ArticleBrain and neuroscience advances2017
Circadian mood variations in Twitter content.
Article in Brain and neuroscience advances, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Glucocorticoid ultradian rhythmicity differentially regulates mood and resting state networks in the human brain: A randomised controlled clinical trial.Psychoneuroendocrinology · 2021Trial
- Large-scale patterns of self-reported mood and sleep across the day and week on an online cognitive-training platform.PLOS digital health · 2026Article
- Analysis of social media language reveals the psychological interaction of three successive upheavals.Scientific reports · 2025Article
- Will things feel better in the morning? A time-of-day analysis of mental health and wellbeing from nearly 1 million observations.BMJ mental health · 2025Article
- Let's Talk About It in the Morning: How Circadian Rhythms Impact Information Sharing on Social Media.Affective science · 2024Article
- Early morning hour and evening usage habits increase misinformation-spread.Scientific reports · 2024Article
- Risk for Suicide and Homicide Peaks at Night: Findings From the National Violent Death Reporting System, 35 States, 2003-2017.The Journal of clinical psychiatry · 2024Article
- Expecting the Unexpected: Predicting Panic Attacks From Mood, Twitter, and Apple Watch Data.IEEE open journal of engineering in medicine and biology · 2024Article
- Diurnal patterns in Twitter sentiment in Italy and United Kingdom are correlated.Frontiers in psychology · 2023Article
- Computational analyses identify addiction help-seeking behaviors on the social networking website Reddit: Insights into online social interactions and addiction support communities.PLOS digital health · 2022Article
- Global and Local Trends Affecting the Experience of US and UK Healthcare Professionals during COVID-19: Twitter Text Analysis.International journal of environmental research and public health · 2022Article
- RuSentiTweet: a sentiment analysis dataset of general domain tweets in Russian.PeerJ. Computer science · 2022Article
- Chronotype, circadian rhythm, and psychiatric disorders: Recent evidence and potential mechanisms.Frontiers in neuroscience · 2022Review
- The voice of Twitter: observable subjective well-being inferred from tweets in Russian.PeerJ. Computer science · 2022Article
- The Mind After Midnight: Nocturnal Wakefulness, Behavioral Dysregulation, and Psychopathology.Frontiers in network physiology · 2022Article
- How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program.International journal of environmental research and public health · 2021Article
- Twitter, time and emotions.Royal Society open science · 2021Article
- The rhythms of the night: increase in online night activity and emotional resilience during the spring 2020 Covid-19 lockdown.EPJ data science · 2021Article
- Utilizing Machine Learning on Internet Search Activity to Support the Diagnostic Process and Relapse Detection in Young Individuals With Early Psychosis: Feasibility Study.JMIR mental health · 2020Article
- Mining Social Media Data for Biomedical Signals and Health-Related Behavior.Annual review of biomedical data science · 2020Article
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
Abstract
backgroundCircadian regulation of sleep, cognition, and metabolic state is driven by a central clock, which is in turn entrained by environmental signals. Understanding the circadian regulation of mood, which is vital for coping with day-to-day needs, requires large datasets and has classically utilised subjective reporting.
methodsIn this study, we use a massive dataset of over 800 million Twitter messages collected over 4 years in the United Kingdom. We extract robust signals of the changes that happened during the course of the day in the collective expression of emotions and fatigue. We use methods of statistical analysis and Fourier analysis to identify periodic structures, extrema, change-points, and compare the stability of these events across seasons and weekends.
resultsWe reveal strong, but different, circadian patterns for positive and negative moods. The cycles of fatigue and anger appear remarkably stable across seasons and weekend/weekday boundaries. Positive mood and sadness interact more in response to these changing conditions. Anger and, to a lower extent, fatigue show a pattern that inversely mirrors the known circadian variation of plasma cortisol concentrations. Most quantities show a strong inflexion in the morning.
conclusionSince circadian rhythm and sleep disorders have been reported across the whole spectrum of mood disorders, we suggest that analysis of social media could provide a valuable resource to the understanding of mental disorder.
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