Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases

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

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, October). Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases. Presented at 16th International Symposium, IDA 2017, London, UK

• Presentation Conference Type: Edited Proceedings
• Conference Name: 16th International Symposium, IDA 2017
• 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: Nov 10, 2017
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Pages: 63-74
• Series Title: Lecture Notes in Computer Science
• Series Number: 10584
• 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