Longitudinal Changes in Psychological States in Online Health Community Members: Understanding the Long-Term Effects of Participating in an Online Depression Community

Predicting negative attitudes towards suicide in social media texts: prediction model development and validation study

Background

Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigorous external validation. By analyzing a large-scale social media dataset (Sina Weibo), this paper aims to fully cover varied forms of negative attitudes and develop a classification model for predicting negative attitudes as a whole, and then to externally validate its performance on population and individual levels.

Methods

938,866 Weibo posts with relevant keywords were downloaded, including 737,849 posts updated between 2009 and 2014 (2009-2014 dataset), and 201,017 posts updated between 2015 and 2020 (2015-2020 dataset). (1) For model development, based on 10,000 randomly selected posts from 2009 to 2014 dataset, a human-based content analysis was performed to manually determine labels of each post (non-negative or negative attitudes). Then, a computer-based content analysis was conducted to automatically extract psycholinguistic features from each of the same 10,000 posts. Finally, a classification model for predicting negative attitudes was developed on selected features. (2) For model validation, on the population level, the developed model was implemented on remaining 727,849 posts from 2009 to 2014 dataset, and was externally validated by comparing proportions of negative attitudes between predicted and human-coded results. Besides, on the individual level, similar analyses were performed on 300 randomly selected posts from 2015 to 2020 dataset, and the developed model was externally validated by comparing labels of each post between predicted and actual results.

Results

For model development, the F1 and area under ROC curve (AUC) values reached 0.93 and 0.97. For model validation, on the population level, significant differences but very small effect sizes were observed for the whole sample (χ 2 1 = 32.35, p < 0.001; Cramer's V = 0.007, p < 0.001), men (χ 2 1 = 9.48, p = 0.002; Cramer's V = 0.005, p = 0.002), and women (χ 2 1 = 25.34, p < 0.001; Cramer's V = 0.009, p < 0.001). Besides, on the individual level, the F1 and AUC values reached 0.76 and 0.74.

Conclusion

This study demonstrates the efficiency and necessity of machine learning prediction of negative attitudes as a whole, and confirms that external validation is essential before implementing prediction models into practice.

Natural language sentiment as an indicator of depression and anxiety symptoms: a longitudinal mixed methods study1

The study tested how the use of positive- (e.g. beautiful) and negative-valenced (e.g. horrible) words in natural language and its change in time affects the severity of depression and anxiety symptoms among depressed and non-depressed individuals. This longitudinal mixed methods study (N = 40 participants, n = 1440 narratives) with three measurements within a year showed that at the between-person level the use of negative-valenced words was strongly associated with the increase in anxiety and depression symptoms over time while the use of positive-valenced words was slightly associated with the decrease in anxiety and depression symptom. These effects were not supported for within-person level (i.e. changes in word usage). No significant differences were observed in the effects between depressed and non-depressed groups. Summing up, the overall use of positive- and negative-valenced words (particularly negative-valenced words) had a stronger effect on the severity of psychopathological symptoms than their change over time. The results were discussed in the context of natural language processing and its application in diagnosing depression and anxiety symptoms.

Leveraging stylometry analysis to identify unique characteristics of peer support user groups in online mental health forums

Online peer support mental health forums provide an effective and accessible form of support, augmenting scarce clinical and face-to-face assistance. However, to enhance their effectiveness, it is essential to understand the unique characteristics of peer support user groups, and how they participate, contribute and communicate in these forums. This paper proposes and tests a novel approach that leverages stylometry analysis to uncover the unique characteristics of peer support user groups in such forums. Our approach identifies how each group empowers and supports other users, and what distinguishes them from others. The analysis shows that emotion-related words are crucial in identifying and distinguishing user groups based on their writing style. Comparative analysis of emotion expressions across user groups also uncovers the significance of emotional content in these forums in promoting mental well-being. Valued 'senior contributors' were more likely than all other groups including trained community guides to use a wide range of both positive and negative emotions in their posts. These findings have significant implications for improving the training of peer-mentors and moderators, scaling forum services, and improving guidelines for emotional expression among peer support users. Our approach presents an objective approach to differentiating the characteristics and communication patterns of valued senior contributors, mentors, and guides, enabling service providers to foster the kinds of communication that supports positive outcomes for distressed users.

Posting patterns in peer online support forums and their associations with emotions and mood in bipolar disorder: Exploratory analysis

BACKGROUND

Mental health (MH) peer online forums offer robust support where internet access is common, but healthcare is not, e.g., in countries with under-resourced MH support, rural areas, and during pandemics. Despite their widespread use, little is known about who posts in such forums, and in what mood states. The discussion platform Reddit is ideally suited to study this as it hosts forums (subreddits) for MH and non-MH topics. In bipolar disorder (BD), where extreme mood states are core defining features, mood influences are particularly relevant.

OBJECTIVES

This exploratory study investigated posting patterns of Reddit users with a self-reported BD diagnosis and the associations between posting and emotions, specifically: 1) What proportion of the identified users posts in MH versus non-MH subreddits? 2) What differences exist in the emotions that they express in MH or non-MH subreddit posts? 3) How does mood differ between those users who post in MH subreddits compared to those who only post in non-MH subreddits?

METHODS

Reddit users were automatically identified via self-reported BD diagnosis statements and all their 2005-2019 posts were downloaded. First, the percentages of users who posted only in MH (non-MH) subreddits were calculated. Second, affective vocabulary use was compared in MH versus non-MH subreddits by measuring the frequency of words associated with positive emotions, anxiety, sadness, anger, and first-person singular pronouns via the LIWC text analysis tool. Third, a logistic regression distinguished users who did versus did not post in MH subreddits, using the same LIWC variables (measured from users' non-MH subreddit posts) as predictors, controlling for age, gender, active days, and mean posts/day.

RESULTS

1) Two thirds of the identified 19,685 users with a self-reported BD diagnosis posted in both MH and non-MH subreddits. 2) Users who posted in both MH and non-MH subreddits exhibited less positive emotion but more anxiety and sadness and used more first-person singular pronouns in their MH subreddit posts. 3) Feminine gender, higher positive emotion, anxiety, and sadness were significantly associated with posting in MH subreddits.

CONCLUSIONS

Many Reddit users who disclose a BD diagnosis use a single account to discuss MH and other concerns. Future work should determine whether users exhibit more anxiety and sadness in their MH subreddit posts because they more readily post in MH subreddits when experiencing lower mood or because they feel more able to express negative emotions in these spaces. MH forums may reflect the views of people who experience more extreme mood (outside of MH subreddits) compared to people who do not post in MH subreddits. These findings can be useful for MH professionals to discuss online forums with their clients. For example, they may caution them that forums may underrepresent people living well with BD.

Cannabis and male sexual health: contemporary qualitative review and insight into perspectives of young men on the internet

INTRODUCTION

Cannabis use is increasing across the United States, yet its short- and long-term effects on sexual function remain controversial. Currently, there is a paucity of studies exploring the relationship between cannabis and men's health.

