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

Understanding the Impacts of Online Mental Health Peer Support Forums: Realist Synthesis

BACKGROUND

Online forums are widely used for mental health peer support. However, evidence of their safety and effectiveness is mixed. Further research focused on articulating the contexts in which positive and negative impacts emerge from forum use is required to inform innovations in implementation.

OBJECTIVE

This study aimed to develop a realist program theory to explain the impacts of online mental health peer support forums on users.

METHODS

We conducted a realist synthesis of literature published between 2019 and 2023 and 18 stakeholder interviews with forum staff.

RESULTS

Synthesis of 102 evidence sources and 18 interviews produced an overarching program theory comprising 22 context-mechanism-outcome configurations. Findings indicate that users' perceptions of psychological safety and the personal relevance of forum content are foundational to ongoing engagement. Safe and active forums that provide convenient access to information and advice can lead to improvements in mental health self-efficacy. Within the context of welcoming and nonjudgmental communities, users may benefit from the opportunity to explore personal difficulties with peers, experience reduced isolation and normalization of mental health experiences, and engage in mutual encouragement. The program theory highlights the vital role of moderators in creating facilitative online spaces, stimulating community engagement, and limiting access to distressing content. A key challenge for organizations that host mental health forums lies in balancing forum openness and anonymity with the need to enforce rules, such as restrictions on what users can discuss, to promote community safety.

CONCLUSIONS

This is the first realist synthesis of online mental health peer support forums. The novel program theory highlights how successful implementation depends on establishing protocols for enhancing safety and strategies for maintaining user engagement to promote forum sustainability.

TRIAL REGISTRATION

PROSPERO CRD42022352528; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=352528.

The Effect of Characteristics of Patient Communication on Physician Feedback in Online Health Communities: An Observational Cross-Sectional Study

With the rapid development of e-health and telemedicine, previous studies have explored the relationship between physician-patient communication and patient satisfaction; however, there is a paucity of research on the influence of the characteristics of patient communication on the characteristics of physician feedback. Based on the communication accommodation theory, as well as the computer-mediated communication theory and media richness theory, this study aimed to explore how characteristics of patient communication influence characteristics of physician feedback in online health communities. We employed a crawler software to download the communication data between 1652 physicians and 105,325 patients from the Good Doctor platform, the biggest online health community in China. We built an empirical model using this data and employed a multilevel model to test our hypotheses using Stata and Python software. The results indicate that the amount of patients' rendered information positively influences the physicians' text (α = 0.123, t = 33.147, P < .001) and voice feedback (β = 0.201, t = 40.011, P < .001). Patients' hope for help signals and the provision of their electronic health records weaken the effect of the amount of patients' rendered information on physicians' text feedback (α = -0.040, t = -24.857, P < .001; α = -0.048, t = -15.784, P < .001), whereas, it strengthened the effect of the amount of patients' rendered information on physicians' voice feedback (β = 0.033, t = 14.789, P < .001; β = 0.017, t = 4.208, P < .001). Moreover, the occurrence of high-privacy diseases strengthened the effect of the amount of patients' presented information on physicians' text and voice feedback (α = 0.023, t = 4.870, P < .001; β = 0.028, t = 4.282, P < .001). This research contributes to the development of computer-mediated communication theories and sheds light on service delivery in the online health community.

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.

The Stories We Tell Influence the Support We Receive: Examining the Reception of Support-Seeking Messages on Reddit

Although social support facilitates coping and recovering from stressful life events, people do not always get the support that they need. Prior research suggests that the way one talks about stressful events to others may influence the support they receive. Given that people are increasingly relying on online communities for social support, this study adopted a person-centered approach (latent profile analysis) to examine how narrative variables related to the motivational themes, emotional content, and organizational structure of randomly sampled support-seeking messages (N = 495) posted on Reddit (r/Anxiety and r/Depression) influenced the quantity (number of comments and post score) and quality (type of support in comments) of support that they received. We identified five distinct narrative profiles of support-seeking posts, which in turn differentially predicted the quality, but not quantity, of social support people received. While commenters provided high levels of emotional support to all forms of posts, we found that coherence was an important determinant of esteem support. A combination of coherence, as well as agency and affective tone, were important determinants of instrumental, informational, and network support. The ways in which one talks about their problems influence the way others support them.

Buffering against exposure to mental health misinformation in online communities on Facebook: the interplay of depression literacy and expert moderation

BACKGROUND

The proliferation of health misinformation on social media is a growing public health concern. Online communities for mental health (OCMHs) are also considered an outlet for exposure to misinformation. This study explored the impact of the self-reported volume of exposure to mental health misinformation in misinformation agreement and the moderating effects of depression literacy and type of OCMHs participation (expert vs. peer-led).

METHODS

Participants (n = 403) were recruited in Italian-speaking OCMHs on Facebook. We conducted regression analyses using PROCESS macro (moderated moderation, Model 3). Measures included: the Depression Literacy Questionnaire (Griffiths et al., 2004), the self-reported misinformation exposure in the OCMHs (3 items), and misinformation agreement with the exposure items (3 items). Whether participants were members of expert or peer-led OCMHs was also investigated.

RESULTS

The final model explained the 12% variance in the agreement. There was a positive and significant relationship between misinformation exposure and misinformation agreement (β = 0.3221, p < .001), a significant two-way interaction between misinformation exposure and depression literacy (β = - 0.2179, p = .0014 ), and between self-reported misinformation exposure and type of OCMH (β = - 0.2322, p = .0254), such that at higher levels of depression literacy and in case of participation to expert-led OCMHs, the relationship misinformation exposure-misinformation agreement was weaker. Finally, a three-way interaction was found (β = 0.2497, p = .0144) that showed that depression literacy moderated the positive relationship between misinformation exposure and misinformation agreement such that the more misinformation participants were exposed to, the more they agreed with it unless they had higher levels of depression literacy; this, however, occurred only if they participated in peer-led groups.

CONCLUSIONS

Results provide evidence that the more members reported being exposed to mental health misinformation, the more they tended to agree with it, however this was only visible when participants had lower depression literacy and were participating in peer-led OCMHs. Results of this study suggest that both internal factors (i.e., high depression literacy) and external factors (the type of online community individuals were participating in) can buffer the negative effects of misinformation exposure. It also suggests that increasing depression literacy and expert community moderation could curb the negative consequences of misinformation exposure related to mental health. Results will guide interventions to mitigate the effects of misinformation in OCMHs, including encouraging health professionals in their administration and implementing health education programs.

A Study on Online Health Community Users' Information Demands Based on the BERT-LDA Model

As the economy and society develop and the standard of living improves, people's health awareness increases and the demand for health information grows. This study introduces an advanced BERT-LDA model to conduct topic-sentiment analysis within online health communities. It examines nine primary categories of user information requirements: causes, symptoms and manifestations, examination and diagnosis, treatment, self-management and regulation, impact, prevention, social life, and knowledge acquisition. By analyzing the distribution of positive and negative sentiments across each topic, the correlation between various health information demands and emotional expressions is investigated. The model established in this paper integrates BERT's semantic comprehension with LDA's topic modeling capabilities, enhancing the accuracy of topic identification and sentiment analysis while providing a more comprehensive evaluation of user information demands. This research furthers our understanding of users' emotional reactions and presents valuable insights for delivering personalized health information in online communities.

