical implications of our work.

Social Connection and Online Engagement: Insights From Interviews With Users of a Mental Health Online Forum

BACKGROUND

Over the past 2 decades, online forums for mental health support have emerged as an important tool for improving mental health and well-being. There has been important research that analyzes the content of forum posts, studies on how and why individuals engage with forums, and how extensively forums are used. However, we still lack insights into key questions on how they are experienced from the perspective of their users, especially those in rural and remote settings.

OBJECTIVE

The aim of our study was to investigate the dynamics, benefits, and challenges of a generalized peer-to-peer mental health online forum from a user perspective; in particular, to better explore and understand user perspectives on connection, engagement, and support offered in such forums; information and advice they gained; and what issues they encountered. We studied experiences of the forums from the perspective of both people with lived experience of mental illness and people who care for people with mental illness.

METHODS

To understand the experience of forum users, we devised a qualitative study utilizing semistructured interviews with 17 participants (12 women and 5 men). Data were transcribed, and a thematic analysis was undertaken.

RESULTS

The study identified 3 key themes: participants experienced considerable social and geographical isolation, which the forums helped to address; participants sought out the forums to find a social connection that was lacking in their everyday lives; and participants used the forums to both find and provide information and practical advice.

CONCLUSIONS

The study suggests that online peer support provides a critical, ongoing role in providing social connection for people with a lived experience of mental ill-health and their carers, especially for those living in rural and remote areas. Forums may offer a way for individuals to develop their own understanding of recovery through reflecting on the recovery experiences and peer support shown by others and individuals enacting peer support themselves. Key to the success of this online forum was the availability of appropriate moderation, professional support, and advice.

Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior

Risk assessment of suicidal behavior is a time-consuming but notoriously inaccurate activity for mental health services globally. In the last 50 years a large number of tools have been designed for suicide risk assessment, and tested in a wide variety of populations, but studies show that these tools suffer from low positive predictive values. More recently, advances in research fields such as machine learning and natural language processing applied on large datasets have shown promising results for health care, and may enable an important shift in advancing precision medicine. In this conceptual review, we discuss established risk assessment tools and examples of novel data-driven approaches that have been used for identification of suicidal behavior and risk. We provide a perspective on the strengths and weaknesses of these applications to mental health-related data, and suggest research directions to enable improvement in clinical practice.

Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances

The importance of incorporating Natural Language Processing (NLP) methods in clinical informatics research has been increasingly recognized over the past years, and has led to transformative advances. Typically, clinical NLP systems are developed and evaluated on word, sentence, or document level annotations that model specific attributes and features, such as document content (e.g., patient status, or report type), document section types (e.g., current medications, past medical history, or discharge summary), named entities and concepts (e.g., diagnoses, symptoms, or treatments) or semantic attributes (e.g., negation, severity, or temporality). From a clinical perspective, on the other hand, research studies are typically modelled and evaluated on a patient- or population-level, such as predicting how a patient group might respond to specific treatments or patient monitoring over time. While some NLP tasks consider predictions at the individual or group user level, these tasks still constitute a minority. Owing to the discrepancy between scientific objectives of each field, and because of differences in methodological evaluation priorities, there is no clear alignment between these evaluation approaches. Here we provide a broad summary and outline of the challenging issues involved in defining appropriate intrinsic and extrinsic evaluation methods for NLP research that is to be used for clinical outcomes research, and vice versa. A particular focus is placed on mental health research, an area still relatively understudied by the clinical NLP research community, but where NLP methods are of notable relevance. Recent advances in clinical NLP method development have been significant, but we propose more emphasis needs to be placed on rigorous evaluation for the field to advance further. To enable this, we provide actionable suggestions, including a minimal protocol that could be used when reporting clinical NLP method development and its evaluation.

For Better or for Worse? A Systematic Review of the Evidence on Social Media Use and Depression Among Lesbian, Gay, and Bisexual Minorities

BACKGROUND

Over 90% of adults in the United States have at least one social media account, and lesbian, gay, and bisexual (LGB) persons are more socially active on social media than heterosexuals. Rates of depression among LGB persons are between 1.5- and 2-fold higher than those among their heterosexual counterparts. Social media allows users to connect, interact, and express ideas, emotions, feelings, and thoughts. Thus, social media use might represent both a protective and a risk factor for depression among LGB persons. Studying the nature of the relationship between social media use and depression among LGB individuals is a necessary step to inform public health interventions for this population.

