ArticlePloS one2024
Negative affect variability differs between anxiety and depression on social media.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 10 citations in OpenAlex.
- Early predictors of the childhood dysregulation profile from ages 5 to 8: evidence from the UK household study.European child & adolescent psychiatry · 2026Article
- Naturalistic Online Language as a Marker of Depression in Midlife and Older Adults: Computational Text Analysis of Bluesky and Reddit Posts.Journal of medical Internet research · 2026Article
- Daily Negative Affect and Reaction Time Inconsistency in Emerging Adults: Ecological Momentary Assessment Study.JMIR mHealth and uHealth · 2026Article
- Psychometric validation and measurement invariance of the Polish Multi-Affect Indicator across diverse demographic and occupational groups.Scientific reports · 2026Article
- Understanding mental health discourse on Reddit with transformers and explainability.Scientific reports · 2026Article
- Forging Online Community Among People in Recovery From Substance Use: Natural Language Processing and Deep-Learning Analysis of The Phoenix App User-Generated Data.JMIR mHealth and uHealth · 2025Article
- Emoji or real emotions? The effect of digitalization on the quality of life through social media addiction - a multigroup SEM analysis, moderated by positive and negative affect.BMC psychology · 2025Article
- Anxiety and Depression are Associated with More Distorted Thinking on Social Media: A Longitudinal Multi-Method Study.Cognitive therapy and research · 2025Article
- Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.Journal of affective disorders · 2025Article
- Positive and Negative Affect Differentially Predict Individual Differences and Intra-Individual Changes in Daily Cognitive Failures in Younger and Older Adults.Brain sciences · 2024Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveNegative affect variability is associated with increased symptoms of internalizing psychopathology (i.e., depression, anxiety). The Contrast Avoidance Model (CAM) suggests that individuals with anxiety avoid negative emotional shifts by maintaining pathological worry. Recent evidence also suggests that the CAM can be applied to major depression and social phobia, both characterized by negative affect changes. Here, we compare negative affect variability between individuals with a variety of anxiety and depression diagnoses by measuring the levels and degree of change in the sentiment of their online communications.
methodParticipants were 1,853 individuals on Twitter who reported that they had been clinically diagnosed with an anxiety disorder (A cohort, n = 896) or a depressive disorder (D cohort, n = 957). Mean negative affect (NA) and negative affect variability were calculated using the Valence Aware Dictionary for Sentiment Reasoning (VADER), an accurate sentiment analysis tool that scores text in terms of its negative affect content.
resultsFindings showed differences in negative affect variability between the D and A cohort, with higher levels of NA variability in the D cohort than the A cohort, U = 367210, p < .001, r = 0.14, d = 0.25. Furthermore, we found that A and D cohorts had different average NA, with the D cohort showing higher NA overall, U = 377368, p < .001, r = 0.12, d = 0.21. LIMITATIONS: Our sample is limited to individuals who disclosed their diagnoses online, which may involve bias due to self-selection and stigma. Our sentiment analysis of online text may not completely capture all nuances of individual affect.
conclusionsIndividuals with depression diagnoses showed a higher degree of negative affect variability compared to individuals with anxiety disorders. Our findings support the idea that negative affect variability can be measured using computational approaches on large-scale social media data and that social media data can be used to study naturally occurring mental health effects at scale.
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