OBJECTIVES

To summarize the available literature on cannabis and men's health and provide insight into lay perceptions of this topic.

METHODS

We performed a qualitative PubMed review of the existing literature on cannabis and men's health according to the PRISMA guidelines. Separately, we analyzed relevant themes in online men's health forums. We utilized a Google cloud-based platform (BigQuery) to extract relevant posts from 5 men's health Reddit forums from August 2018 to August 2019. We conducted a qualitative thematic analysis of the posts and quantitatively analyzed them using natural language processing and a meaning extraction method with principal component analysis.

RESULTS

Our literature review revealed a mix of animal and human studies demonstrating the negative effects of cannabis on semen parameters and varying effects on erectile function and hormone levels. In our analysis of 372 686 Reddit posts, 1190 (0.3%) included relevant discussion on cannabis and men's health. An overall 272 posts were manually analyzed, showing that online discussions revolve around seeking answers and sharing the effects of cannabis on various aspects of sexual health and quality of life, often with conflicting experiences. Quantitative analysis revealed 1 thematic cluster related to cannabis, insecurity, and mental/physical health.

CONCLUSIONS

There is a limited number of quality human studies investigating the effects of cannabis on men's health. Men online are uncertain about how cannabis affects their sexual health and seek more information. As the prevalence of cannabis use increases, so does the need for research in this area.

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.

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

Background

Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging.

Objective

The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit.

Methods

We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19–related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country.

Results

Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19–related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits.

Conclusions

This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches.

Resilience in Web-Based Mental Health Communities: Building a Resilience Dictionary With Semiautomatic Text Analysis

Background

Resilience is an accepted strengths-based concept that responds to change, adversity, and crises. This concept underpins both personal and community-based preventive approaches to mental health issues and shapes digital interventions. Online mental health peer-support forums have played a prominent role in enhancing resilience by providing accessible places for sharing lived experiences of mental issues and finding support. However, little research has been conducted on whether and how resilience is realized, hindering service providers’ ability to optimize resilience outcomes.

Objective

This study aimed to create a resilience dictionary that reflects the characteristics and realization of resilience within online mental health peer-support forums. The findings can be used to guide further analysis and improve resilience outcomes in mental health forums through targeted moderation and management.

Methods

A semiautomatic approach to creating a resilience dictionary was proposed using topic modeling and qualitative content analysis. We present a systematic 4-phase analysis pipeline that preprocesses raw forum posts, discovers core themes, conceptualizes resilience indicators, and generates a resilience dictionary. Our approach was applied to a mental health forum run by SANE (Schizophrenia: A National Emergency) Australia, with 70,179 forum posts between 2018 and 2020 by 2357 users being analyzed.

Results

The resilience dictionary and taxonomy developed in this study, reveal how resilience indicators (ie, “social capital,” “belonging,” “learning,” “adaptive capacity,” and “self-efficacy”) are characterized by themes commonly discussed in the forums; each theme’s top 10 most relevant descriptive terms and their synonyms; and the relatedness of resilience, reflecting a taxonomy of indicators that are more comprehensive (or compound) and more likely to facilitate the realization of others. The study showed that the resilience indicators “learning,” “belonging,” and “social capital” were more commonly realized, and “belonging” and “learning” served as foundations for “social capital” and “adaptive capacity” across the 2-year study period.

Conclusions

This study presents a resilience dictionary that improves our understanding of how aspects of resilience are realized in web-based mental health forums. The dictionary provides novel guidance on how to improve training to support and enhance automated systems for moderating mental health forum discussions.

Exploring Users' Health Behavior Changes in Online Health Communities: Heuristic-Systematic Perspective Study

(1) Background: With the continuous advancement of internet technology, use of the internet along with medical service provides a new solution to solve the shortage of medical resources and the uneven distribution of available resources. Online health communities (OHCs) that emerged at this historical moment have flourished with various advantages, such as being free from location and time constraints. Understanding users' behavior changes via engagement in OHCs is necessary to support the development of internet medicine and promote public health. (2) Methods: The hypotheses of our research model were developed based on the protective action decision model (PADM) and heuristic-systematic model (HSM). A questionnaire was developed with seven constructs through previous studies and verified using a presurvey. Our survey respondents are online health community users. We used structural equation modelling to test the research hypotheses. (3) Results: The results of the analysis of 290 valid samples showed that the research model fit the data collected well. The perceived benefits (PB) positively affect information needs (IN) (beta = 0.280, p < 0.001, R² = 0.252), thereby promoting users' engagement in OHCs (EOHCs) (beta = 0.353, p < 0.001, R² = 0.387); EOHCs has a significant positive impact on health behavior change (HBC) (beta = 0.314, p < 0.001), and it also significantly positively affects users' health behavior change through systematic processing indirectly (beta = 0.252, p < 0.001, R² = 0.387). (4) Conclusions: Our study offers support for the usefulness of the PADM and HSM in explaining users' health behavior changes. For practitioners, this study introduces influence processes as policy tools that managers can employ for health-promoting with mHealth.

Tweets Related to Motivation and Physical Activity for Obesity-Related Behavior Change: Descriptive Analysis

BACKGROUND

Obesity is one of the greatest modern public health problems, due to the associated health and economic consequences. Decreased physical activity is one of the main societal changes driving the current obesity pandemic.

OBJECTIVE

Our goals are to fill a gap in the literature and study whether users organically utilize a social media platform, Twitter, for providing motivation. We examine the topics of messages and social network structures on Twitter. We discuss social media's potential for providing peer support and then draw insights to inform the development of interventions for long-term health-related behavior change.

METHODS

We examined motivational messages related to physical activity on Twitter. First, we collected tweets related to physical activity. Second, we analyzed them using (1) a lexicon-based approach to extract and characterize motivation-related tweets, (2) a thematic analysis to examine common themes in retweets, and (3) topic models to understand prevalent factors concerning motivation and physical activity on Twitter. Third, we created 2 social networks to investigate organically arising peer-support network structures for sustaining physical activity and to form a deeper understanding of the feasibility of these networks in a real-world context.

RESULTS

We collected over 1.5 million physical activity-related tweets posted from August 30 to November 6, 2018. A relatively small percentage of the tweets mentioned the term motivation; many of these were made on Mondays or during morning or late morning hours. The analysis of retweets showed that the following three themes were commonly conveyed on the platform: (1) using a number of different types of motivation (self, process, consolation, mental, or quotes), (2) promoting individuals or groups, and (3) sharing or requesting information. Topic models revealed that many of these users were weightlifters or people trying to lose weight. Twitter users also naturally forged relations, even though 98.12% (2824/2878) of these users were in different physical locations.