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

BACKGROUND

The COVID-19 pandemic has resulted in heightened levels of depression, anxiety, and other mental health issues due to sudden changes in daily life, such as economic stress, social isolation, and educational irregularity. Accurately assessing emotional and behavioral changes in response to the pandemic can be challenging, but it is essential to understand the evolving emotions, themes, and discussions surrounding the impact of COVID-19 on mental health.

OBJECTIVE

This study aims to understand the evolving emotions and themes associated with the impact of COVID-19 on mental health support groups (eg, r/Depression and r/Anxiety) on Reddit (Reddit Inc) during the initial phase and after the peak of the pandemic using natural language processing techniques and statistical methods.

METHODS

This study used data from the r/Depression and r/Anxiety Reddit communities, which consisted of posts contributed by 351,409 distinct users over a period spanning from 2019 to 2022. Topic modeling and Word2Vec embedding models were used to identify key terms associated with the targeted themes within the data set. A range of trend and thematic analysis techniques, including time-to-event analysis, heat map analysis, factor analysis, regression analysis, and k-means clustering analysis, were used to analyze the data.

RESULTS

The time-to-event analysis revealed that the first 28 days following a major event could be considered a critical window for mental health concerns to become more prominent. The theme trend analysis revealed key themes such as economic stress, social stress, suicide, and substance use, with varying trends and impacts in each community. The factor analysis highlighted pandemic-related stress, economic concerns, and social factors as primary themes during the analyzed period. Regression analysis showed that economic stress consistently demonstrated the strongest association with the suicide theme, whereas the substance theme had a notable association in both data sets. Finally, the k-means clustering analysis showed that in r/Depression, the number of posts related to the "depression, anxiety, and medication" cluster decreased after 2020, whereas the "social relationships and friendship" cluster showed a steady decrease. In r/Anxiety, the "general anxiety and feelings of unease" cluster peaked in April 2020 and remained high, whereas the "physical symptoms of anxiety" cluster showed a slight increase.

CONCLUSIONS

This study sheds light on the impact of COVID-19 on mental health and the related themes discussed in 2 web-based communities during the pandemic. The results offer valuable insights for developing targeted interventions and policies to support individuals and communities in similar crises.

Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects

Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.

What is brain fog?

BACKGROUND

The term 'brain fog' is increasingly used colloquially to describe difficulties in the cognitive realm. But what is brain fog? What sort of experiences do people talk about when they talk about brain fog? And, in turn, what might this tell us about potential underlying pathophysiological mechanisms? This study examined first-person descriptions in order to better understand the phenomenology of brain fog.

METHODS

Posts containing 'brain fog' were scraped from the social media platform Reddit, using python, over a week in October 2021. We examined descriptions of brain fog, themes of containing subreddits (topic-specific discussion forums), and causal attributions.

RESULTS

1663 posts containing 'brain fog' were identified, 717 meeting inclusion criteria. 141 first person phenomenological descriptions depicted forgetfulness (51), difficulty concentrating (43), dissociative phenomena (34), cognitive 'slowness' and excessive effort (26), communication difficulties (22), 'fuzziness' or pressure (10) and fatigue (9). 50% (363/717) posts were in subreddits concerned with illness and disease: including COVID-19 (87), psychiatric, neurodevelopmental, autoimmune and functional disorders. 134 posts were in subreddits about drug use or discontinuation, and 44 in subreddits about abstention from masturbation. 570 posts included the poster's causal attribution, the most frequent attribution being long COVID in 60/570 (10%).

CONCLUSIONS

'Brain fog' is used on Reddit to describe heterogeneous experiences, including of dissociation, fatigue, forgetfulness and excessive cognitive effort, and in association with a range of illnesses, drugs and behaviours. Encouraging detailed description of these experiences will help us better understand pathophysiological mechanisms underlying cognitive symptoms in health and disease.

Evolution of discussion topics on an online depression self-help group

Purpose

This study aims to explore the evolutionary pattern of discussion topics over time in an online depression self-help community.

Design/methodology/approach

Using the Latent Dirichlet Allocation (LDA) method, the authors analyzed 17,534 posts and 138,567 comments posted over 8 years on an online depression self-help group in China and identified the major discussion topics. Based on significant changes in the frequency of posts over time, the authors identified five stages of development. Through a comparative analysis of discussion topics in the five stages, the authors identified the changes in the extent and range of topics over time. The authors discuss the influence of socio-cultural factors on depressed individuals' health information behavior.

Findings

The results illustrate an evolutionary pattern of topics in users' discussion in the online depression self-help group, including five distinct stages with a sequence of topic changes. The discussion topics of the group included self-reflection, daily record, peer diagnosis, companionship support and instrumental support. While some prominent topics were discussed frequently in each stage, some topics were short-lived.

Originality/value

While most prior research has ignored topic changes over time, the study takes an evolutionary perspective of online discussion topics among depressed individuals. The authors provide a nuanced account of the progression of topics through five distinct stages, showing that the community experienced a sequence of changes as it developed. Identifying this evolutionary pattern extends the scope of research on depression therapy in China and offers a deeper understanding of the support that individuals with depression seek, receive and provide online.

The Effect of Visualization Techniques on Students of Occupational Therapy during the First Visit to the Dissection Room

Background: Part of the basic teaching of human anatomy are prosection sessions with a human corpse, which may generate stress or anxiety among students. The objective of this work was to study how, through the visualization technique (a coping technique), these levels could be reduced before starting prosection classes. Methods: A cross-sectional pilot study was conducted involving first-year students who had never participated in screening sessions. Prior to the visit, occupational therapy students underwent a viewing session (visualization technique). On the day of the visit, before and after the screening session, an anonymous questionnaire was distributed to find out about aspects of the students’ experiences, such as their feelings and perceptions. The State−Trait Anxiety Inventory was used to assess anxiety. Results: The baseline levels of anxiety measured remained stable (from 18.5 to 18.2 points), with no differences being found (p > 0.05). The levels of emotional anxiety measured fell from 15.2 to 12.6 points (p < 0.05). Before starting the class, there were six students (17.1%) with anxiety criteria, and this figure was doubled at the end of the session (33.3%) (p < 0.05). Conclusions: Sessions in a dissection room can cause stressful experiences and change the emotional balances of some students. The results obtained and published here showed no significant differences after the visualization technique. We found that the students believed that the prosection sessions were very useful for teaching anatomy.

