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Article ; Online: The genetic basis of autoimmunity seen through the lens of T cell functional traits.

Lagattuta, Kaitlyn A / Park, Hannah L / Rumker, Laurie / Ishigaki, Kazuyoshi / Nathan, Aparna / Raychaudhuri, Soumya

2024 Volume 15, Issue 1, Page(s) 1204

Abstract: Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including ... ...

Abstract	Autoimmune disease heritability is enriched in T cell-specific regulatory regions of the genome. Modern-day T cell datasets now enable association studies between single nucleotide polymorphisms (SNPs) and a myriad of molecular phenotypes, including chromatin accessibility, gene expression, transcriptional programs, T cell antigen receptor (TCR) amino acid usage, and cell state abundances. Such studies have identified hundreds of quantitative trait loci (QTLs) in T cells that colocalize with genetic risk for autoimmune disease. The key challenge facing immunologists today lies in synthesizing these results toward a unified understanding of the autoimmune T cell: which genes, cell states, and antigens drive tissue destruction?
MeSH term(s)	Humans ; T-Lymphocytes ; Autoimmunity/genetics ; Quantitative Trait Loci/genetics ; Phenotype ; Polymorphism, Single Nucleotide ; Receptors, Antigen, T-Cell/genetics ; Autoimmune Diseases/genetics ; Genome-Wide Association Study
Chemical Substances	Receptors, Antigen, T-Cell
Language	English
Publishing date	2024-02-08
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-024-45170-w
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The T cell receptor sequence influences the likelihood of T cell memory formation.

Lagattuta, Kaitlyn A / Nathan, Aparna / Rumker, Laurie / Birnbaum, Michael E / Raychaudhuri, Soumya

bioRxiv : the preprint server for biology

2023

Abstract: T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell ... ...

Abstract	T cell differentiation depends on activation through the T cell receptor (TCR), whose amino acid sequence varies cell to cell. Particular TCR amino acid sequences nearly guarantee Mucosal-Associated Invariant T (MAIT) and Natural Killer T (NKT) cell fates. To comprehensively define how TCR amino acids affects all T cell fates, we analyze the paired αβTCR sequence and transcriptome of 819,772 single cells. We find that hydrophobic CDR3 residues promote regulatory T cell transcriptional states in both the CD8 and CD4 lineages. Most strikingly, we find a set of TCR sequence features, concentrated in CDR2α, that promotes positive selection in the thymus as well as transition from naïve to memory in the periphery. Even among T cells that recognize the same antigen, these TCR sequence features help to explain which T cells form immunological memory, which is essential for effective pathogen response.
Language	English
Publishing date	2023-07-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.07.20.549939
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Low, Daniel M / Rumker, Laurie / Talkar, Tanya / Torous, John / Cecchi, Guillermo / Ghosh, Satrajit S

Journal of medical Internet research

2020 Volume 22, Issue 10, Page(s) e22635

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Adolescent ; Adult ; Anxiety/diagnosis ; Anxiety/epidemiology ; Anxiety/psychology ; Betacoronavirus ; Borderline Personality Disorder/epidemiology ; Borderline Personality Disorder/psychology ; COVID-19 ; Coronavirus Infections/epidemiology ; Female ; Global Health ; Humans ; Male ; Mental Health/statistics & numerical data ; Middle Aged ; Natural Language Processing ; Pandemics ; Pneumonia, Viral/epidemiology ; SARS-CoV-2 ; Self-Help Groups/statistics & numerical data ; Social Media/statistics & numerical data ; Stress Disorders, Post-Traumatic/epidemiology ; Stress Disorders, Post-Traumatic/psychology ; Suicidal Ideation ; Young Adult
Keywords	covid19
Language	English
Publishing date	2020-10-12
Publishing country	Canada
Document type	Journal Article ; Observational Study ; Research Support, N.I.H., Extramural
ZDB-ID	2028830-X
ISSN	1438-8871 ; 1438-8871
ISSN (online)	1438-8871
ISSN	1438-8871
DOI	10.2196/22635
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Identifying genetic variants that influence the abundance of cell states in single-cell data.

Rumker, Laurie / Sakaue, Saori / Reshef, Yakir / Kang, Joyce B / Yazar, Seyhan / Alquicira-Hernandez, Jose / Valencia, Cristian / Lagattuta, Kaitlyn A / Mah-Som, Annelise / Nathan, Aparna / Powell, Joseph E / Loh, Po-Ru / Raychaudhuri, Soumya

bioRxiv : the preprint server for biology

2023

Language	English
Publishing date	2023-11-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.13.566919
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Efficient and precise single-cell reference atlas mapping with Symphony.

Kang, Joyce B / Nathan, Aparna / Weinand, Kathryn / Zhang, Fan / Millard, Nghia / Rumker, Laurie / Moody, D Branch / Korsunsky, Ilya / Raychaudhuri, Soumya

Nature communications

2021 Volume 12, Issue 1, Page(s) 5890

Abstract: Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a ... ...

Abstract	Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony ( https://github.com/immunogenomics/symphony ), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.
MeSH term(s)	Algorithms ; Computational Biology ; Genome ; Humans ; Single-Cell Analysis ; Software
Language	English
Publishing date	2021-10-07
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-021-25957-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics.