CONCLUSIONS

This study fills a knowledge gap on how individuals organically use social media to encourage and sustain physical activity. Elements related to peer support are found in the organic use of social media. Our findings suggest that geographical location is less important for providing peer support as long as the support provides motivation, despite users having few factors in common (eg, the weather) affecting their physical activity. This presents a unique opportunity to identify successful motivation-providing peer support groups in a large user base. However, further research on the effects in a real-world context, as well as additional design and usability features for improving user engagement, are warranted to develop a successful intervention counteracting the current obesity pandemic. This is especially important for young adults, the main user group for social media, as they develop lasting health-related behaviors.

Do Informational and Emotional Elements Differ between Online Psychological and Physiological Disease Communities in China? A Comparative Study of Depression and Diabetes

Disease-specific online health communities provide a convenient and common platform for patients to share experiences, change information, provide and receive social support. This study aimed to compare differences between online psychological and physiological disease communities in topics, sentiment, participation, and emotional contagion patterns using multiple methods as well as to discuss how to satisfy the users' different informational and emotional needs. We chose the online depression and diabetes communities on the Baidu Tieba platform as the data source. Topic modeling and theme coding were employed to analyze discussion preferences for various topic categories. Sentiment analysis was used to identify the sentiment polarity of each post and comment. The social network was used to represent the users' interaction and emotional flows to discover the differences in participation and emotional contagion patterns between psychological and physiological disease communities. The results revealed that people affected by depression focused more on their symptoms and social relationships, while people affected by diabetes were more likely to discuss treatment and self-management behavior. In the depression community, there were obvious interveners spreading positive emotions and more core users in the negative emotional contagion network. In the diabetes community, emotional contagion was less prevalent and core users in positive and negative emotional contagion networks were basically the same. The study reveals insights into the differences between online psychological and physiological disease communities, providing a greater understanding of the users' informational and emotional needs expressed online. These results are helpful for society to provide actual medical assistance and deploy health interventions based on disease types.

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.

Emotional Contagion in the Online Depression Community

Negative emotions are prevalent in the online depression community (ODC), which potentially puts members at risk, according to the theory of emotional contagion. However, emotional contagion in the ODC has not been confirmed. The generalized estimating equation (GEE) was used to verify the extent of emotional contagion using data from 1548 sample users in China's popular ODC. During interaction, the emotional themes were analyzed according to language use. The diurnal patterns of the interaction behaviors were also analyzed. We identified the susceptible groups and analyzed their characteristics. The results confirmed the occurrence of emotional contagion in ODC, that is, the extent to which the user's emotion was affected by the received emotion. Our study also found that when positive emotional contagion occurred, the replies contained more hopefulness, and when negative emotional contagion occurred, the replies contained more hopelessness and fear. Second, positive emotions were easier to spread, and people with higher activity in ODC were more susceptible. In addition, nighttime was an active period for user interaction. The results can help community managers and support groups take measures to promote the spread of positive emotions and reduce the spread of negative emotions.

Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing

INTRODUCTION

The relationship between cannabis, tobacco, and vaping devices is both rapidly changing and poorly understood, with consumers rapidly shifting between use of all three product types. Given this dynamic and evolving landscape, there is an urgent need to monitor and better understand co-use, dual-use, and transition patterns between these products. This study describes work that utilizes social media - in this case, Reddit - in conjunction with automated Natural Language Processing (NLP) methods to better understand cannabis, tobacco, and vaping device product usage patterns.

METHODS

We collected Reddit data from the period 2013-2018, sourced from eight popular, high-volume Reddit communities (subreddits) related to the three product categories. We then manually annotated (coded) a set of 2640 Reddit posts and trained a machine learning-based NLP algorithm to automatically identify and disambiguate between cannabis or tobacco mentions (both smoking and vaping) in Reddit posts. This classifier was then applied to all data derived from the eight subreddits, 767,788 posts in total.

RESULTS

The NLP algorithm achieved an overall moderate performance (overall F-score of 0.77). When applied to our large corpus of Reddit posts, we discovered that over 10% of posts in the smoking cessation subreddit r/stopsmoking were classified as referring to vaping nicotine, and that only 2% of posts from the subreddits r/electronic_cigarette and r/vaping were classified as referring to smoking (tobacco) cessation.

CONCLUSIONS

This study presents the results of applying an NLP algorithm designed to identify and distinguish between cannabis and tobacco mentions (both smoking and vaping) in Reddit posts, hence contributing to our currently limited understanding of co-use, dual-use, and transition patterns between these products.

What Happens When People with Depression Gather Online?

Depression is a common mental disease that impacts people of all ages and backgrounds. To meet needs that cannot otherwise be met, people with depression or who tend to suffer from depression often gather in online depression communities. However, since joining a depression community exposes members to the depression of others, the impact of such communities is not entirely clear. This study therefore explored what happens when people with depression gather in Sina Weibo’s Depression Super Topic online community. Through website crawling, postings from Depression Super Topic were compared with postings from members’ regular timelines with respect to themes, emotions disclosed, activity patterns, and the number of likes and comments. Topics of distilled postings covering support, regulations, emotions and life sharing, and initiating discussions were then coded. From comparison analysis, it was found that postings in the Depression Super Topic community received more comments and disclosed more emotions than regular timelines and that members were more active in the community at night. This study offers a picture of what occurs when people with depression gather online, which helps better understand their issues and therefore provide more targeted support.

A Newly Developed Online Peer Support Community for Depression (Depression Connect): Qualitative Study

Background

Internet support groups enable users to provide peer support by exchanging knowledge about and experiences in coping with their illness. Several studies exploring the benefits of internet support groups for depression have found positive effects on recovery-oriented values, including empowerment. However, to date, little attention has been paid to user narratives.

Objective

This study aims to capture the user perspective on an online peer support community for depression with a focus on the modes of user engagement and the benefits users derive from participation in the forum.

Methods

In this qualitative study, we conducted 15 semistructured interviews with users of Depression Connect, a newly developed online peer support community for individuals with depression. Combining a concept-driven and a data-driven approach, we aimed to gain insight into what users value in our Depression Connect platform and whether and how the platform promotes empowerment. We performed a thematic analysis to explore the merits and demerits reported by users by using theoretical concepts widely used in internet support group research. In the subsequent data-driven analysis, we sought to understand the relationship between different styles of user engagement and the participants’ experiences with the use of Depression Connect. Data analysis consisted of open, axial, and selective coding. To include as diverse perspectives as possible, we opted for purposive sampling. To verify and validate the (interim) results, we included negative cases and performed member checks.

Results

We found participation in Depression Connect contributes to a sense of belonging, emotional growth, self-efficacy, and empowerment. “Getting too caught up” was the most frequently reported negative aspect of using Depression Connect. The deployment and development of three participation styles (ie, reading, posting, and responding) affected the perceived benefits of Depression Connect use differentially, where the latter style was central to enhancing empowerment. “Being of value to others” boosted the users’ belief in their personal strength. Finally, Depression Connect was predominantly used to supplement offline support and care for depression, and it mainly served as a safe environment where members could freely reflect on their coping mechanisms for depression and exchange and practice coping strategies.

