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Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases

Dzogang, Fabon; Goulding, James; Lightman, Stafford; Cristianini, Nello

Authors

Fabon Dzogang

Stafford Lightman

Nello Cristianini



Abstract

The analysis of sentiment contained in vast amounts of Twitter messages has reliably shown seasonal patterns of variation in multiple studies, a finding that can have great importance in the understanding of seasonal affective disorders, particularly if related with known seasonal variations in certain hormones. An important question, however, is that of directly linking the signals coming from Twitter with other sources of evidence about average mood changes. Specifically we compare Twitter signals relative to anxiety, sadness, anger, and fatigue with purchase of items related to anxiety, stress and fatigue at a major UK Health and Beauty retailer. Results show that all of these signals are highly correlated and strongly seasonal, being under-expressed in the summer and over-expressed in the other seasons, with interesting differences and similarities across them. Anxiety signals, extracted from both Twitter and from Health product purchases, peak in spring and autumn, and correlate also with the purchase of stress remedies, while Twitter sadness has a peak in the Winter, along with Twitter anger and remedies for fatigue. Surprisingly, purchase of remedies for fatigue do not match the Twitter fatigue, suggesting that perhaps the names we give to these indicators are only approximate indications of what they actually measure. This study contributes both to the clarification of the mood signals contained in social media, and more generally to our understanding of seasonal cycles in collective mood.

Citation

Dzogang, F., Goulding, J., Lightman, S., & Cristianini, N. (2017). Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases. In Advances in Intelligent Data Analysis XVI (63-74). https://doi.org/10.1007/978-3-319-68765-0_6

Conference Name 16th International Symposium, IDA 2017
Conference Location London, UK
Start Date Oct 26, 2017
End Date Oct 28, 2017
Acceptance Date Oct 4, 2017
Online Publication Date Oct 4, 2017
Publication Date Oct 4, 2017
Deposit Date Nov 10, 2017
Publicly Available Date Mar 29, 2024
Publisher Springer Verlag
Volume 10584
Pages 63-74
Series Title Lecture Notes in Computer Science
Series ISSN 1611-3349
Book Title Advances in Intelligent Data Analysis XVI
ISBN 9783319687643
DOI https://doi.org/10.1007/978-3-319-68765-0_6
Keywords Social Media Mining, Emotions, Human Behaviour, Periodic Patterns, Computational Neuroscience
Public URL https://nottingham-repository.worktribe.com/output/886425
Publisher URL https://link.springer.com/chapter/10.1007%2F978-3-319-68765-0_6
Additional Information The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-68765-0_6

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