A Content Analysis of Reddit Users' Posts about Challenges to Contraceptive care-seeking during COVID-19-related Restrictions in the United States

The COVID-19 pandemic in the United States caused disruptions in care seeking and delivery during the spring of 2020, including for contraceptive care. We examined how some individuals experienced and responded to barriers to accessing contraceptive care by conducting a content analysis of relevant Reddit posts. We collected 2666 posts by scraping relevant subreddits from February 1, 2020, to April 15, 2020, and filtering by selected keywords. Among the 101 posts on contraception and the COVID-19 pandemic, we explored three main themes: barriers to accessing general healthcare during the early pandemic, problems and concerns specific to contraceptive use, and attempts to navigate the obstacles to contraceptive care or use-related concerns. The Reddit posts demonstrated the disruptive force the early pandemic had on contraceptive care and provided a unique window into the concerns posters expressed on Reddit during this time. Many posters asked questions related to accessing contraception and side effects and sought reassurance from these online forums. Our results suggest that there were barriers to accessing reliable, high-quality, and evidence-based information about contraception during this disruption in care. The findings also underscore that conversational and interactive means of seeking out information are important modes for learning about and discussing contraception for some and may be especially helpful during clinic closures and other restrictions on access.

Exploring Reddit conversations about mental health difficulties among college students during the COVID-19 pandemic

Objective: We aimed to explore conversations about mental health difficulties by Reddit users who posted within college subreddits during the COVID-19 pandemic. Participants: Data were collected from the subreddits of 22 California campuses, representing 113,579 anonymous members. Using the following search terms, we retrieved 577 posts (ie, 268 original posts and 309 replies): COVID, Coronavirus, Quarantine, Pandemic, Anxiety, Anxious, Depressed, Depression, Overwhelmed, Stress, and Stressed. Methods: We used inductive, thematic data analysis to explore themes within posts and replies dated from 3/16/2020 to 3/16/2021. Results: We identified the following themes: 1) the COVID-19 pandemic has negatively impacted engagement with learning; 2) remote learning has exacerbated students' mental health difficulties; and 3) students provide and receive social support online. Conclusions: These findings have implications that are particularly relevant as campuses are faced with continuous decisions related to repopulation.

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.

Predicting Mental Health Status in Remote and Rural Farming Communities: Computational Analysis of Text-Based Counseling

Background

Australians living in rural and remote areas are at elevated risk of mental health problems and must overcome barriers to help seeking, such as poor access, stigma, and entrenched stoicism. e-Mental health services circumvent such barriers using technology, and text-based services are particularly well suited to clients concerned with privacy and self-presentation. They allow the client to reflect on the therapy session after it has ended as the chat log is stored on their device. The text also offers researchers an opportunity to analyze language use patterns and explore how these relate to mental health status.

Objective

In this project, we investigated whether computational linguistic techniques can be applied to text-based communications with the goal of identifying a client’s mental health status.

Methods

Client-therapist text messages were analyzed using the Linguistic Inquiry and Word Count tool. We examined whether the resulting word counts related to the participants’ presenting problems or their self-ratings of mental health at the completion of counseling.

Results

The results confirmed that word use patterns could be used to differentiate whether a client had one of the top 3 presenting problems (depression, anxiety, or stress) and, prospectively, to predict their self-rated mental health after counseling had been completed.

Conclusions

These findings suggest that language use patterns are useful for both researchers and clinicians trying to identify individuals at risk of mental health problems, with potential applications in screening and targeted intervention.

Let's Talk About It: A Narrative Review of Digital Approaches for Disseminating and Communicating Health Research and Innovation

Best health practice and policy are derived from research, yet the adoption of research findings into health practice and policy continues to lag. Efforts to close this knowledge-to-action gap can be addressed through knowledge translation, which is composed of knowledge synthesis, dissemination, exchange, and application. Although all components warrant investigation, improvements in knowledge dissemination are particularly needed. Specifically, as society continues to evolve and technology becomes increasingly present in everyday life, knowing how to share research findings (with the appropriate audience, using tailored messaging, and through the right digital medium) is an important component towards improved health knowledge translation. As such, this article presents a review of digital presentation formats and communication channels that can be leveraged by health researchers, as well as practitioners and policy makers, for knowledge dissemination of health research. In addition, this article highlights a series of additional factors worth consideration, as well as areas for future direction.

Perceived barriers to psychiatric help-seeking in South Korea by age groups: text mining analyses of social media big data

BACKGROUND

The psychiatric treatment gap is substantial in Korea, implying barriers in seeking help.

OBJECTIVES

This study aims to explore barriers of seeing psychiatrists, expressed on the internet by age groups.

METHODS

A corpus of data was garnered extensively from internet communities, blogs and social network services from 1 January 2016 to 31 July 2019. Among the texts collected, texts containing words linked to psychiatry were selected. Then the corpus was dismantled into words by using natural language processing. Words linked to barriers to seeking help were identified and classified. Then the words from web communities that we were able to identify the age groups were additionally organized by age groups.

RESULTS

97,730,360 articles were identified and 6,097,369 were included in the analysis. Words implying the barriers were selected and classified into four groups of structural discrimination, public prejudice, low accessibility, and adverse drug effects. Structural discrimination was the greatest barrier occupying 34%, followed by public prejudice (27.8%), adverse drug effects (18.6%), and cost/low accessibility (16.1%). In the analysis by age groups, structural discrimination caused teenagers (51%), job seekers (64%) and mothers with children (43%) the most concern. In contrast, the public prejudice (49%) was the greatest barriers in the senior group.

CONCLUSIONS

Although structural discrimination may most contribute to barriers to visiting psychiatrists in Korea, variation by generations may exist. Along with the general attempt to tackle the discrimination, customized approach might be needed.

A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research

Purpose

How to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and abstracts of articles.

Design/methodology/approach

The authors conduct a comparison study of topic modeling on full-text paper and corresponding abstract to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.

Findings

The authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding abstract is higher when more documents are analyzed.

Originality/value

First, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

Natural language processing applied to mental illness detection: a narrative review

Mental illness is highly prevalent nowadays, constituting a major cause of distress in people’s life with impact on society’s health and well-being. Mental illness is a complex multi-factorial disease associated with individual risk factors and a variety of socioeconomic, clinical associations. In order to capture these complex associations expressed in a wide variety of textual data, including social media posts, interviews, and clinical notes, natural language processing (NLP) methods demonstrate promising improvements to empower proactive mental healthcare and assist early diagnosis. We provide a narrative review of mental illness detection using NLP in the past decade, to understand methods, trends, challenges and future directions. A total of 399 studies from 10,467 records were included. The review reveals that there is an upward trend in mental illness detection NLP research. Deep learning methods receive more attention and perform better than traditional machine learning methods. We also provide some recommendations for future studies, including the development of novel detection methods, deep learning paradigms and interpretable models.

