Séminaire Donnees et APprentissage ArtificielRSS

Circadian Mood Variations in Twitter Content

Intervenant(s) : Fabon Dzogang (Intelligent Systems Laboratory, UK)
Circadian 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. We use a massive dataset of over 800 million Twitter messages collected over the course of 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. We 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. Since 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.
Fabon defended his PhD thesis in Computer Science in 2013 “on Learning and Representation from Texts for both Emotional and dynamical Information” at the University of Pierre et Marie Curie, in the DAPA department at LIP6. After graduating he held a short post-doctoral position in LIP6, working on building interpretable models for the classification of multivariate time series’ data. At this time he grew an interest in the analysis of time series’ data, and in the Fourier transform as a mean to extract meaningful features from data. He later joined the University of Bristol as a research associate in 2014 where he worked on efficient machine learning algorithms for data streams, and developed tools to study our human behaviours at a collective level via the analysis of the social media and large samples of press archives. He combined his works on information dynamics and his interest in the study of emotions to research periodic patterns of emotions and mental health. His results provide evidence that a share of the variance in our collective behaviours and emotions are predictable across the year, and over the 24-h cycle.
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benjamin.piwowarski (at) nulllip6.fr
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