OBJECTIVE

The objective of this systematic review was to synthesize and critique the evidence on social media use and depression among LGB populations.

METHODS

We conducted a literature search for quantitative and qualitative studies published between January 2003 and June 2017 using 3 electronic databases. Articles were included if they were peer-reviewed, were in English, assessed social media use either quantitatively or qualitatively, measured depression, and focused on LGB populations. A minimum of two authors independently extracted data from each study using an a priori developed abstraction form. We assessed appropriate reporting of studies using the Strengthening the Reporting of Observational Studies in Epidemiology and the Consolidated Criteria for Reporting Qualitative Research for quantitative and qualitative studies, respectively.

RESULTS

We included 11 articles in the review; 9 studies were quantitative and cross-sectional and 2 were qualitative. Appropriate reporting of results varied greatly. Across quantitative studies, we found heterogeneity in how social media use was defined and measured. Cyberbullying was the most studied social media experience and was associated with depression and suicidality. Qualitative studies found that while social media provides a space to disclose minority experiences and share ways to cope and get support, constant surveillance of one's social media profile can become a stressor, potentially leading to depression. In most studies, sexual minority participants were identified inconsistently.

CONCLUSIONS

This review supports the need for research on the role of social media use on depression outcomes among LBG persons. Using social media may be both a protective and a risk factor for depression among LGB individuals. Support gained via social media may buffer the impact of geographic isolation and loneliness. Negative experiences such as cyberbullying and other patterns of use may be associated with depression. Future research would benefit from more consistent definitions of both social media use and study populations. Moreover, use of larger samples and accounting for patterns of use and individuals' experiences on social media may help better understand the factors that impact LGB mental health disparities.

Understanding emerging forms of cannabis use through an online cannabis community: An analysis of relative post volume and subjective highness ratings

BACKGROUND

Legalization of medical and recreational cannabis has coincided with an increase in novel forms of cannabis use and a burgeoning cannabis product industry. This research seeks to understand the occurrence of discussions about these emerging and traditional forms of use in an online social media discussion forum.

METHODS

We analyzed posts to a cannabis-specific forum on the Reddit social media platform posted from January 2010-December 2016. For each of various keywords describing smoking, vaping, edibles, dabbing, and butane hash oil (BHO) concentrate use, we analyzed (1) relative prevalence of posts mentioning these cannabis forms of use; (2) user-reported subjective ratings of "highness" on a scale of 1-10; (3) the ten most common words mentioned in posts; and (4) the frequency of adverse health effect terms.

RESULTS

Form of use was mentioned in approximately 17.7% of 2.26 million posts; smoking was the most commonly mentioned form of cannabis use. From 2010-2016, relative post volume increased significantly for posts mentioning dabbing (3.63/1000 additional posts per year, p < .001), butane hash oil terms (3.16/1000, p < .001), and edible terms (2.84/1000, p = .002). Mean subjective highness was significantly greater for posts mentioning dabbing (mean = 7.8, p < .001), butane hash oil terms (mean = 7.5, p < .001), and edible terms (mean = 7.2, p < .001) but not significantly different for vaping (mean = 6.7, p = .19), when compared to smoking (mean = 6.8).

CONCLUSIONS

Despite limitations in representativeness, findings indicate a significant increase in online discussion of emerging cannabis forms of use over time and greater subjective effects of dabbing, butane hash oil, and edible use.

Analyzing hidden populations online: topic, emotion, and social network of HIV-related users in the largest Chinese online community

BACKGROUND

Traditional survey methods are limited in the study of hidden populations due to the hard to access properties, including lack of a sampling frame, sensitivity issue, reporting error, small sample size, etc. The rapid increase of online communities, of which members interact with others via the Internet, have generated large amounts of data, offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. In this study, we try to understand the multidimensional characteristics of a hidden population by analyzing the massive data generated in the online community.

METHODS

By elaborately designing crawlers, we retrieved a complete dataset from the "HIV bar," the largest bar related to HIV on the Baidu Tieba platform, for all records from January 2005 to August 2016. Through natural language processing and social network analysis, we explored the psychology, behavior and demand of online HIV population and examined the network community structure.