An observational analysis of the trope “A p-value of < 0.05 was considered statistically significant” and other cut-and-paste statistical methods

Appropriate descriptions of statistical methods are essential for evaluating research quality and reproducibility. Despite continued efforts to improve reporting in publications, inadequate descriptions of statistical methods persist. At times, reading statistical methods sections can conjure feelings of dèjá vu, with content resembling cut-and-pasted or “boilerplate text” from already published work. Instances of boilerplate text suggest a mechanistic approach to statistical analysis, where the same default methods are being used and described using standardized text. To investigate the extent of this practice, we analyzed text extracted from published statistical methods sections from PLOS ONE and the Australian and New Zealand Clinical Trials Registry (ANZCTR). Topic modeling was applied to analyze data from 111,731 papers published in PLOS ONE and 9,523 studies registered with the ANZCTR. PLOS ONE topics emphasized definitions of statistical significance, software and descriptive statistics. One in three PLOS ONE papers contained at least 1 sentence that was a direct copy from another paper. 12,675 papers (11%) closely matched to the sentence “a p-value < 0.05 was considered statistically significant”. Common topics across ANZCTR studies differentiated between study designs and analysis methods, with matching text found in approximately 3% of sections. Our findings quantify a serious problem affecting the reporting of statistical methods and shed light on perceptions about the communication of statistics as part of the scientific process. Results further emphasize the importance of rigorous statistical review to ensure that adequate descriptions of methods are prioritized over relatively minor details such as p-values and software when reporting research outcomes.

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.

Using crowdsourced medicine to manage uncertainty on Reddit: The case of COVID-19 long-haulers

OBJECTIVE

Causes of and treatments for long-COVID syndrome remain unknown. Drawing on uncertainty management theory (UMT), this study elucidates the communicative nature of crowdsourced medicine as a means by which COVID "long-haulers" respond to their poorly understood illness.

METHODS

31,892 posts on the long-haulers subreddit (r/covidlonghaulers) were analyzed, starting with its creation date, July 24th, 2020, until January 7, 2021. The Meaning Extraction Method was used to identify clusters of words that mathematically group together across the text observations.

RESULTS

Analyses yielded 16 distinct factors of words, which we thematized based on their composition, the data, and UMT. The 16 themes encompassed symptoms (e.g., pain, respiratory, sensory), diagnostic concerns (testing, diagnosis), broad health concerns (immunity, physical activity, diet), chronicity, support, identity, and anxiety.

CONCLUSION

Findings provide a succinct, yet robust set of themes reflecting the information-seeking (i.e., "This is happening to me") and support-seeking functions of long-haulers' talk (i.e., "Is this happening to you?"). Findings have implications for collective uncertainty management, online crowdsourcing, and patient advocacy.

PRACTICE IMPLICATIONS

We recommend that health care providers employ sensitivity when addressing the anxiety that long-haulers are experiencing while also validating that their physical symptoms are real. Online communities help long-haulers manage their uncertainty.

How loneliness is talked about in social media during COVID-19 pandemic: Text mining of 4,492 Twitter feeds

BACKGROUND

Loneliness is a public health problem that is expected to rise during the COVID-19 pandemic, given the widespread policy of quarantine. The literature is unclear whether loneliness during COVID-19 is similar to those of non-pandemic seasons. This study examined the expression of loneliness on Twitter during COVID-19 pandemic, and identified key areas of loneliness across diverse communities.

METHODS

Twitter was searched for feeds that were:(1) in English; (2) posted from May 1, 2020 to July 1, 2020; (3) posted by individual users (not organisations); and (4) contained the words 'loneliness' and 'COVID-19'. A machine-learning approach (Topic Modeling) identified key topics from the Twitter feeds; Hierarchical Modeling identified overarching themes. Variations in the prevalence of the themes were examined over time and across the number of followers of the Twitter users.

RESULTS

4492 Twitter feeds were included and classified into 3 themes: (1) Community impact of loneliness during COVID-19; (2) Social distancing during COVID-19 and its effects on loneliness; and (3) Mental health effects of loneliness during COVID-19. The 3 themes demonstrated temporal variations. Particularly in Europe, Theme 1 showed a drastic reduction over time, with a corresponding rise in Theme 3. The themes also varied across number of followers. Highly influential users were more likely to talk about Theme 3 and less about Theme 2.

CONCLUSIONS

The findings reflect close-to-real-time public sentiments on loneliness during the COVID-19 pandemic and demonstrated the potential usefulness of social media to keep tabs on evolving mental health issues. It also provides inspiration for potential interventions to address novel problems-such as loneliness-during COVID-19 pandemic.

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach

Background

Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts.

Objective

By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts.

Methods

The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder–related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post’s language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts.

Results

Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter.

Conclusions

This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

What users seek and share in online diabetes communities: examining similarities and differences in expressions and themes

Purpose

This paper aims to investigate user health information seeking and sharing patterns and content in an online diabetes community and explore the similarities and differences in the ways and themes they expressed.

Design/methodology/approach

Multiple methods are applied to analyze the expressions and themes that users seek and share based on large-scale text data in an online diabetes community. First, a text classifier using deep learning method is performed based on the expression category this study developed. Second, statistical and social network analyses are used to measure the popularity and compare differences between expressions. Third, topic modeling, manual coding and similarity analysis are used to mining topics and thematic similarity between seeking and sharing threads.

Findings

There are four different ways users seek and share in online health communities (OHCs) including informational seeking, situational seeking, objective information sharing and experiential information sharing. The results indicate that threads with self-disclosure could receive more replies and attract more users to contribute. This study also examines the 10 topics that were discussed for information seeking and 14 topics for information sharing. They shared three discussion themes: self-management, medication and symptoms. Information about symptoms can be largely matched between seeking and sharing threads while there is less overlap in self-management and medication categories.

Originality/value

Being different from previous studies that mainly describe one type of health information behavior, this paper analyzes user health information seeking and sharing behaviors in OHCs and investigates whether there is a correspondence or discrepancy between expressions and information users spontaneously seek and share in OHCs.

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.

Distributional social semantics: Inferring word meanings from communication patterns

Distributional models of lexical semantics have proven to be powerful accounts of how word meanings are acquired from the natural language environment (Günther, Rinaldi, & Marelli, 2019; Kumar, 2020). Standard models of this type acquire the meaning of words through the learning of word co-occurrence statistics across large corpora. However, these models ignore social and communicative aspects of language processing, which is considered central to usage-based and adaptive theories of language (Tomasello, 2003; Beckner et al., 2009). Johns (2021) recently demonstrated that integrating social and communicative information into a lexical strength measure allowed for benchmark fits to be attained for lexical organization data, indicating that the social world contains important statistical information for language learning and processing. Through the analysis of the communication patterns of over 330,000 individuals on the online forum Reddit, totaling approximately 55 billion words of text, the findings of the current article demonstrates that social information about word usage allows for unique aspects of a word's meaning to be acquired, providing a new pathway for distributional model development.