Conclusions

Our findings shed new light on user engagement processes on which internet support groups rely. The online community primarily served as a virtual meeting place to practice (social) skills for deployment in the offline world. It also allowed the members to learn from each other’s knowledge and experiences and explore newly gained insights and coping skills.

Opportunities for Qualitative Analysis of Social Media Platforms in Dietetic Research and Practice

To date, most qualitative knowledge about individual eating patterns and the food environment has been derived from traditional data collection methods, such as interviews, focus groups, and observations. However, there currently exists a large source of nutrition-related data in social media discussions that have the potential to provide opportunities to improve dietetic research and practice. Qualitative social media discussion analysis offers a new tool for dietetic researchers and practitioners to gather insights into how the public discusses various nutrition-related topics. We first consider how social media discussion data come with significant advantages including low-cost access to timely ways to gather insights from the public, while also cautioning that social media data have limitations (e.g., difficulty verifying demographic information). We then outline 3 types of social media discussion platforms in particular: (i) online news article comment sections, (ii) food and nutrition blogs, and (iii) discussion forums. We discuss how each different type of social media offers unique insights and provide a specific example from our own research using each platform. We contend that social media discussions can contribute positively to dietetic research and practice.

Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion

The current global threat brought on by the Covid-19 pandemic has led to widespread social isolation, posing new challenges in dealing with metal suffering related to social distancing, and in quickly learning new social habits intended to prevent contagion. Neuroscience and psychology agree that dreaming helps people to cope with negative emotions and to learn from experience, but can dreaming effectively reveal mental suffering and changes in social behavior? To address this question, we applied natural language processing tools to study 239 dream reports by 67 individuals, made either before the Covid-19 outbreak or during the months of March and April, 2020, when lockdown was imposed in Brazil following the WHO’s declaration of the pandemic. Pandemic dreams showed a higher proportion of anger and sadness words, and higher average semantic similarities to the terms “contamination” and “cleanness”. These features seem to be associated with mental suffering linked to social isolation, as they explained 40% of the variance in the PANSS negative subscale related to socialization (p = 0.0088). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits that directly impact socialization.

The Public Health Impact and Policy Implications of Online Support Group Use for Mental Health in Singapore: Cross-Sectional Survey

BACKGROUND

The wide mental health treatment gap continues to pose a global and local public health challenge. Online support groups are on the rise and could be used to complement formal treatment services for mental health.

OBJECTIVE

This study aimed to examine the prevalence of online support group use and explore factors associated with the use in the general population using data from a national cross-sectional mental health survey in Singapore.

METHODS

Singapore residents aged 18 years and above participated in a nationally representative household survey in which the World Health Organization Composite International Diagnostic Interview 3.0 was administered by trained interviewers to examine the use of online support groups for mental health. Multiple logistic regressions were used to analyze the association of online support group use with various sociodemographic and health factors.

RESULTS

A total of 6110 respondents with complete data were included in this study. Overall, 10 individuals per 1000 adults (1%) reported seeking help from online support groups for their mental health problems. Compared to younger adults (those aged 18 to 34 years) and those with university education, individuals aged 50 to 64 years (P<.001; OR 0.1, 95% CI 0.0-0.3) and those with preuniversity qualifications (P=.02; OR 0.1, 95% CI 0.0-0.8) were less likely to use online support groups for mental health, respectively. Participants with a Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) mental disorder were 6.8 times more likely (P<.001; 95% CI 3.0-15.4) to use an online support group; in particular, individuals with major depressive disorder (P<.001; OR 5.4, 95% CI 2.1-13.8) and obsessive compulsive disorder (P=.01; OR 3.5, 95% CI 1.3-9.7) were more likely to use an online support group for their mental health.

CONCLUSIONS

Online support groups could be used to complement formal treatment services, especially for mood and anxiety-related disorders. As online support group use for mental health issues may be more prevalent among younger people, early detection and accurate information in online support groups may guide individuals toward seeking professional help for their mental health problems.

Understanding Patient Anxieties in the Social Media Era: Qualitative Analysis and Natural Language Processing of an Online Male Infertility Community

BACKGROUND

Couples struggling with infertility are increasingly turning to the internet for infertility-related content and to connect with others. Most of the published data on infertility and the internet only address the experiences of women, with limited studies focusing exclusively on internet discussions on male factor infertility.

OBJECTIVE

The aim of this study was to understand the concerns and experiences of discussants on an online male infertility community and to provide insight into their perceptions of interactions with health care professionals.

METHODS

Using the large-scale data analytics tool BigQuery, we extracted all posts in the r/MaleInfertility community (877 members) of the social media website and discussion board Reddit from November 2017 to October 2018. We performed a qualitative thematic analysis and quantitative semantic analysis using Language Inquiry and Word Count 2015 of the extracted posts to identify dominant themes and subthemes of discussions. Descriptive statistics and semantic analytic Z-scores were computed.

RESULTS

From the analysis of 97 posts, notable themes and subthemes emerged: 70 (72%) posts shared personal experiences, including feeling emasculated or isolated or describing a negative (28/97, 29%), positive (13/97, 13%), or neutral (56/97, 58%) experience with a health care professional; 19% (18/97) of the posts posed questions about personal semen analysis results. On the basis of semantic analysis, posts by men had higher authenticity scores (Z=3.44; P<.001), suggesting more honest or personal texts, but lower clout scores (Z=4.57; P<.001), suggesting a more tentative or anxious style of writing, compared with posts by women.

CONCLUSIONS

To our knowledge, this study represents the first evaluation of a social media community focused exclusively on male infertility using mixed methodology. These results suggest a role for physicians on social media to engage with patients and connect them to accurate resources, in addition to opportunities to improve in-office patient education.

Who says what? Content and participation characteristics in an online depression community

BACKGROUND

An increasingly important source of informal help for people with depression are online depression communities. This study investigates the prevailing topics in an online depression community and how they are related to participation styles.

METHODS

A topic model with 26 topics of N = 16,291 posts and N = 71,543 comments of N = 20,037 users in a depression forum on Reddit was created using Latent Dirichlet allocation (LDA). The topics' proportions in the corpus were correlated with five participation measures, i.e. sum of scores, number of comments, posts to comments ratio, posting frequency, and word count.

RESULTS

The most common topics were Feelings, Motivation, The Community on Reddit, and Time. There were many significant, small to moderate correlations between topic proportions and participation style measures. The topics Feelings, Offering Support, and Small Talk generated a bigger response in the form of scores and comments. Talking about the past and relationships was more common in longer posts, whereas small talk, offering emotional support, and employing cognitive strategies was more readily found in short comments. Lower posting frequency was related to talking about feelings and romantic relationships.

LIMITATIONS

No information on users' demographics or mental health status was available. Topic modeling cannot capture elements of style and tone of text.

CONCLUSIONS

A wide spectrum of topics was uncovered in the topic modeling. Patterns in the correlations point to users with different participation styles preferring different topics. Results of this study can aid the development of online interventions for depression.

