Evidence map›Paper›PMID 39892758›Full record

ArticleJournal of affective disorders2025

Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.

Abigail Beech, Haoxue Fan, Jocelyn Shu, Javiera Oyarzun, Peter Nadel, Olivia T Karaman, Sophia Vranos, Elizabeth A Phelps, M Alexandra Kredlow

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In one paragraph

Article in Journal of affective disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Abigail BeechDepartment of Psychology, Tufts University, 490 Boston Ave, Medford, MA 02155, USA; Department of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA. Electronic address: abeech@bu.edu.
Haoxue FanDepartment of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA.
Jocelyn ShuDepartment of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA.
Javiera OyarzunDepartment of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA.
Peter NadelResearch Technology, Tufts University, 16 Dearborn Rd, Somerville, MA 02144, USA.
Olivia T KaramanDepartment of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA.
Sophia VranosDepartment of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA.
Elizabeth A PhelpsDepartment of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA; Center for Brain Science, Harvard University, 52 Oxford, St Cambridge, MA 02138, USA.
M Alexandra KredlowDepartment of Psychology, Tufts University, 490 Boston Ave, Medford, MA 02155, USA; Department of Psychology, Harvard University, 33 Kirkland, St Cambridge, MA 02138, USA.

Funding

Stress, Arousal and Mood: Affective Influences on Decisions under UncertaintyR01DA042855 · NIDA · NEW YORK UNIVERSITY · PI PHELPS, ELIZABETH ANYA · 2017 to 2021
$2.6M
NIDA NIH HHS R01 DA042855
6 · The paper itself

Abstract

backgroundCombining data-driven natural language processing techniques with traditional methods using predefined word lists may offer greater insights into the connections between language patterns and depression and anxiety symptoms, particularly within specific stressful contexts.

methodsBetween 2020 and 2021, 1106 participants wrote narrative responses describing their experiences during the COVID-19 pandemic and completed the Depression Anxiety Stress Scale-21 (DASS). We investigated language patterns associated with DASS symptoms using established categories from Linguistic Inquiry and Word Count (LIWC) and sentiment analysis, as well as exploratory natural language processing techniques. Finally, we constructed machine learning regression models in order to assess how much of the variance in DASS symptoms is related to language use.

resultsWe found significant positive bivariate correlations between total DASS symptoms and hypothesized LIWC categories: first-person singular pronouns, absolute language, and negative emotion words. These results remained largely similar when using negative sentiment scores and when statistically controlling for gender, age, and education. Exploratory n-gram analyses also revealed new individual words and phrases correlated with total DASS symptoms. Lastly, our regression models demonstrated a significant association between language use and total DASS symptoms (R

conclusionsThe current study is one of the first to examine associations between language use and DASS symptoms during the pandemic using both traditional and data-driven techniques. These results replicate and extend prior findings regarding negative emotion and absolute language and identify unique correlates of DASS symptoms during pandemic-related stress, contributing to the literature on language and mental health more broadly.

Indexed as

AnxietyCOVID-19DepressionNatural Language ProcessingStress, PsychologicalAdultFemaleHumansMachine LearningMaleMiddle AgedSARS-CoV-2Young AdultAnxietyDepressionLanguageMachine learningNatural language processing

Identifiers

PMID39892758
PMCPMC11927753

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.