Leveraging Text Mining Approach to Identify What People Want to Know About Mental Disorders From Online Inquiry Platforms

Online inquiry platforms, which is where a person can anonymously ask questions, have become an important information source for those who are concerned about social stigma and discrimination that follow mental disorders. Therefore, examining what people inquire about regarding mental disorders would be useful when designing educational programs for communities. The present study aimed to examine the contents of the queries regarding mental disorders that were posted on online inquiry platforms. A total of 4,714 relevant queries from the two major online inquiry platforms were collected. We computed word frequencies, centralities, and latent Dirichlet allocation (LDA) topic modeling. The words like symptom, hospital and treatment ranked as the most frequently used words, and the word my appeared to have the highest centrality. LDA identified four latent topics: (1) the understanding of general symptoms, (2) a disability grading system and welfare entitlement, (3) stressful life events, and (4) social adaptation with mental disorders. People are interested in practical information concerning mental disorders, such as social benefits, social adaptation, more general information about the symptoms and the treatments. Our findings suggest that instructions encompassing different scopes of information are needed when developing educational programs.

The role of social media for patients with temporomandibular disorders: A content analysis of Reddit

BACKGROUND

Social media is frequently used to discuss health topics among users. Reddit is a popular social media platform particularly suit for discussion about chronic illness because of its anonymity that allow users to express uninhibited feelings. Temporomandibular disorders (TMD) represent a chronic painful disorder which has been rarely studied in terms of social media discussion.

OBJECTIVES

By exploring how Reddit is used to discuss about TMD, we aim to raise awareness to clinicians involved in TMD management about the online discussion on this topic.

METHODS

A quantitative content analysis was performed on a pool of most relevant threads and comments about the topic "TMJ" on Reddit. Following a codebook, two independent coders assessed multiple clinically relevant variables. A third subject resolved eventual discrepancies.

RESULTS

Reddit is mostly used by subjects with TMD asking for advice to other users about symptoms and treatment modalities. The most discussed causes of TMD were bruxism and dental occlusion, and the most discussed treatments were oral appliance therapy and complementary and alternative treatments. The most favourable opinions about treatment modalities were for self-care and behavioural therapy while the least favourable opinions were for surgery and irreversible dental treatments.

CONCLUSIONS

Reddit represents an excellent data-mining platform to retrieve valuable information about health-related discussion by the community. Our findings suggest an overall alignment of such discussion with evidence-based science about TMD; however, to further increase this trend, we encourage healthcare provider to take an active role in the digital spread of scientifically valid information.

Teens' Social Media Engagement during the COVID-19 Pandemic: A Time Series Examination of Posting and Emotion on Reddit

Research has rarely examined how the COVID-19 pandemic may affect teens' social media engagement and psychological wellbeing, and even less research has compared the difference between teens with and without mental health concerns. We collected and analyzed weekly data from January to December 2020 from teens in four Reddit communities (subreddits), including teens in r/Teenagers and teens who participated in three mental health subreddits (r/Depression, r/Anxiety, and r/SuicideWatch). The results showed that teens' weekly subreddit participation, posting/commenting frequency, and emotion expression were related to significant pandemic events. Teen Redditors on r/Teenagers had a higher posting/commenting frequency but lower negative emotion than teen Redditors on the three mental health subreddits. When comparing posts/comments on r/Teenagers, teens who ever visited one of the three mental health subreddits posted/commented twice as frequently as teens who did not, but their emotion expression was similar. The results from the Interrupted Time Series Analysis (ITSA) indicated that both teens with and without mental health concerns reversed the trend in posting frequency and negative emotion from declining to increasing right after the pandemic outbreak, and teens with mental health concerns had a more rapidly increasing trend in posting/commenting. The findings suggest that teens' social media engagement and emotion expression reflect the pandemic evolution. Teens with mental health concerns are more likely to reveal their emotions on specialized mental health subreddits rather than on the general r/Teenagers subreddit. In addition, the findings indicated that teens with mental health concerns had a strong social interaction desire that various barriers in the real world may inhibit. The findings call for more attention to understand the pandemic's influence on teens by monitoring and analyzing social media data and offering adequate support to teens regarding their mental health wellbeing.

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

BACKGROUND

Many social media studies have explored the ability of thematic structures, such as hashtags and subreddits, to identify information related to a wide variety of mental health disorders. However, studies and models trained on specific themed communities are often difficult to apply to different social media platforms and related outcomes. A deep learning framework using thematic structures from Reddit and Twitter can have distinct advantages for studying alcohol abuse, particularly among the youth in the United States.

OBJECTIVE

This study proposes a new deep learning pipeline that uses thematic structures to identify alcohol-related content across different platforms. We apply our method on Twitter to determine the association of the prevalence of alcohol-related tweets with alcohol-related outcomes reported from the National Institute of Alcoholism and Alcohol Abuse, Centers for Disease Control Behavioral Risk Factor Surveillance System, county health rankings, and the National Industry Classification System.

METHODS

The Bidirectional Encoder Representations From Transformers neural network learned to classify 1,302,524 Reddit posts as either alcohol-related or control subreddits. The trained model identified 24 alcohol-related hashtags from an unlabeled data set of 843,769 random tweets. Querying alcohol-related hashtags identified 25,558,846 alcohol-related tweets, including 790,544 location-specific (geotagged) tweets. We calculated the correlation between the prevalence of alcohol-related tweets and alcohol-related outcomes, controlling for confounding effects of age, sex, income, education, and self-reported race, as recorded by the 2013-2018 American Community Survey.

RESULTS

Significant associations were observed: between alcohol-hashtagged tweets and alcohol consumption (P=.01) and heavy drinking (P=.005) but not binge drinking (P=.37), self-reported at the metropolitan-micropolitan statistical area level; between alcohol-hashtagged tweets and self-reported excessive drinking behavior (P=.03) but not motor vehicle fatalities involving alcohol (P=.21); between alcohol-hashtagged tweets and the number of breweries (P<.001), wineries (P<.001), and beer, wine, and liquor stores (P<.001) but not drinking places (P=.23), per capita at the US county and county-equivalent level; and between alcohol-hashtagged tweets and all gallons of ethanol consumed (P<.001), as well as ethanol consumed from wine (P<.001) and liquor (P=.01) sources but not beer (P=.63), at the US state level.

CONCLUSIONS

Here, we present a novel natural language processing pipeline developed using Reddit's alcohol-related subreddits that identify highly specific alcohol-related Twitter hashtags. The prevalence of identified hashtags contains interpretable information about alcohol consumption at both coarse (eg, US state) and fine-grained (eg, metropolitan-micropolitan statistical area level and county) geographical designations. This approach can expand research and deep learning interventions on alcohol abuse and other behavioral health outcomes.

Evolutionary Overview of Consumer Health Informatics: Bibliometric Study on the Web of Science from 1999 to 2019

BACKGROUND

Consumer health informatics (CHI) originated in the 1990s. With the rapid development of computer and information technology for health decision making, an increasing number of consumers have obtained health-related information through the internet, and CHI has also attracted the attention of an increasing number of scholars.