RESULTS

In HIV communities, the average topic similarity among members is positively correlated to network efficiency (r = 0.70, p < 0.001), indicating that the closer the social distance between members of the community, the more similar their topics. The proportion of negative users in each community is around 60%, weakly correlated with community size (r = 0.25, p = 0.002). It is found that users suspecting initial HIV infection or first in contact with high-risk behaviors tend to seek help and advice on the social networking platform, rather than immediately going to a hospital for blood tests.

CONCLUSIONS

Online communities have generated copious amounts of data offering new opportunities for understanding hidden populations with unprecedented sample sizes and richness of information. It is recommended that support through online services for HIV/AIDS consultation and diagnosis be improved to avoid privacy concerns and social discrimination in China.

A Social Media Based Index of Mental Well-Being in College Campuses

Psychological distress in the form of depression, anxiety and other mental health challenges among college students is a growing health concern. Dearth of accurate, continuous, and multi-campus data on mental well-being presents significant challenges to intervention and mitigation efforts in college campuses. We examine the potential of social media as a new "barometer" for quantifying the mental well-being of college populations. Utilizing student-contributed data in Reddit communities of over 100 universities, we first build and evaluate a transfer learning based classification approach that can detect mental health expressions with 97% accuracy. Thereafter, we propose a robust campus-specific Mental Well-being Index: MWI. We find that MWI is able to reveal meaningful temporal patterns of mental well-being in campuses, and to assess how their expressions relate to university attributes like size, academic prestige, and student demographics. We discuss the implications of our work for improving counselor efforts, and in the design of tools that can enable better assessment of the mental health climate of college campuses.

Social Media as It Interfaces with Psychosocial Development and Mental Illness in Transitional Age Youth

For transitional age individuals, social media (SM) is an integral component of connecting with others. There are 2 billion SM users worldwide. SM users may experience an increase in perceived social support and life satisfaction. Use of SM may facilitate forming connections among people with potentially stigmatizing mental disorders. However, epidemiologic studies suggest that increased SM use is associated with conditions such as depression, anxiety, and sleep disturbance. Future research should examine directionality of these associations and the role of contextual factors. It also will be useful to leverage SM to provide mental health care and surveillance of mental health concerns.

Characterisation of mental health conditions in social media using Informed Deep Learning

The number of people affected by mental illness is on the increase and with it the burden on health and social care use, as well as the loss of both productivity and quality-adjusted life-years. Natural language processing of electronic health records is increasingly used to study mental health conditions and risk behaviours on a large scale. However, narrative notes written by clinicians do not capture first-hand the patients' own experiences, and only record cross-sectional, professional impressions at the point of care. Social media platforms have become a source of 'in the moment' daily exchange, with topics including well-being and mental health. In this study, we analysed posts from the social media platform Reddit and developed classifiers to recognise and classify posts related to mental illness according to 11 disorder themes. Using a neural network and deep learning approach, we could automatically recognise mental illness-related posts in our balenced dataset with an accuracy of 91.08% and select the correct theme with a weighted average accuracy of 71.37%. We believe that these results are a first step in developing methods to characterise large amounts of user-generated content that could support content curation and targeted interventions.

Identifying Topics for E-Cigarette User-Generated Contents: A Case Study From Multiple Social Media Platforms

Background

Electronic cigarette (e-cigarette) is an emerging product with a rapid-growth market in recent years. Social media has become an important platform for information seeking and sharing. We aim to mine hidden topics from e-cigarette datasets collected from different social media platforms.

Objective

This paper aims to gain a systematic understanding of the characteristics of various types of social media, which will provide deep insights into how consumers and policy makers effectively use social media to track e-cigarette-related content and adjust their decisions and policies.

Methods

We collected data from Reddit (27,638 e-cigarette flavor-related posts from January 1, 2011, to June 30, 2015), JuiceDB (14,433 e-juice reviews from June 26, 2013 to November 12, 2015), and Twitter (13,356 “e-cig ban”-related tweets from January, 1, 2010 to June 30, 2015). Latent Dirichlet Allocation, a generative model for topic modeling, was used to analyze the topics from these data.

Results

We found four types of topics across the platforms: (1) promotions, (2) flavor discussions, (3) experience sharing, and (4) regulation debates. Promotions included sales from vendors to users, as well as trades among users. A total of 10.72% (2,962/27,638) of the posts from Reddit were related to trading. Promotion links were found between social media platforms. Most of the links (87.30%) in JuiceDB were related to Reddit posts. JuiceDB and Reddit identified consistent flavor categories. E-cigarette vaping methods and features such as steeping, throat hit, and vapor production were broadly discussed both on Reddit and on JuiceDB. Reddit provided space for policy discussions and majority of the posts (60.7%) holding a negative attitude toward regulations, whereas Twitter was used to launch campaigns using certain hashtags. Our findings are based on data across different platforms. The topic distribution between Reddit and JuiceDB was significantly different (P<.001), which indicated that the user discussions focused on different perspectives across the platforms.

