scholarly journals Natural Language Processing Insights into LGBTQ+ Youth Mental Health during the COVID-19 Pandemic: Longitudinal Analysis of r/LGBTeens Microcommunity Reveals Increased Anxiety in Topics and Trends (Preprint)

10.2196/29029 ◽  
2021 ◽  
Author(s):  
Hannah Stevens ◽  
Irena Acic ◽  
Sofia Rhea
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Longitudinal Analysis ◽  
Youth Mental Health ◽  
Lgbtq Youth

scholarly journals Natural Language Processing Insights into LGBTQ+ Youth Mental Health during the COVID-19 Pandemic: Longitudinal Analysis of r/LGBTeens Micro-Community Reveals Increased Anxiety in Topics and Trends (Preprint)

2021 ◽  
Author(s):  
Hannah Stevens ◽  
Irena Acic ◽  
Sofia Rhea
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Health Outcomes ◽  
Language Processing ◽  
Coming Out ◽  
Latent Dirichlet Allocation ◽  
Mental Health Outcomes ◽  
Lgbtq Youth ◽  
Social Distancing

BACKGROUND Widespread fear surrounding COVID-19, coupled with the extreme physical and social distancing orders, has caused severe negative mental health outcomes. Yet little is known about how the COVID-19 pandemic is impacting LGBTQ+ youth, who experienced disproportionately high adverse mental health outcomes prior to the COVID-19 pandemic. This study aims to address this knowledge gap. OBJECTIVE This work aims to harness natural language processing (NLP) methodologies to investigate the evolution of conversation topics in the most popular subreddit for LGBTQ+ youth. METHODS We generated a dataset of all r/LGBTeens subreddit posts made between Jan 1, 2020 to Feb 1, 2021. We analyzed meaningful trends in anxiety, anger, and sadness in posts. Since the distribution of anxiety before widespread social distancing orders was meaningfully different from the distribution after (P < .001), we employed Latent Dirichlet Allocation (LDA) to examine topics provoking this shift in anxiety. RESULTS While the present study did not find differences in LGBTQ+ youth anger and sadness, results revealed that anxiety increased significantly during social distancing measures compared to before lockdown (P < .001). Further analysis revealed a list of 10 anxiety-provoking topics discussed during the pandemic: attraction to a friend, coming out, coming out to family, discrimination, education, exploring sexuality, gender pronouns, love/relationship advice, starting a new relationship, and struggling with mental health. CONCLUSIONS Conversation topics related to coming-out, gender and sexual identities, discrimination, and relationships were anxiety provoking for LGBTQ+ youth, both before and after the pandemic. The frequency of these conversations increased with lifestyle disruptors related to the pandemic, reflecting LGBTQ+ teens' increased reliance on anonymous discussion forums as safe spaces for discussing lifestyle stressors during COVID-19 lifestyle disruptions (e.g., school closures).

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

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.

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

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

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

2013 ◽  
Vol 52 (01) ◽  
pp. 33-42 ◽  
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.

scholarly journals Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19: an observational study

2020 ◽  
Author(s):  
Daniel Mark 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 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&lt;.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.

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

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.

scholarly journals Generation and evaluation of artificial mental health records for Natural Language Processing

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Julia Ive ◽  
Natalia Viani ◽  
Joyce Kam ◽  
Lucia Yin ◽  
Somain Verma ◽  
...  
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Health Records

scholarly journals Machine learning and natural language processing in mental health: a systematic review (Preprint)

2019 ◽  
Author(s):  
Aziliz Le Glaz ◽  
Yannis Haralambous ◽  
Deok-Hee Kim-Dufor ◽  
Philippe Lenca ◽  
Romain Billot ◽  
...  
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Systematic Review ◽  
Social Media ◽  
Natural Language Processing ◽  
Clinical Practice ◽  
Natural Language ◽  
Language Processing ◽  
Care Providers ◽  
Medical Databases

BACKGROUND Machine learning (ML) systems are parts of Artificial Intelligence (AI) that automatically learn models from data in order to make better decisions. Natural Language Processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. OBJECTIVE The primary aim of this systematic review is to summarize and characterize studies that used ML and NLP techniques for mental health, in methodological and technical terms. The secondary aim is to consider the interest of these methods in the mental health clinical practice. METHODS This systematic review follows the PRISMA guidelines and is registered on PROSPERO. The research was conducted on 4 medical databases (Pubmed, Scopus, ScienceDirect and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, mental disorder. The exclusion criteria are: languages other than English, anonymization process, case studies, conference papers and reviews. No limitations on publication dates were imposed. RESULTS 327 articles were identified, 269 were excluded, and 58 were included in the review. Results were organized through a qualitative perspective. Even though studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into three categories: patients included in medical databases, patients who came to the emergency room, and social-media users. The main objectives were symptom extraction, severity of illness classification, comparison of therapy effectiveness, psychopathological clues, and nosography challenging. Data from electronic medical records and that from social media were the two major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than "transparent” functioning classifiers. Python was the most frequently used platform. CONCLUSIONS ML and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new knowledge,. and one major category of the population, social-media users, is obviously an imprecise cohort. In addition, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, ML and NLP techniques provide useful information from unexplored data (i.e., patient’s daily habits that are usually inaccessible to care providers). This may be considered to be an additional tool at every step of mental health care: diagnosis, prognosis, treatment efficacy and monitoring. Therefore, ethical issues – like predicting psychiatric troubles or involvement in the physician-patient relationship – remain and should be discussed in a timely manner. ML and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice. CLINICALTRIAL Number CRD42019107376

Using Natural Language Processing to Explore Mental Health Insights from UK Tweets During the COVID-19 Pandemic (Preprint)

10.2196/32449 ◽  
2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing
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