Reshef, Yakir A / Rumker, Laurie / Kang, Joyce B / Nathan, Aparna / Korsunsky, Ilya / Asgari, Samira / Murray, Megan B / Moody, D Branch / Raychaudhuri, Soumya

Nature biotechnology

2021 Volume 40, Issue 3, Page(s) 355–363

Abstract: As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters ... ...

Abstract	As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space-termed neighborhoods-that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.
MeSH term(s)	Cluster Analysis ; Phenotype ; T-Lymphocytes ; Transcriptome/genetics
Language	English
Publishing date	2021-10-21
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1311932-1
ISSN	1546-1696 ; 1087-0156
ISSN (online)	1546-1696
ISSN	1087-0156
DOI	10.1038/s41587-021-01066-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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

Low, Daniel M / Rumker, Laurie / Talkar, Tanya / Torous, John / Cecchi, Guillermo / Ghosh, Satrajit S

J Med Internet Res

Abstract: 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 ... ...

Abstract	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.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #771623
Database	COVID19

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Article ; Online: Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19

Low, Daniel M. / Rumker, Laurie / Talkar, Tanya / Torous, John / Cecchi, Guillermo / Ghosh, Satrajit S

Journal of Medical Internet Research

Observational Study

2020

Abstract	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. NIH (Grants 5T32DC000038-28, 5T32DC000038, 5T32HG2295-17)
Keywords	covid19
Subject code	150
Publisher	JMIR Publications Inc.
Publishing country	us
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Reddit Mental Health Dataset

Low, Daniel M. / Rumker, Laurie / Talker, Tanya / Torous, John / Cecchi, Guillermo / Ghosh, Satrajit S.

2020

Abstract: This dataset contains posts from 28 subreddits (15 mental health support groups) from 2018-2020. We used this dataset to understand the impact of COVID-19 on mental health support groups from January to April, 2020 and included older timeframes to obtain ...

Abstract	This dataset contains posts from 28 subreddits (15 mental health support groups) from 2018-2020. We used this dataset to understand the impact of COVID-19 on mental health support groups from January to April, 2020 and included older timeframes to obtain baseline posts before COVID-19. Please cite if you use this dataset: Low, D. M., Rumker, L., Torous, J., Cecchi, G., Ghosh, S. S., & Talkar, T. (2020). 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, 22(10), e22635. @article{low2020natural, title={Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study}, author={Low, Daniel M and Rumker, Laurie and Torous, John and Cecchi, Guillermo and Ghosh, Satrajit S and Talkar, Tanya}, journal={Journal of medical Internet research}, volume={22}, number={10}, pages={e22635}, year={2020}, publisher={JMIR Publications Inc., Toronto, Canada} } License This dataset is made available under the Public Domain Dedication and License v1.0 whose full text can be found at: http://www.opendatacommons.org/licenses/pddl/1.0/ It was downloaded using pushshift API. Re-use of this data is subject to Reddit API terms. Reddit Mental Health Dataset Contains posts and text features for the following timeframes from 28 mental health and non-mental health subreddits: 15 specific mental health support groups (r/EDAnonymous, r/addiction, r/alcoholism, r/adhd, r/anxiety, r/autism, r/bipolarreddit, r/bpd, r/depression, r/healthanxiety, r/lonely, r/ptsd, r/schizophrenia, r/socialanxiety, and r/suicidewatch) 2 broad mental health subreddits (r/mentalhealth, r/COVID19_support) 11 non-mental health subreddits (r/conspiracy, r/divorce, r/fitness, r/guns, r/jokes, r/legaladvice, r/meditation, r/parenting, r/personalfinance, r/relationships, r/teaching). filenames and corresponding timeframes: post: Jan 1 to April 20, 2020 (called "mid-pandemic" in manuscript; r/COVID19_support appears). Unique users: 320,364. pre: Dec 2018 to Dec 2019. A full year which provides more data for a baseline of Reddit posts. Unique users: 327,289. 2019: Jan 1 to April 20, 2019 (r/EDAnonymous appears). A control for seasonal fluctuations to match post data. Unique users: 282,560. 2018: Jan 1 to April 20, 2018. A control for seasonal fluctuations to match post data. Unique users: 177,089 Unique users across all time windows (pre and 2019 overlap): 826,961. See manuscript Supplementary Materials (https://doi.org/10.31234/osf.io/xvwcy) for more information. Note: if subsampling (e.g., to balance subreddits), we recommend bootstrapping analyses for unbiased results.
Keywords	Natural Language Processing ; Mental Health ; Psychiatry ; COVID-19 ; Reddit ; Social Media ; covid19
Subject code	401
Language	English
Publishing date	2020-07-13
Publishing country	eu
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Natural language processing reveals vulnerable mental health support groups and heightened health anxiety on Reddit during COVID-19

Low, Daniel Mark / Rumker, Laurie / Talkar, Tanya / Torous, John / Cecchi, Guillermo / Ghosh, Satrajit S

an observational study

2020

Abstract: 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 ... ...

Abstract	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.
Keywords	covid19
Publisher	Center for Open Science
Publishing country	us
Document type	Book ; Online
DOI	10.31234/osf.io/xvwcy
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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