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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

James Goulding

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.

Journal Article Type Article
Journal Lecture Notes in Computer Science
Electronic ISSN 0302-9743
Publisher Humana Press
Peer Reviewed Peer Reviewed
Volume 10584
APA6 Citation Dzogang, F., Goulding, J., Lightman, S., & Cristianini, N. (in press). Seasonal variation in collective mood via Twitter content and medical purchases. Lecture Notes in Artificial Intelligence, 10584, doi:10.1007/978-3-319-68765-0_6
DOI https://doi.org/10.1007/978-3-319-68765-0_6
Keywords Social Media Mining, Emotions, Human Behaviour, Periodic Patterns, Computational Neuroscience
Publisher URL https://link.springer.com/chapter/10.1007%2F978-3-319-68765-0_6
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
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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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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