Faculty Opinions recommendation of Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study.

Background: The COVID-19 pandemic is exerting a devastating impact on mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. Objective: We leverage natural language processing (NLP) with the goal of characterizing changes in fifteen of the world's largest mental health support groups (e.g., r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with eleven non-mental health groups (e.g., r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. Methods: We create and release the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyze trends from 90 text-derived features such as sentiment analysis, personal pronouns, and a “guns” semantic category. Using supervised machine learning, we classify posts into their respective support group and interpret important features to understand how different problems manifest in language. We apply unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. Results: We find that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately two months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories “economic stress”, “isolation”, and “home” while others such as “motion” significantly decreased. We find that support groups related to attention deficit hyperactivity disorder (ADHD), eating disorders (ED), and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discover that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ = -0.96, P<.001). Using unsupervised clustering, we find the Suicidality and Loneliness clusters more than doubled in amount of posts during the pandemic. Specifically, the support groups for borderline personality disorder and post-traumatic stress disorder became significantly associated with the Suicidality cluster. Furthermore, clusters surrounding Self-Harm and Entertainment emerged. Conclusions: By using a broad set of NLP techniques and analyzing a baseline of pre-pandemic posts, we uncover patterns of how specific mental health problems manifest in language, identify at-risk users, and reveal the distribution of concerns across Reddit which could help provide better resources to its millions of users. We then demonstrate that textual analysis is sensitive to uncover mental health complaints as they arise in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests from the present or the past.

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Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study

Journal of Medical Internet Research ◽

10.2196/22635 ◽

2020 ◽

Vol 22 (10) ◽

pp. e22635 ◽

Cited By ~ 1

Author(s):

Daniel M Low ◽

Laurie Rumker ◽

Tanya Talkar ◽

John Torous ◽

Guillermo Cecchi ◽

...

Keyword(s):

Mental Health ◽

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Support Groups ◽

Language Processing ◽

Mental Health Problems ◽

Health Anxiety ◽

Mental Health Support ◽

Health Support

Background The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. Objective The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world’s largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non–mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. Methods We created and released the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyzed trends from 90 text-derived features such as sentiment analysis, personal pronouns, and semantic categories. Using supervised machine learning, we classified posts into their respective support groups and interpreted important features to understand how different problems manifest in language. We applied unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. Results We found that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately 2 months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories “economic stress,” “isolation,” and “home,” while others such as “motion” significantly decreased. We found that support groups related to attention-deficit/hyperactivity disorder, eating disorders, and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discovered that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ=–0.96, P<.001). Using unsupervised clustering, we found the suicidality and loneliness clusters more than doubled in the number of posts during the pandemic. Specifically, the support groups for borderline personality disorder and posttraumatic stress disorder became significantly associated with the suicidality cluster. Furthermore, clusters surrounding self-harm and entertainment emerged. Conclusions By using a broad set of NLP techniques and analyzing a baseline of prepandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests.

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Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study (Preprint)

10.2196/preprints.22635 ◽

2020 ◽

Author(s):

Daniel M Low ◽

Laurie Rumker ◽

Tanya Talkar ◽

John Torous ◽

Guillermo Cecchi ◽

...

Keyword(s):