Associations Between Affective States and Sexual and Health Status Among Men Who Have Sex With Men in China: Exploratory Study Using Social Media Data

Background

Affective states, including sentiment and emotion, are critical determinants of health. However, few studies among men who have sex with men (MSM) have examined sentiment and emotion specifically using real-time social media technologies. Moreover, the explorations on their associations with sexual and health status among MSM are limited.

Objective

This study aimed to understand and examine the associations of affective states with sexual behaviors and health status among MSM using public data from the Blued (Blued International Inc) app.

Methods

A total of 843,745 public postings of 377,610 MSM users located in Guangdong were saved from the Blued app by automatic screen capture. Positive affect, negative affect, sexual behaviors, and health status were measured using the Simplified Chinese Linguistic Inquiry and Word Count. Emotions, including joy, sadness, anger, fear, and disgust, were measured using the Weibo Basic Mood Lexicon. A positive sentiment score and a positive emotion score were also calculated. Univariate and multivariate linear regression models on the basis of a permutation test were used to assess the associations of affective states with sexual behaviors and health status.

Results

A total of 5871 active MSM users and their 477,374 postings were finally selected. Both positive affect and positive emotions (eg, joy) peaked between 7 AM and 9 AM. Negative affect and negative emotions (eg, sadness and disgust) peaked between 2 AM and 4 AM. During that time, 25.1% (97/387) of negative postings were related to health and 13.4% (52/387) of negative postings were related to seeking social support. A multivariate analysis showed that the MSM who were more likely to post sexual behaviors were more likely to express positive affect (beta=0.3107; P<.001) and positive emotions (joy: beta=0.027; P<.001), as well as negative emotions (sadness: beta=0.0443; P<.001 and disgust: beta=0.0256; P<.001). They also had a higher positive sentiment score (beta=0.2947; P<.001) and a higher positive emotion score (beta=0.1612; P<.001). The MSM who were more likely to post their health status were more likely to express negative affect (beta=0.8088; P<.001) and negative emotions, including sadness (beta=0.0705; P<.001), anger (beta=0.0058; P<.001), fear (beta=0.0052; P<.001), and disgust (beta=0.3065; P<.001), and less likely to express positive affect (beta=−0.0224; P=.02). In addition, they had a lower positive sentiment score (beta=−0.8306; P<.001) and a lower positive emotion score (beta=−0.3743; P<.001).

Conclusions

The MSM social media community mainly expressed their positive affect in the early morning and negative affect after midnight. Positive affective states were associated with being sexually active, whereas negative affective states were associated with health problems, mostly about mental health. Our finding suggests the potential to deliver different health-related intervention strategies (eg, psychological counseling and safe sex promotion) on a social media app according to the affective states of MSM in real time.

The Role of Perceived Loneliness in Youth Addictive Behaviors: Cross-National Survey Study

BACKGROUND

In the ever-growing and technologically advancing world, an increasing amount of social interaction takes place through the Web. With this change, loneliness is becoming an unprecedented societal issue, making youth more susceptible to various physical and mental health problems. This societal change also influences the dynamics of addiction.

OBJECTIVE

Employing the cognitive discrepancy loneliness model, this study aimed to provide a social psychological perspective on youth addictions.

METHODS

A comprehensive survey was used to collect data from American (N=1212; mean 20.05, SD 3.19; 608/1212, 50.17% women), South Korean (N=1192; mean 20.61, SD 3.24; 601/1192, 50.42% women), and Finnish (N=1200; mean 21.29, SD 2.85; 600/1200, 50.00% women) youths aged 15 to 25 years. Perceived loneliness was assessed with the 3-item Loneliness Scale. A total of 3 addictive behaviors were measured, including excessive alcohol use, compulsive internet use, and problem gambling. A total of 2 separate models using linear regression analyses were estimated for each country to examine the association between perceived loneliness and addiction.

RESULTS

Loneliness was significantly related to only compulsive internet use among the youth in all 3 countries (P<.001 in the United States, South Korea, and Finland). In the South Korean sample, the association remained significant with excessive alcohol use (P<.001) and problem gambling (P<.001), even after controlling for potentially confounding psychological variables.

CONCLUSIONS

The findings reveal existing differences between youths who spend excessive amounts of time online and those who engage in other types of addictive behaviors. Experiencing loneliness is consistently linked to compulsive internet use across countries, although different underlying factors may explain other forms of addiction. These findings provide a deeper understanding in the mechanisms of youth addiction and can help improve prevention and intervention work, especially in terms of compulsive internet use.

What Drives Patients Affected by Depression to Share in Online Depression Communities? A Social Capital Perspective

Online depression communities give people additional opportunities to share their experiences and exchange social support to care for themselves in fighting against depression. We aimed to explore what drives patients to share in online depression communities. We used three dimensions of social capital (structural, relational, and cognitive) to explain their sharing behaviors. We further proposed that five factors (social interaction ties, a sense of shared identity, trust, expertise, and a sense of shared values) will have significant, positive effects on sharing behaviors and that there are differences among patients who have spent different lengths of time participating in online depression communities. We then chose a popular online depression community in China as our data source and obtained a dataset consisting of 31,440 posts from 197 members. Then, we employed panel data regression analyses to test all six hypotheses. The results revealed that all five factors had significant, positive effects (p < 0.01) on patients' sharing behaviors, and the effects were significantly different across groups. Our empirical results help designers and managers of online depression communities take specific measures to facilitate community members' access to social capital resources. Meanwhile, our results have implications for existing health management and e-health literature.

Patterns and Longitudinal Changes in Negative Emotions of People with Depression on Sina Weibo

Background: This study investigates negative emotional patterns of people with depression on Sina Weibo™ and gives an in-depth analysis of how their negative emotions change over time. Materials and Methods: A text classifier using deep learning methods was built to identify people on Sina Weibo with depression and associated negative emotions. The longitudinal changes in negative emotions were assessed using time series and cluster analysis. Results: Results indicate that people with depression (n = 616) were more active and expressed more negative emotions on social media compared with control users (n = 3,176). Furthermore, negative emotions of people with depression were mostly about their depression issues, such as treatment, hopelessness, suicidal ideation, or self-injury. Both groups of users usually expressed negative emotions in the late evening and early morning hours. Finally, longitudinal changes in negative emotions illustrate that users with depression tended to have relatively high negative emotions in the month when they started using social media to reveal their depression issues and that they exhibited three main evolutionary patterns of negative emotions on social media. Conclusions: Findings from the study could be used to track and monitor negative emotional states of people with depression on social media, in addition to providing an in-depth understanding of how negative emotions change over time, so that better intervention strategies can be adopted to assist them.

Patient access to clinical notes in oncology: A mixed method analysis of oncologists' attitudes and linguistic characteristics towards notes

BACKGROUND

Providers have expressed concern about patient access to clinical notes. There is the possibility that providers may linguistically censor notes knowing that patients have access.

