Séminaire Donnees et APprentissage Artificiel
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Diurnal variations of psychometric indicators in Twitter content
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Intervenant(s) : Fabon Dzogang (U. Bristol, UK)The psychological state of a person is characterised by cognitive and emotional variables which can be inferred by psychometric methods. Using the word lists from the Linguistic Inquiry and Word Count, designed to infer a range of psychological states from the word usage of a person, we studied temporal changes in the average expression of psychological traits in the general population. We sampled the contents of Twitter in the United Kingdom at hourly intervals for a period of four years, revealing a strong diurnal rhythm in most of the psychometric variables, and finding that two independent factors can explain 85% of the variance across their 24-h profiles. The first has peak expression time starting at 5am/6am, it correlates with measures of analytical thinking, with the language of drive (e.g power, and achievement), and personal concerns. It is anticorrelated with the language of negative affect and social concerns. The second factor has peak expression time starting at 3am/4am, it correlates with the language of existential concerns, and anticorrelates with expression of positive emotions. Overall, we see strong evidence that our language changes dramatically between night and day, reflecting changes in our concerns and underlying cognitive and emotional processes. These shifts occur at times associated with major changes in neural activity and hormonal levels.
Fabon obtained his PhD 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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