Mental Health ◽

Machine Learning ◽

Natural Language Processing ◽

Natural Language ◽

Support Groups ◽

Language Processing ◽

Mental Health Problems ◽

Health Anxiety ◽

Mental Health Support ◽

Health Support

BACKGROUND The COVID-19 pandemic is impacting mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit. OBJECTIVE The aim of this study is to leverage natural language processing (NLP) with the goal of characterizing changes in 15 of the world’s largest mental health support groups (eg, r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with 11 non–mental health groups (eg, r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic. METHODS We created and released the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyzed trends from 90 text-derived features such as sentiment analysis, personal pronouns, and semantic categories. Using supervised machine learning, we classified posts into their respective support groups and interpreted important features to understand how different problems manifest in language. We applied unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic. RESULTS We found that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately 2 months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories “economic stress,” “isolation,” and “home,” while others such as “motion” significantly decreased. We found that support groups related to attention-deficit/hyperactivity disorder, eating disorders, and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discovered that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ=–0.96, <i>P</i><.001). Using unsupervised clustering, we found the suicidality and loneliness clusters more than doubled in the number of posts during the pandemic. Specifically, the support groups for borderline personality disorder and posttraumatic stress disorder became significantly associated with the suicidality cluster. Furthermore, clusters surrounding self-harm and entertainment emerged. CONCLUSIONS By using a broad set of NLP techniques and analyzing a baseline of prepandemic posts, we uncovered patterns of how specific mental health problems manifest in language, identified at-risk users, and revealed the distribution of concerns across Reddit, which could help provide better resources to its millions of users. We then demonstrated that textual analysis is sensitive to uncover mental health complaints as they appear in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests.

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Natural language processing in mental health applications using non-clinical texts

Natural Language Engineering ◽

10.1017/s1351324916000383 ◽

2017 ◽

Vol 23 (5) ◽

pp. 649-685 ◽

Cited By ~ 41

Author(s):

RAFAEL A. CALVO ◽

DAVID N. MILNE ◽

M. SAZZAD HUSSAIN ◽

HELEN CHRISTENSEN

Keyword(s):

Mental Health ◽

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Mental States ◽

Data Sources ◽

Computational Techniques ◽

Mental Health Support ◽

Psychological Assistance

AbstractNatural language processing (NLP) techniques can be used to make inferences about peoples’ mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

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Using Natural Language Processing to Assess the Psychological Effect of COVID-19 Pandemic on Insomnia via Tweets: A Pre-Post Retrospective Pilot Study (Preprint)

10.2196/preprints.33454 ◽

2021 ◽

Author(s):

Arash Maghsoudi ◽

Sara Nowakowski ◽

Ritwick Agrawal ◽

Amir Sharafkhaneh ◽

Sadaf Aram ◽

...

Keyword(s):

Mental Health ◽

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Language Processing ◽

Temporal Analysis ◽

Health Condition ◽

Additional Stress ◽

Mental Health Condition ◽

Twitter Users

BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a higher incidence of insomnia. In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. OBJECTIVE In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. METHODS We designed a pre-post retrospective study using public social media content from Twitter. We categorized tweets based on time into two intervals: prepandemic (01/01/2019 to 01/01/2020) and pandemic (01/01/2020 to 01/01/2021). We used NLP to analyze polarity (positive/negative) and intensity of emotions and also users’ tweets psychological states in terms of sadness, anxiety and anger by counting the words related to these categories in each tweet. Additionally, we performed temporal analysis to examine the effect of time on the users’ insomnia experience. RESULTS We extracted 268,803 tweets containing the word insomnia (prepandemic, 123,293 and pandemic, 145,510). The odds of negative tweets (OR, 1.31; 95% CI, 1.29-1.33), anger (OR, 1.19; 95% CI, 1.16-1.21), and anxiety (OR, 1.24; 95% CI: 1.21-1.26) were higher during the pandemic compared to prepandemic. The likelihood of negative tweets after midnight was higher than for other daily intevals, comprising approximately 60% of all negative insomnia-related tweets in 2020 and 2021 collectively. CONCLUSIONS Twitter users shared more negative tweets about insomnia during the pandemic than during the year before. Also, more anger and anxiety-related content were disseminated during the pandemic on the social media platform. Future studies using an NLP framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

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A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness (Preprint)

10.2196/preprints.29768 ◽

2021 ◽

Author(s):

Vishal Dey ◽

Peter Krasniak ◽

Minh Nguyen ◽

Clara Lee ◽

Xia Ning

Keyword(s):