PURPOSE

Qualitative interviews and a pre- and post- linguistic analysis of the implementation of OpenNotes was performed to determine whether oncologists changed the content and style of their notes.

METHODS

Mixed methods were utilized, including 13 semi-structured interviews with oncologists and random effects modeling of over 500 clinical notes. The Linguistic Inquiry and Word Count program was used to evaluate notes for emotions, thinking styles, and social concerns.

RESULTS

No significant differences from pre- and post-implementation of OpenNotes was found. Thematic analysis revealed that oncologists were concerned that changing their notes would negatively impact multidisciplinary communication. However, oncologists acknowledged that notes could be more patient-friendly and may stimulate patient-provider communication.

CONCLUSIONS

Although oncologists were aware that patients could have access, they felt strongly about not changing the content of notes. A comparison between pre- and post-implementation confirmed this view and found that notes did not change.

PRACTICE IMPLICATIONS

Patient access to oncologist's notes may serve as an opportunity to reinforce important aspects of the consultation.

Recent Advances in Using Natural Language Processing to Address Public Health Research Questions Using Social Media and ConsumerGenerated Data

OBJECTIVE

We present a narrative review of recent work on the utilisation of Natural Language Processing (NLP) for the analysis of social media (including online health communities) specifically for public health applications.

METHODS

We conducted a literature review of NLP research that utilised social media or online consumer-generated text for public health applications, focussing on the years 2016 to 2018. Papers were identified in several ways, including PubMed searches and the inspection of recent conference proceedings from the Association of Computational Linguistics (ACL), the Conference on Human Factors in Computing Systems (CHI), and the International AAAI (Association for the Advancement of Artificial Intelligence) Conference on Web and Social Media (ICWSM). Popular data sources included Twitter, Reddit, various online health communities, and Facebook.

RESULTS

In the recent past, communicable diseases (e.g., influenza, dengue) have been the focus of much social media-based NLP health research. However, mental health and substance use and abuse (including the use of tobacco, alcohol, marijuana, and opioids) have been the subject of an increasing volume of research in the 2016 - 2018 period. Associated with this trend, the use of lexicon-based methods remains popular given the availability of psychologically validated lexical resources suitable for mental health and substance abuse research. Finally, we found that in the period under review "modern" machine learning methods (i.e. deep neural-network-based methods), while increasing in popularity, remain less widely used than "classical" machine learning methods.

Extracting Biomedical Terms from Postpartum Depression Online Health Communities

Online health communities play a vital role in supporting patients by connecting them to people with similar health conditions, thereby enabling interactions that may be distinct from those with their healthcare providers. Online health support groups can provide social support for people experiencing clinical conditions such as postpartum depression (PPD). In this paper, we describe our creation of a dataset of annotated PPD discussion forums and a preliminary assessment of topics that are discussed on the forums. Our approach leverages the capabilities of MetaMapLite (MMLite) and the Human Phenotype Ontology (HPO) concept recognition software to identify biomedical terms from BabyCenter.com online health communities. A data extraction pipeline wherein text from discussion forums on the topic of PPD is scraped and structured for annotation by the MMLite and HPO. The final corpus includes 10,584 posts with their all associated comments. Our analysis of the performance of MMLite to annotate biomedical terms relevant to PPD indicated a precision of 86.7%, recall of 81.3%, and AUC of 0.714. We propose a data model illustrating the main topics discussed among PPD forum users. Topics include: exposures, phenotypes, health conditions, behaviors, and timing. This resource has potential to enable investigating previously unexplored experiences with PPD.

OpenNotes in oncology: oncologists' perceptions and a baseline of the content and style of their clinician notes

Patients' ability to access their provider's clinical notes (OpenNotes) has been well received and has led to greater transparency in health systems. However, the majority of this research has occurred in primary care, and little is known about how patients' access to notes is used in oncology. This study aims to understand oncologists' perceptions of OpenNotes, while also establishing a baseline of the linguistic characteristics and patterns used in notes. Data from 13 in-depth, semistructured interviews with oncologists were thematically analyzed. In addition, the Linguistic Inquiry and Word Count (LIWC) program evaluated over 200 clinician notes, measuring variables encompassing emotions, thinking styles, social concerns, and parts of speech. Analysis from LIWC revealed that notes contained negative emotional tone, low authenticity, high clout, and high analytical writing. Oncologists' use of stigmatized and sensitive words, such as "obese" and "distress," was mainly absent. Themes from interviews revealed that oncologists were uncertain about patients' access to their notes and may edit their notes to avoid problematic terminology. Despite their reluctance to embrace OpenNotes, they envisioned opportunities for an improved patient-provider relationship due to patients initiating interactions from viewing notes. Oncologists believe notes are not intended for patients and altering their content may compromise the integrity of the note. This study established a baseline for further study to compare notes pre-implementation to post-implementation. Further analysis will clarify whether oncologists are altering the style and content of their notes and determine the presence of patient-centered language.

Autobiographical recall of personally familiar names and temporal information in e-mails: An automatic analytic approach using e-mail communications

An important question that arises from autobiographical memory research is whether the variables that influence memory in the laboratory also drive memory for autobiographical episodes in real life. We explored this question within the context of e-mail communications and investigated the variables that influence recall for personally familiar names and temporal information in e-mails. We designed a Web-based program that analyzed each participant's year-old sent e-mail archive and applied textual analysis algorithms to identify a set of sentences likely to be memorable. These sentences were then used as the stimuli in a cued recall task. Participants saw two sentences from their sent e-mail as a cue and attempted to recall the name of the e-mail recipient. Participants also rated the vividness of recall for the e-mail conversation and estimated the month in which they had written the e-mail. Linear mixed-effect analyses revealed that recipient name recall accuracy decreased with longer retention intervals and increased with greater frequency of contact with the recipient. Also, with longer retention intervals, participants dated e-mails as being more recent than their actual month. This telescoping error was moderately larger for e-mails with greater sentiment. These findings suggest that memory for personally familiar names and temporal information in e-mails closely follows the patterns for autobiographical memory and proper-name recall found in laboratory settings. This study introduces an innovative, Web-based experimental method for studying the cognitive processes related to autobiographical memories using ecologically valid, naturalistic communications.

Evaluation of an eHealth intervention aiming to promote healthy food habits from infancy -the Norwegian randomized controlled trial Early Food for Future Health

BACKGROUND

Strategies to optimize early-life nutrition provide an important opportunity for primary prevention of childhood obesity. Interventions that can be efficiently scaled-up to the magnitude needed for sustainable childhood obesity prevention are needed. The objective of this study was to evaluate the effects of an eHealth intervention on parental feeding practices and infant eating behaviors.