OBJECTIVE

The aim of this study was to analyze the research themes and evolution characteristics of different study periods and to discuss the dynamic evolution path and research theme rules in a time-series framework from the perspective of a strategy map and a data flow in CHI.

METHODS

The Web of Science core collection database of the Institute for Scientific Information was used as the data source to retrieve relevant articles in the field of CHI. SciMAT was used to preprocess the literature data and construct the overlapping map, evolution map, strategic diagram, and cluster network characterized by keywords. Besides, a bibliometric analysis of the general characteristics, the evolutionary characteristics of the theme, and the evolutionary path of the theme was conducted.

RESULTS

A total of 986 articles were obtained after the retrieval, and 931 articles met the document-type requirement. In the past 21 years, the number of articles increased every year, with a remarkable growth after 2015. The research content in 4 different study periods formed the following 38 themes: patient education, medicine, needs, and bibliographic database in the 1999-2003 study period; world wide web, patient education, eHealth, patients, medication, terminology, behavior, technology, and disease in the 2004-2008 study period; websites, information seeking, physicians, attitudes, technology, risk, food labeling, patient, strategies, patient education, and eHealth in the 2009-2014 study period; and electronic medical records, health information seeking, attitudes, health communication, breast cancer, health literacy, technology, natural language processing, user-centered design, pharmacy, academic libraries, costs, internet utilization, and online health information in the 2015-2019 study period. Besides, these themes formed 10 evolution paths in 3 research directions: patient education and intervention, consumer demand attitude and behavior, and internet information technology application.

CONCLUSIONS

Averaging 93 publications every year since 2015, CHI research is in a rapid growth period. The research themes mainly focus on patient education, health information needs, health information search behavior, health behavior intervention, health literacy, health information technology, eHealth, and other aspects. Patient education and intervention research, consumer demand, attitude, and behavior research comprise the main theme evolution path, whose evolution process has been relatively stable. This evolution path will continue to become the research hotspot in this field. Research on the internet and information technology application is a secondary theme evolution path with development potential.

Reddit Users' Experiences of Suicidal Thoughts During the COVID-19 Pandemic: A Qualitative Analysis of r/Covid19_support Posts

Background: The COVID-19 pandemic is having considerable impacts on population-level mental health, with research illustrating an increased prevalence in suicidal thoughts due to pandemic stressors. While the drivers of suicidal thoughts amid the pandemic are poorly understood, qualitative research holds great potential for expanding upon projections from pre-pandemic work and nuancing emerging epidemiological data. Despite calls for qualitative inquiry, there is a paucity of qualitative research examining experiences of suicidality related to COVID-19. The use of publicly available data from social media offers timely and pertinent information into ongoing pandemic-related mental health, including individual experiences of suicidal thoughts. Objective: To examine how Reddit users within the r/COVID19_support community describe their experiences of suicidal thoughts amid the COVID-19 pandemic. Methods: This study draws on online posts from within r/COVID19_support that describe users' suicidal thoughts during and related to the COVID-19 pandemic. Data were collected from creation of this subreddit on February 12, 2020 until December 31, 2020. A qualitative thematic analysis was conducted to generate themes reflecting users' experiences of suicidal thoughts. Results: A total of 83 posts from 57 users were included in the analysis. Posts described a range of users' lived and living experiences of suicidal thoughts related to the pandemic, including deterioration in mental health and complex emotions associated with suicidal thinking. Reddit users situated their experiences of suicidal thoughts within various pandemic stressors: social isolation, employment and finances, virus exposure and COVID-19 illness, uncertain timeline of the pandemic, news and social media, pre-existing mental health conditions, and lack of access to mental health resources. Some users described individual coping strategies and supports used in attempt to manage suicidal thoughts, however these were recognized as insufficient for addressing the multilevel stressors of the pandemic. Conclusions: Multiple and intersecting stressors have contributed to individuals' experiences of suicidal thoughts amid the COVID-19 pandemic, requiring thoughtful and complex public health responses. While ongoing challenges exist with self-disclosure of mental health challenges on social media, Reddit and other online platforms may offer a space for users to share suicidal thoughts and discuss potential coping strategies.

Analyses of posts written in online eating disorder and depression/anxiety moderated communities: Emotional and informational communication before and during the COVID-19 outbreak

INTRODUCTION

Moderated online health communities (OHCs) are digital platforms that provide a means for patients with similar medical conditions to communicate with each other under the supervision of healthcare professionals.

AIMS

To examine the impact of the COVID-19 outbreak on content and type of posts published in two moderated OHCs - eating disorders and depression/anxiety - by comparing categorizations of posts written before vs. after the lockdown, and about vs. not about the pandemic.

METHODS

Posts were retrieved from Camoni, the first Israeli medical social network (January-June 2017, March-May 2020). A total of 1475 posts were analyzed. Of them, 802 posts were written before and 680 were written during the first lockdown. Posts were divided into two main categories: informational and emotional, and into fourteen subcategories.

RESULTS

Before the pandemic, the eating disorders OHC was characterized as primarily emotional (emotional: 66.7%, informational: 45.4%) and the depression/anxiety OHC as primarily informational (emotional: 49.8%, informational: 65.8%) (χ² = 31.6, p < 0.001). During the lockdown, there was a transition in the eating disorders community, from primarily emotional to primarily informational communication (emotional: 46.1%, informational: 71.7%) (χ² = 30.3, p < 0.001). In both OHCs, only about one in six posts written during the lockdown was related to the pandemic. There were only minimal differences in subcategorization of posts written before vs. after the outbreak (e.g., searching for medical information was more common during the pandemic: χ² = 40.9, p < 0.001), as well as about vs. not about the pandemic (e.g., sharing negative emotions was more common when writing about the pandemic: χ² = 4.1, p = 0.43).

CONCLUSION

During the first lockdown, people with eating disorders have increased their use of OHCs as sources of informational (as opposed to emotional) support, but the overall impact of the pandemic on the content of posts written in the examined OHCs was minimal, suggesting that OHCs have not changed their function as a valuable means of providing emotional and informational support for people with mental difficulties.

COVID-19 Pandemic Impact on Substance Misuse: A Social Media Listening, Mixed Method Analysis

The restrictive measures adopted during the COVID-19 pandemic modified some previously consolidated drug use patterns. A focus on social networks allowed drug users to discuss, share opinions and provide advice during a worldwide emergency context. In order to explore COVID-19-related implications on drug trends/behaviour and on most popular psychotropic substances debated, the focus here was on the constantly updated, very popular, Reddit social platform’s posts and comments. A quantitative and qualitative analysis of r/Drugs and related subreddits, using a social media listening netnographic approach, was carried out. The post/comments analysed covered the time-frame December 2019–May 2020. Between December 2019 and May 2020, the number of whole r/Drugs subreddit members increased from 619,563 to 676,581 members, respectively, thus increasing by 9.2% by the end of the data collection. Both the top-level r/Drugs subreddit and 92 related subreddits were quantitatively analysed, with posts/comments related to 12 drug categories. The drugs most frequently commented on included cannabinoids, psychedelics, opiates/opioids, alcohol, stimulants and prescribed medications. The qualitative analysis was carried out focussing on four subreddits, relating to some 1685 posts and 3263 comments. Four main themes of discussion (e.g., lockdown-associated immunity and drug intake issues; drug-related behaviour/after-quarantine plans’ issues; lockdown-related psychopathological issues; and peer-to-peer advice at the time of COVID-19) and four categories of Redditors (e.g., those continuing the use of drugs despite the pandemic; the “couch epidemiologists”; the conspirationists/pseudo-science influencers; and the recovery-focused users) were tentatively identified here. A mixed-methods, social network-based analysis provided a range of valuable information on Redditors’ drug use/behaviour during the first phase of the COVID-19 pandemic. Further studies should be carried out focusing on other social networks as well as later phases of the pandemic.

