Seasonal variation in collective mood via Twitter content and medical purchases
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 cert...
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| Format: | Article |
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Springer
2017
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| Online Access: | https://eprints.nottingham.ac.uk/48034/ |
| _version_ | 1848797676380880896 |
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| author | Dzogang, Fabon Goulding, James Lightman, Stafford Cristianini, Nello |
| author_facet | Dzogang, Fabon Goulding, James Lightman, Stafford Cristianini, Nello |
| author_sort | Dzogang, Fabon |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:07:40Z |
| format | Article |
| id | nottingham-48034 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:07:40Z |
| publishDate | 2017 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-480342020-05-04T19:10:51Z https://eprints.nottingham.ac.uk/48034/ Seasonal variation in collective mood via Twitter content and medical purchases Dzogang, Fabon Goulding, James Lightman, Stafford Cristianini, Nello 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. Springer 2017-10-04 Article PeerReviewed Dzogang, Fabon, Goulding, James, Lightman, Stafford and Cristianini, Nello (2017) Seasonal variation in collective mood via Twitter content and medical purchases. Lecture Notes in Computer Science, 10584 . pp. 63-74. ISSN 0302-9743 Social Media Mining Emotions Human Behaviour Periodic Patterns Computational Neuroscience https://link.springer.com/chapter/10.1007%2F978-3-319-68765-0_6 doi:10.1007/978-3-319-68765-0_6 doi:10.1007/978-3-319-68765-0_6 |
| spellingShingle | Social Media Mining Emotions Human Behaviour Periodic Patterns Computational Neuroscience Dzogang, Fabon Goulding, James Lightman, Stafford Cristianini, Nello Seasonal variation in collective mood via Twitter content and medical purchases |
| title | Seasonal variation in collective mood via Twitter content and medical purchases |
| title_full | Seasonal variation in collective mood via Twitter content and medical purchases |
| title_fullStr | Seasonal variation in collective mood via Twitter content and medical purchases |
| title_full_unstemmed | Seasonal variation in collective mood via Twitter content and medical purchases |
| title_short | Seasonal variation in collective mood via Twitter content and medical purchases |
| title_sort | seasonal variation in collective mood via twitter content and medical purchases |
| topic | Social Media Mining Emotions Human Behaviour Periodic Patterns Computational Neuroscience |
| url | https://eprints.nottingham.ac.uk/48034/ https://eprints.nottingham.ac.uk/48034/ https://eprints.nottingham.ac.uk/48034/ |