METHODS

The Norwegian study Early Food for Future Health is a randomized controlled trial. Parents were recruited via social media and child health clinics during spring 2016 when their child was aged 3 to 5 months. In total 718 parents completed a web-based baseline questionnaire at child age 5.5 months. The intervention group had access to a webpage with monthly short video clips addressing specific infant feeding topics and age-appropriate baby food recipes from child age 6 to 12 months. The control group received routine care. The primary outcomes were child eating behaviors, dietary intake, mealtime routines and maternal feeding practices and feeding styles. The secondary outcomes were child anthropometry. This paper reports outcomes at child age 12 months.

RESULTS

More than 80% of the intervention group reported viewing all/most of the video clips addressing infant feeding topics and indicated that the films were well adapted to the child's age and easy to understand. Children in the intervention group were served vegetables/fruits more frequently (p = 0.035) and had tasted a wider variety of vegetables (p = 0.015) compared to controls. They were also more likely to eat family breakfast (p = 0.035) and dinner (p = 0.011) and less likely to be playing or watching TV/tablet during meals (p = 0.009) compared to control-group children. We found no group differences for child anthropometry or maternal feeding practices.

CONCLUSIONS

Our findings suggest that the eHealth intervention is an appropriate and feasible tool to propagate information on healthy infant feeding to Norwegian mothers. Our study also suggests that anticipatory guidance on early protective feeding practices by such a tool may increase young children's daily vegetable/fruit intake and promote beneficial mealtime routines.

TRIAL REGISTRATION

ISRCTN, ISRCTN13601567. Registered 29 February 2016, http://www.isrctn.com/ISRCTN13601567.

How Online Communities of People With Long-Term Conditions Function and Evolve: Network Analysis of the Structure and Dynamics of the Asthma UK and British Lung Foundation Online Communities

Background

Self-management support can improve health and reduce health care utilization by people with long-term conditions. Online communities for people with long-term conditions have the potential to influence health, usage of health care resources, and facilitate illness self-management. Only recently, however, has evidence been reported on how such communities function and evolve, and how they support self-management of long-term conditions in practice.

Objective

The aim of this study is to gain a better understanding of the mechanisms underlying online self-management support systems by analyzing the structure and dynamics of the networks connecting users who write posts over time.

Methods

We conducted a longitudinal network analysis of anonymized data from 2 patients’ online communities from the United Kingdom: the Asthma UK and the British Lung Foundation (BLF) communities in 2006-2016 and 2012-2016, respectively.

Results

The number of users and activity grew steadily over time, reaching 3345 users and 32,780 posts in the Asthma UK community, and 19,837 users and 875,151 posts in the BLF community. People who wrote posts in the Asthma UK forum tended to write at an interval of 1-20 days and six months, while those in the BLF community wrote at an interval of two days. In both communities, most pairs of users could reach one another either directly or indirectly through other users. Those who wrote a disproportionally large number of posts (the superusers) represented 1% of the overall population of both Asthma UK and BLF communities and accounted for 32% and 49% of the posts, respectively. Sensitivity analysis showed that the removal of superusers would cause the communities to collapse. Thus, interactions were held together by very few superusers, who posted frequently and regularly, 65% of them at least every 1.7 days in the BLF community and 70% every 3.1 days in the Asthma UK community. Their posting activity indirectly facilitated tie formation between other users. Superusers were a constantly available resource, with a mean of 80 and 20 superusers active at any one time in the BLF and Asthma UK communities, respectively. Over time, the more active users became, the more likely they were to reply to other users’ posts rather than to write new ones, shifting from a help-seeking to a help-giving role. This might suggest that superusers were more likely to provide than to seek advice.

Conclusions

In this study, we uncover key structural properties related to the way users interact and sustain online health communities. Superusers’ engagement plays a fundamental sustaining role and deserves research attention. Further studies are needed to explore network determinants of the effectiveness of online engagement concerning health-related outcomes. In resource-constrained health care systems, scaling up online communities may offer a potentially accessible, wide-reaching and cost-effective intervention facilitating greater levels of self-management.

Analyzing big data in social media: Text and network analyses of an eating disorder forum

OBJECTIVE

Social media plays an important role in everyday life of young people. Numerous studies claim negative effects of social media and media in general on eating disorder risk factors. Despite the availability of big data, only few studies have exploited the possibilities so far in the field of eating disorders.

METHOD

Methods for data extraction, computerized content analysis, and network analysis will be introduced. Strategies and methods will be exemplified for an ad-hoc dataset of 4,247 posts and 34,118 comments by 3,029 users of the proed forum on Reddit.

RESULTS

Text analysis with latent Dirichlet allocation identified nine topics related to social support and eating disorder specific content. Social network analysis describes the overall communication patterns, and could identify community structures and most influential users. A linear network autocorrelation model was applied to estimate associations in language among network neighbors. The supplement contains R code for data extraction and analyses.

DISCUSSION

This paper provides an introduction to investigating social media data, and will hopefully stimulate big data social media research in eating disorders. When applied in real-time, the methods presented in this manuscript could contribute to improving the safety of ED-related online communication.

Scaling PatientsLikeMe via a "Generalized Platform" for Members with Chronic Illness: Web-Based Survey Study of Benefits Arising

Background

Launched in 2006 for patients with amyotrophic lateral sclerosis, PatientsLikeMe is an online community offering patient-reported outcomes, symptom tracking, and social features. Every member of the site can see all the data reported by every other member, view aggregated reports, identify “patients like them,” and learn about treatment options in order to live better with their condition. In previous studies, members reported benefits such as improved condition knowledge, increased medication adherence, and better management of side effects. However, the site evolved in 2011 from condition-specific “vertical” communities consisting only of people with the same disease to a “generalized platform,” in which every patient could connect with every other patient regardless of condition and with generic, rather than condition-specific, data tools. Some, but not all, communities received further custom tracking tools.

Objective

We aimed to understand (1) whether members of PatientsLikeMe using the generalized platform still reported similar benefits and (2) assess factors associated with benefits, such as community customization, site use, and patient activation.

Methods

A cross-sectional retrospective custom survey was fielded to 377,625 members between 2016 and 2017 including the Patient Activation Measure (PAM). A benefit index was developed for comparability across conditions.

Results

The invitation was viewed by 26,048 members of whom 11,915 did not respond, 5091 opted out, 1591 provided partial data, and 17 were screened out. Complete responses were received from 7434 participants. Users perceived greatest benefit in understanding how their condition may affect them (4530/6770, 66.91% participants, excluding “does not apply” answers), understanding what might help them live better with their condition (4247/6750, 62.92%), which treatments were available (4143/6898, 60.06%), understanding treatment side effects (4182/6902, 60.59%), and important factors in making treatment decisions (3919/6813, 57.52%). The benefit index was 29% higher for the “most activated” patients (PAM level 4 vs PAM level 1; relative risk [RR]=1.29, P<.001), 21% higher for conditions with some community customization versus none (RR=1.21, P<.001), and 11% higher in those using the site most often versus least (RR=1.11, P<.001).

Conclusions

Members of the generalized platform reported a range of benefits related to improved knowledge and understanding of their condition and treatment management. Condition-specific customization may improve their experience still further. Future studies will explore longitudinal changes to patient activation.