Experiences of Mental Distress during COVID-19: Thematic Analysis of Discussion Forum Posts for Anxiety, Depression, and Obsessive-Compulsive Disorder

The psychological impact of the COVID-19 pandemic on coronavirus patients, health care workers, and the general population is clear. Relatively few studies have, however, considered the impact of the pandemic on those with pre-existing mental health conditions. Therefore, the present study investigates the personal experiences of those with anxiety, depression, and obsessive-compulsive disorder during COVID-19. We conducted a qualitative study utilising Reddit discussion forum posts. We conducted three separate thematic analyses from 130 posts in subreddit forums aimed for people identifying with anxiety, depression, and obsessive-compulsive disorder. We identified a number of similar discussion forum themes (e.g., COVID-19 intensifying symptoms and a lack of social support), as well as themes that were unique to each forum type (e.g., hyperawareness and positive experiences during the pandemic). Findings should guide future practice and the support provided to those living with mental distress.

Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review

BACKGROUND

Despite advancements in artificial intelligence (AI) to develop prediction and classification models, little research has been devoted to real-world translations with a user-centered design approach. AI development studies in the health care context have often ignored two critical factors of ecological validity and human cognition, creating challenges at the interface with clinicians and the clinical environment.

OBJECTIVE

The aim of this literature review was to investigate the contributions made by major human factors communities in health care AI applications. This review also discusses emerging research gaps, and provides future research directions to facilitate a safer and user-centered integration of AI into the clinical workflow.

METHODS

We performed an extensive mapping review to capture all relevant articles published within the last 10 years in the major human factors journals and conference proceedings listed in the "Human Factors and Ergonomics" category of the Scopus Master List. In each published volume, we searched for studies reporting qualitative or quantitative findings in the context of AI in health care. Studies are discussed based on the key principles such as evaluating workload, usability, trust in technology, perception, and user-centered design.

RESULTS

Forty-eight articles were included in the final review. Most of the studies emphasized user perception, the usability of AI-based devices or technologies, cognitive workload, and user's trust in AI. The review revealed a nascent but growing body of literature focusing on augmenting health care AI; however, little effort has been made to ensure ecological validity with user-centered design approaches. Moreover, few studies (n=5 against clinical/baseline standards, n=5 against clinicians) compared their AI models against a standard measure.

CONCLUSIONS

Human factors researchers should actively be part of efforts in AI design and implementation, as well as dynamic assessments of AI systems' effects on interaction, workflow, and patient outcomes. An AI system is part of a greater sociotechnical system. Investigators with human factors and ergonomics expertise are essential when defining the dynamic interaction of AI within each element, process, and result of the work system.

Oral Healthcare Implications of Dedicated Online Communities: A Computational Content Analysis of the r/Dentistry Subreddit

The current study explores communication expressed by participants in a subreddit surrounding oral health care, moderated by dentists and dental hygienists. The corpus was analyzed through Leximancer, a computer-assisted program used for computational content analyses of large data sets. Users' personal disclosures about ongoing dental concerns, advice about others' self-care, and the role of interpersonal communication with and among health care providers emerged as dominant themes. The findings suggest that online communities may serve an important role that dentists are unable to fill in their limited interactions with individual patients. Such interaction spaces may therefore offer a fertile environment for future interventions to promote beneficial practices and achieve positive health-related outcomes.

Understanding the Health Behavior Decision-Making Process with Situational Theory of Problem Solving in Online Health Communities: The Effects of Health Beliefs, Message Credibility, and Communication Behaviors on Health Behavioral Intention

Online health communities (OHCs) offer users the opportunity to share and seek health information through these platforms, which in turn influence users’ health decisions. Understanding what factors influence people’s health decision-making process is essential for not only the design of the OHC, but also for commercial health business who are promoting their products to patients. Previous studies explored the health decision-making process from many factors, but lacked a comprehensive model with a theoretical model. The aim of this paper is to propose a research model from the situational theory of problem solving in relation to forecasting health behaviors in OHCs. An online questionnaire was developed to collect data from 321 members of online health communities (HPV Tieba and HPV vaccina Tieba) who have not received an HPV vaccination. The partial least squares structural equation modeling (PLS-SEM) method was employed for the data analysis. Findings showed that information selection and acquisition is able to forecast HPV vaccination intentions, perceived seriousness and perceived susceptibility can directly impact HPV vaccination intention and have an indirect impact by information selection and acquisition, and perceived message credibility indirectly affected HPV vaccination intention via information selection. The current paper supports health motivations analysis in OHCs, with potential to assist users’ health-related decision-making.

Suitability of Text-Based Communications for the Delivery of Psychological Therapeutic Services to Rural and Remote Communities: Scoping Review

BACKGROUND

People living in rural and remote areas have poorer access to mental health services than those living in cities. They are also less likely to seek help because of self-stigma and entrenched stoic beliefs about help seeking as a sign of weakness. E-mental health services can span great distances to reach those in need and offer a degree of privacy and anonymity exceeding that of traditional face-to-face counseling and open up possibilities for identifying at-risk individuals for targeted intervention.

OBJECTIVE

This scoping review maps the research that has explored text-based e-mental health counseling services and studies that have used language use patterns to predict mental health status. In doing so, one of the aims was to determine whether text-based counseling services have the potential to circumvent the barriers faced by clients in rural and remote communities using technology and whether text-based communications, in particular, can be used to identify individuals at risk of psychological distress or self-harm.

METHODS

We conducted a comprehensive electronic literature search of PsycINFO, PubMed, ERIC, and Web of Science databases for articles published in English through November 2020.

RESULTS

Of the 9134 articles screened, 70 met the eligibility criteria and were included in the review. There is preliminary evidence to suggest that text-based, real-time communication with a qualified therapist is an effective form of e-mental health service delivery, particularly for individuals concerned with stigma and confidentiality. There is also converging evidence that text-based communications that have been analyzed using computational linguistic techniques can be used to accurately predict progress during treatment and identify individuals at risk of serious mental health conditions and suicide.