Mental Health ◽

Social Media ◽

Natural Language Processing ◽

Data Analysis ◽

Natural Language ◽

Language Processing ◽

Breast Implant ◽

Public Attention ◽

Social Media Data ◽

Media Data

BACKGROUND A new illness can come to public attention through social media before it is medically defined, formally documented, or systematically studied. One example is a condition known as breast implant illness (BII), which has been extensively discussed on social media, although it is vaguely defined in the medical literature. OBJECTIVE The objective of this study is to construct a data analysis pipeline to understand emerging illnesses using social media data and to apply the pipeline to understand the key attributes of BII. METHODS We constructed a pipeline of social media data analysis using natural language processing and topic modeling. Mentions related to signs, symptoms, diseases, disorders, and medical procedures were extracted from social media data using the clinical Text Analysis and Knowledge Extraction System. We mapped the mentions to standard medical concepts and then summarized these mapped concepts as topics using latent Dirichlet allocation. Finally, we applied this pipeline to understand BII from several BII-dedicated social media sites. RESULTS Our pipeline identified topics related to toxicity, cancer, and mental health issues that were highly associated with BII. Our pipeline also showed that cancers, autoimmune disorders, and mental health problems were emerging concerns associated with breast implants, based on social media discussions. Furthermore, the pipeline identified mentions such as rupture, infection, pain, and fatigue as common self-reported issues among the public, as well as concerns about toxicity from silicone implants. CONCLUSIONS Our study could inspire future studies on the suggested symptoms and factors of BII. Our study provides the first analysis and derived knowledge of BII from social media using natural language processing techniques and demonstrates the potential of using social media information to better understand similar emerging illnesses. CLINICALTRIAL

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Predictors of Seeking Health Information and Mental Health Support in U.S. Prisons: A Study Using 2014 PIAAC Data

Innovation in Aging ◽

10.1093/geroni/igaa057.1082 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 337-337

Author(s):

Candidus Nwakasi ◽

Darlingtina Esiaka ◽

Janardan Subedi

Keyword(s):

Mental Health ◽

General Population ◽

Health Information ◽

Support Groups ◽

Social Trust ◽

Well Being ◽

Mental Illnesses ◽

Literacy Skill ◽

Mental Health Support ◽

Health Support

Abstract Being in prison increases the vulnerability to poor health, especially mental illnesses. This is evident in the documented health disparities between prison inmates and the general population. For example, suicide rates among inmates are higher than in the general population. There is an urgent need to understand how inmates experience mental well-being. This is important as some inmates serve long/life sentences and some will need to successfully re-integrate into the society. Although they have a constitutional right to health care access through the Eight Amendment, little is known of the health information and mental health support seeking patterns among inmates. The current study examined factors associated with the amount of health information accessed, and participation in mental health support groups in US prisons. Data (N= 645) from the Program for the International Assessment of Adult Competencies (2014) were analyzed using linear and logistic regressions. Sample weights were applied in the analyses. Results show statistically significant relationships between amount of health information acquired and age (66 years and above), race, health-status, readiness to learn, literacy skill, and numeracy skill. Social trust moderated the effect of education on the odds of participating in mental health support groups. Also, gender, work duration, attending substance abuse support and life skills groups were significant predictors. Our study may provide insight for stakeholders (e.g., policymakers, clinicians, social workers, and wardens, etc.) working in partnership to deliver a more tailored health interventions for inmates, by highlighting key contextual issues predicting mental health and well-being within prison settings.