Tracking Health Related Discussions on Reddit for Public Health Applications

We use Reddit to demonstrate social media's potential for public health applications. First, we employ a lexicon-based approach to track the prevalence of keywords indicating public interest in Ebola, electronic cigarette, influenza, and marijuana. Second, to better understand the public reactions, we use the Latent Dirichlet Allocation algorithm, to identify either the general themes or motivations for extreme changes in the volume of discussion over time. We observe that discussions related to Ebola and influenza, infectious diseases of public health interests, surged when the first case of Ebola was diagnosed and a new strain of H1N1 influenza virus was confirmed in the United States. We also observed that discussions of a controversial health topic like marijuana increased with the announcement of a major change in United States federal policy. Discussions of electronic cigarette highlighted opportunities for better health education. Lastly, we discuss the implications of our findings for utilizing Reddit data for public health applications.

Harnessing Reddit to Understand the Written-Communication Challenges Experienced by Individuals With Mental Health Disorders: Analysis of Texts From Mental Health Communities

BACKGROUND

Mental disorders such as depression, bipolar disorder, and schizophrenia are common, incapacitating, and have the potential to be fatal. Despite the prevalence and gravity of mental disorders, our knowledge concerning everyday challenges associated with them is relatively limited. One of the most studied deficits related to everyday challenges is language impairment, yet we do not know how mental disorders can impact common forms of written communication, for example, social media.

OBJECTIVE

The aims of this study were to investigate written communication challenges manifest in online mental health communities focusing on depression, bipolar disorder, and schizophrenia, as well as the impact of participating in these online mental health communities on written communication. As the control, we selected three online health communities focusing on positive emotion, exercising, and weight management.

METHODS

We examined lexical diversity and readability, both important features for measuring the quality of writing. We used four well-established readability metrics that consider word frequencies and syntactic complexity to measure writers' written communication ability. We then measured the lexical diversity by calculating the percentage of unique words in posts. To compare lexical diversity and readability among communities, we first applied pairwise independent sample t tests, followed by P value adjustments using the prespecified Hommel procedure to adjust for multiple comparison. To measure the changes, we applied linear least squares regression to the readability and lexical diversity scores against the interaction sequence for each member, followed by pairwise independent sample t tests and P value adjustments. Given the large sample of members, we also report effect sizes and 95% CIs for the pairwise comparisons.

RESULTS

On average, members of depression, bipolar disorder, and schizophrenia communities showed indications of difficulty expressing their ideas compared with three other online health communities. Our results also suggest that participating in these platforms has the potential to improve members' written communication. For example, members of all three mental health communities showed statistically significant improvement in both lexical diversity and readability compared with members of the OHC focusing on positive emotion.

CONCLUSIONS

We provide new insights into the written communication challenges faced by individuals suffering from depression, bipolar disorder, and schizophrenia. A comparison with three other online health communities suggests that written communication in mental health communities is significantly more difficult to read, while also consisting of a significantly less diverse lexicon. We contribute practical suggestions for utilizing our findings in Web-based communication settings to enhance members' communicative experience. We consider these findings to be an important step toward understanding and addressing everyday written communication challenges among individuals suffering from mental disorders.

Toward Impactful Collaborations on Computing and Mental Health

We describe an initiative to bring mental health researchers, computer scientists, human-computer interaction researchers, and other communities together to address the challenges of the global mental ill health epidemic. Two face-to-face events and one special issue of the Journal of Medical Internet Research were organized. The works presented in these events and publication reflect key state-of-the-art research in this interdisciplinary collaboration. We summarize the special issue articles and contextualize them to present a picture of the most recent research. In addition, we describe a series of collaborative activities held during the second symposium and where the community identified 5 challenges and their possible solutions.

Examining Thematic Similarity, Difference, and Membership in Three Online Mental Health Communities from Reddit: A Text Mining and Visualization Approach

Objectives

Social media, including online health communities, have become popular platforms for individuals to discuss health challenges and exchange social support with others. These platforms can provide support for individuals who are concerned about social stigma and discrimination associated with their illness. Although mental health conditions can share similar symptoms and even co-occur, the extent to which discussion topics in online mental health communities are similar, different, or overlapping is unknown. Discovering the topical similarities and differences could potentially inform the design of related mental health communities and patient education programs. This study employs text mining, qualitative analysis, and visualization techniques to compare discussion topics in publicly accessible online mental health communities for three conditions: Anxiety, Depression and Post-Traumatic Stress Disorder.

Methods

First, online discussion content for the three conditions was collected from three Reddit communities (r/Anxiety, r/Depression, and r/PTSD). Second, content was pre-processed, and then clustered using the k-means algorithm to identify themes that were commonly discussed by members. Third, we qualitatively examined the common themes to better understand them, as well as their similarities and differences. Fourth, we employed multiple visualization techniques to form a deeper understanding of the relationships among the identified themes for the three mental health conditions.

Results

The three mental health communities shared four themes: sharing of positive emotion, gratitude for receiving emotional support, and sleep- and work-related issues. Depression clusters tended to focus on self-expressed contextual aspects of depression, whereas the Anxiety Disorders and Post-Traumatic Stress Disorder clusters addressed more treatment- and medication-related issues. Visualizations showed that discussion topics from the Anxiety Disorders and Post-Traumatic Stress Disorder subreddits shared more similarities to one another than to the depression subreddit.

Conclusions

We observed that the members of the three communities shared several overlapping concerns (i.e., sleep- and work-related problems) and discussion patterns (i.e., sharing of positive emotion and showing gratitude for receiving emotional support). We also highlighted that the discussions from the r/Anxiety and r/PTSD communities were more similar to one another than to discussions from the r/Depression community. The r/Anxiety and r/PTSD subreddit members are more likely to be individuals whose experiences with a condition are long-term, and who are interested in treatments and medications. The r/Depression subreddit members may be a comparatively diffuse group, many of whom are dealing with transient issues that cause depressed mood. The findings from this study could be used to inform the design of online mental health communities and patient education programs for these conditions. Moreover, we suggest that researchers employ multiple methods to fully understand the subtle differences when comparing similar discussions from online health communities.

Toward Predicting Social Support Needs in Online Health Social Networks

Background

While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge.

Objective

The objective of this study was to discriminate important features for identifying users’ social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework, which can be used to predict users’ social support needs based on raw data collected from OHSNs.

Methods

We initially conducted a Web-based survey with 184 OHSN users. From this survey data, we extracted 34 features based on 5 categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first 4 categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: gradient boosting tree, random forest, decision tree, support vector machines, and logistic regression. We then calculated the scores of the area under the receiver operating characteristic (ROC) curve (AUC) to understand the comparative effectiveness of the used features.

Results

The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one’s social support need. Among other discoveries, we found that users seeking emotional support tend to post more in OHSNs compared with others.

Conclusions

We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve nonsurvey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.