CONCLUSIONS

This review reveals a clear need for intensified research into the extent to which text-based counseling (and predictive models using modern computational linguistics tools) may help deliver mental health treatments to underserved groups such as regional communities, identify at-risk individuals for targeted intervention, and predict progress during treatment. Such approaches have implications for policy development to improve intervention accessibility in at-risk and underserved populations.

Impact of the COVID-19 Pandemic on Online Obsessive-Compulsive Disorder Support Community Members: Survey Study

BACKGROUND

People with obsessive-compulsive disorder (OCD) have faced unique challenges during the COVID-19 pandemic. Research from the first two months of the pandemic suggests that a small proportion of people with OCD experienced worsening in their OCD symptoms since the pandemic began, whereas the rest experienced either no change or an improvement in their symptoms. However, as society-level factors relating to the pandemic have evolved, the effects of the pandemic on people with OCD have likely changed as well, in complex and population-specific ways. Therefore, this study contributes to a growing body of knowledge on the impact of the COVID-19 pandemic on people and demonstrates how differences across studies might emerge when studying specific populations at specific timepoints.

OBJECTIVE

This study aimed to assess how members of online OCD support communities felt the COVID-19 pandemic had affected their OCD symptoms, around 3 months after the pandemic began.

METHODS

We recruited participants from online OCD support communities for our brief survey. Participants indicated how much they felt their OCD symptoms had changed since the pandemic began and how much they felt that having OCD was making it harder to deal with the pandemic.

RESULTS

We collected survey data from June through August 2020 and received a total of 196 responses, some of which were partial responses. Among the nonmissing data, 65.9% (108/164) of the participants were from the United States and 90.5% (152/168) had been subjected to a stay-at-home order. In all, 92.9% (182/196) of the participants said they experienced worsening of their OCD symptoms since the pandemic began, although the extent to which their symptoms worsened differed across dimensions of OCD; notably, symmetry and completeness symptoms were less likely to have worsened than others. Moreover, 95.5% (171/179) of the participants felt that having OCD made it difficult to deal with the pandemic.

CONCLUSIONS

Our study of online OCD support community members found a much higher rate of OCD symptom worsening than did other studies on people with OCD conducted during the current COVID-19 pandemic. Factors such as quarantine length, location, overlapping society-level challenges, and differing measurement and sampling choices may help to explain this difference across studies.

Humane Visual AI: Telling the Stories Behind a Medical Condition

A biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations.

#Menopause on Instagram: a mixed-methods study

OBJECTIVE

The aims of this study were to: (1) characterize the menopause transition (MT) on social media and (2) determine if concordance or discordance exists when comparing MT-focused social media posts and biomedical research literature.

METHODS

We analyzed 440 sequential Instagram posts with the hashtag #menopause over 2 weeks from January to February 2019. Posts were composed of 299 unique accounts, resulting in an average of 1.7 posts per account (standard deviation [SD] 1; range 1-9; median 1 and interquartile range [IQR] 1-2). Each account had an average of 2,616 followers (SD 11,271; range 3-129,000; median 421.5 and IQR 177-1,101). Content and thematic analyses were completed for posts, images, and videos to identify codes related to the MT. The top 15 codes were then searched along with the key term "menopause" in PubMed to ascertain the level of concordance between Instagram content and peer-reviewed literature on the MT.

RESULTS

We identified 69 codes in our corpus of Instagram content, resulting in 9 categories: physical health, mental health, complementary and integrative health, advertising, social, advice, self-care, nature, and self-expression (kappa 0.95-1.00). The most prevalent codes were related to weight loss/fitness (20.5%) and hormones (18.4%). The majority of frequent codes identified in Instagram posts were infrequently listed in biomedical literature related to menopause. However, there were two codes, Weight loss/Fitness and Hot flashes, that were frequently discussed in Instagram posts and the biomedical literature.

CONCLUSIONS

The examination of #menopause on Instagram provides novel insights for researchers and clinicians. Our findings provide a better understanding of the experiences and support needs of individuals experiencing menopause. Furthermore, codes related to menopause have low prominence in the biomedical literature, suggesting key topics that could be explored in the future.

Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review

Background

Visual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots.

Objective

This scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR).

Methods

Using the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences.

Results

After screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard.

Conclusions

With the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage.

International Registered Report Identifier (IRRID)

RR2-10.2196/14019

Using Social Media to Understand Online Social Factors Concerning Obesity: A Systematic Review (Preprint)

Background

Evidence in the literature surrounding obesity suggests that social factors play a substantial role in the spread of obesity. Although social ties with a friend who is obese increase the probability of becoming obese, the role of social media in this dynamic remains underexplored in obesity research. Given the rapid proliferation of social media in recent years, individuals socialize through social media and share their health-related daily routines, including dieting and exercising. Thus, it is timely and imperative to review previous studies focused on social factors in social media and obesity.

Objective

This study aims to examine web-based social factors in relation to obesity research.

Methods

We conducted a systematic review. We searched PubMed, Association for Computing Machinery, and ScienceDirect for articles published by July 5, 2019. Web-based social factors that are related to obesity behaviors were studied and analyzed.

Results

In total, 1608 studies were identified from the selected databases. Of these 1608 studies, 50 (3.11%) studies met the eligibility criteria. In total, 10 types of web-based social factors were identified, and a socioecological model was adopted to explain their potential impact on an individual from varying levels of web-based social structure to social media users’ connection to the real world.

Conclusions

We found 4 levels of interaction in social media. Gender was the only factor found at the individual level, and it affects user’s web-based obesity-related behaviors. Social support was the predominant factor identified, which benefits users in their weight loss journey at the interpersonal level. Some factors, such as stigma were also found to be associated with a healthy web-based social environment. Understanding the effectiveness of these factors is essential to help users create and maintain a healthy lifestyle.

Discussing non-invasive prenatal testing on Reddit: The benefits, the concerns, and the comradery

OBJECTIVE

As the use of non-invasive prenatal testing (NIPT) increases, its benefits and concerns are being examined through surveys, qualitative studies, and bioethical analysis. However, only scant research has examined public discourse on the topic. This research examined NIPT discussions on the social media platform Reddit.

METHOD

Content and qualitative description analysis was performed on 98 NIPT discussions (2682 comments), obtained by inputting "NIPT" into Reddit's search engine.

RESULTS

Detailing of benefits and concerns was found in collaborative and supportive discussions. Overall, NIPT is seen as valuable and desirable. Some concerns focused on cost-related barriers to access, anxiety related to testing, and interpretation of results. NIPT is often portrayed as offering peace of mind and is sometimes described as a means of preparing for possible outcomes.

CONCLUSION

In the discussions analyzed, NIPT is seen, overall, as valuable and greater access to it is desired. Some questions and concerns about NIPT were evident. Reddit stands as a valuable and appreciated tool for individuals wishing to discuss NIPT and to solicit and share information, opinions, and experiences. Health care providers should consider the ways social platforms such as Reddit can be engaged to better inform and educate the public.