Conclusions

This study examined Reddit, JuiceDB, and Twitter as social media data sources for e-cigarette research. These mined findings could be further used by other researchers and policy makers. By utilizing the automatic topic-modeling method, the proposed unified feedback model could be a useful tool for policy makers to comprehensively consider how to collect valuable feedback from social media.

Analysis of symptoms and their potential associations with e-liquids' components: a social media study

Background

The electronic cigarette (e-cigarette) market has grown rapidly in recent years. However, causes of e-cigarette related symptoms among users and their impact on health remain uncertain. This research aims to mine the potential relationships between symptoms and e-liquid components, such as propylene glycol (PG), vegetable glycerine (VG), flavor extracts, and nicotine, using user-generated data collected from Reddit.

Methods

A total of 3605 e-liquid related posts from January 1st, 2011 to June 30th, 2015 were collected from Reddit. Then the patterns of VG/PG distribution among different flavors were analyzed. Next, the relationship between throat hit, which was a typical symptom of e-cigarette use, and e-liquid components was studied. Finally, other symptoms were examined based on e-liquid components and user sentiment.

Results

We discovered 3 main sets of findings: 1) We identified three groups of flavors in terms of VG/PG ratios. Fruits, cream, and nuts flavors were similar. Sweet, menthol, and seasonings flavors were classified into one group. Tobacco and beverages flavors were the third group. 2) Throat hit was analyzed and we found that menthol and tobacco flavors, as well as high ratios of PG and nicotine level, could produce more throat hit. 3) A total of 9 systems of 25 symptoms were identified and analyzed. Components including VG/PG ratio, flavor, and nicotine could be possible reasons for these symptoms.

Conclusions

E-liquid components shown to be associated with e-cigarette use symptomology were VG/PG ratios, flavors, and nicotine levels. Future analysis could be conducted based on the structure of e-liquid components categories built in this study. Information revealed in this study could be utilized by e-cigarette users to understand the relationship between e-liquid type and symptoms experienced, by vendors to choose appropriate recipes of e-liquid, and by policy makers to develop new regulations.

Discovering Shifts to Suicidal Ideation from Mental Health Content in Social Media

History of mental illness is a major factor behind suicide risk and ideation. However research efforts toward characterizing and forecasting this risk is limited due to the paucity of information regarding suicide ideation, exacerbated by the stigma of mental illness. This paper fills gaps in the literature by developing a statistical methodology to infer which individuals could undergo transitions from mental health discourse to suicidal ideation. We utilize semi-anonymous support communities on Reddit as unobtrusive data sources to infer the likelihood of these shifts. We develop language and interactional measures for this purpose, as well as a propensity score matching based statistical approach. Our approach allows us to derive distinct markers of shifts to suicidal ideation. These markers can be modeled in a prediction framework to identify individuals likely to engage in suicidal ideation in the future. We discuss societal and ethical implications of this research.

An Examination of Electronic Cigarette Content on Social Media: Analysis of E-Cigarette Flavor Content on Reddit

In recent years, the emerging electronic cigarette (e-cigarette) marketplace has shown great development prospects all over the world. Reddit, one of the most popular forums in the world, has a very large user group and thus great influence. This study aims to gain a systematic understanding of e-cigarette flavors based on data collected from Reddit. Flavor popularity, mixing, characteristics, trends, and brands are analyzed. Fruit flavors were mentioned the most (n = 15,720) among all the posts and were among the most popular flavors (n = 2902) used in mixed blends. Strawberry and vanilla flavors were the most popular for e-juice mixing. The number of posts discussing e-cigarette flavors has increased sharply since 2014. Mt. Baker Vapor and Hangen were the most popular brands discussed among users. Information posted on Reddit about e-cigarette flavors reflected consumers' interest in a variety of flavors. Our findings suggest that Reddit could be used for data mining and analysis of e-cigarette-related content. Understanding how e-cigarette consumers' view and utilize flavors within their vaping experience and how producers and marketers use social media to promote flavors and sell products could provide valuable information for regulatory decision-makers.