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A Proof of Concept for Assessing Emergency Room Use with Primary Care Data and Natural Language Processing

Methods of Information in Medicine ◽

10.3414/me12-01-0012 ◽

2013 ◽

Vol 52 (01) ◽

pp. 33-42 ◽

Cited By ~ 11

Author(s):

M.-H. Kuo ◽

P. Gooch ◽

J. St-Maurice

Keyword(s):

Mental Health ◽

Primary Care ◽

Natural Language Processing ◽

Natural Language ◽

Emergency Room ◽

Language Processing ◽

Free Text ◽

Proof Of Concept ◽

Term Extraction ◽

Primary Care Data

SummaryObjective: The objective of this study was to undertake a proof of concept that demonstrated the use of primary care data and natural language processing and term extraction to assess emergency room use. The study extracted biopsychosocial concepts from primary care free text and related them to inappropriate emergency room use through the use of odds ratios.Methods: De-identified free text notes were extracted from a primary care clinic in Guelph, Ontario and analyzed with a software toolkit that incorporated General Architecture for Text Engineering (GATE) and MetaMap components for natural language processing and term extraction.Results: Over 10 million concepts were extracted from 13,836 patient records. Codes found in at least 1% percent of the sample were regressed against inappropriate emergency room use. 77 codes fell within the realm of biopsychosocial, were very statistically significant (p < 0.001) and had an OR > 2.0. Thematically, these codes involved mental health and pain related concepts.Conclusions: Analyzed thematically, mental health issues and pain are important themes; we have concluded that pain and mental health problems are primary drivers for inappropriate emergency room use. Age and sex were not significant. This proof of concept demonstrates the feasibly of combining natural language processing and primary care data to analyze a system use question. As a first work it supports further research and could be applied to investigate other, more complex problems.

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Mental Well-Being and Its Associated Factors Among Myanmar Migrant Workers in Penang, Malaysia

Asia Pacific Journal of Public Health ◽

10.1177/1010539520940199 ◽

2020 ◽

Vol 32 (6-7) ◽

pp. 320-327 ◽

Cited By ~ 1

Author(s):

Mila Nu Nu Htay ◽

Swe Swe Latt ◽

Khine Sandar Maung ◽

Wai Wai Myint ◽

Soe Moe

Keyword(s):

Mental Health ◽

Support Groups ◽

Migrant Workers ◽

Short Form ◽

Well Being ◽

Cross Sectional Study ◽

Cross Sectional ◽

Mental Health Support ◽

Health Support ◽

Mental Well Being

International migration has become a global phenomenon bringing with it complex and interrelated issues related to the physical and mental well-being of the people involved. This study investigated the mental well-being and factors associated with mental health among Myanmar migrant workers (MMW) in Malaysia. The cross-sectional study was conducted in Penang, Malaysia by using the WHO-5 Well-Being Index Scale (WHO-5) and the Mental Health subscale of 36 items in the Short Form Health Survey (SF-36). Among 192 migrant workers who were understudied, 79.2% had poor mental well-being according to the WHO-5 scale. The duration of stay in Malaysia and without receiving financial aid from their employers despite having a physical illness were significantly associated with poor mental well-being. Mental health support groups should target migrant workers for mental health education and find ways to provide assistance for them. Furthermore, premigration training should be delivered at the country of origin that also provides information on the availability of mental health support in the host country.

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An Exploration of Impact of COVID 19 on mental health -Analysis of tweets using Natural Language Processing techniques

10.1101/2020.07.30.20165571 ◽

2020 ◽

Author(s):

Sohini Sengupta ◽

Sareeta Mugde ◽

Garima Sharma

Keyword(s):

Mental Health ◽

Social Media ◽

Natural Language Processing ◽

Natural Language ◽

Sentiment Analysis ◽

Language Processing ◽

Gold Mine ◽

Social Media Platforms ◽

Processing Techniques

Twitter is one of the world's biggest social media platforms for hosting abundant number of user-generated posts. It is considered as a gold mine of data. Majority of the tweets are public and thereby pullable unlike other social media platforms. In this paper we are analyzing the topics related to mental health that are recently (June, 2020) been discussed on Twitter. Also amidst the on-going pandemic, we are going to find out if covid-19 emerges as one of the factors impacting mental health. Further we are going to do an overall sentiment analysis to better understand the emotions of users.

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