Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases

  • Fabon DzogangEmail author
  • James Goulding
  • Stafford Lightman
  • Nello CristianiniEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10584)


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.


Social media mining Emotions Human behaviour Periodic patterns Computational neuroscience 



NC and FD are supported by the ERC advanced Grant ThinkBig. JG is supported by the EPSRC Neodemographics grant, EP/L021080/1.


  1. 1.
    Bamman, D., Eisenstein, J., Schnoebelen, T.: Gender identity and lexical variation in social media. J. Sociolinguistics 18(2), 135–160 (2014)CrossRefGoogle Scholar
  2. 2.
    Dzogang, F., Lansdall-Welfare, T., Cristianini, N.: Seasonal fluctuations in collective mood revealed by wikipedia searches and twitter posts. In: 2016 IEEE International Conference on Data Mining Workshop (SENTIRE) (2016)Google Scholar
  3. 3.
    Gimpel, K., Schneider, N., O’Connor, B., Das, D., Mills, D., Eisenstein, J., Heilman, M., Yogatama, D., Flanigan, J., Smith, N.A.: Part-of-speech tagging for twitter: annotation, features, and experiments. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers, vol. 2, pp. 42–47 (2011)Google Scholar
  4. 4.
    Golder, S.A., Macy, M.W.: Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051), 1878–1881 (2011)CrossRefGoogle Scholar
  5. 5.
    Hadlow, N.C., Brown, S., Wardrop, R., Henley, D.: The effects of season, daylight saving and time of sunrise on serum cortisol in a large population. Chronobiol. Int. 31(2), 243–251 (2014)CrossRefGoogle Scholar
  6. 6.
    Lansdall-Welfare, T., Dzogang, F., Cristianini, N.: Change-point analysis of the public mood in UK twitter during the brexit referendum. In: 2016 IEEE International Conference on Data Mining in Politics Workshop (DMIP) (2016)Google Scholar
  7. 7.
    Leonard, W., Levy, S., Tarskaia, L., Klimova, T., Fedorova, V., Baltakhinova, M., Krivoshapkin, V., Snodgrass, J.: Seasonal variation in basal metabolic rates among the yakut (sakha) of northeastern siberia. Am. J. Hum. Biol. 26(4), 437–445 (2014)CrossRefGoogle Scholar
  8. 8.
    Melrose, S.: Seasonal affective disorder: an overview of assessment and treatment approaches. Depression Res. Treat. (2015)Google Scholar
  9. 9.
    Migaud, M., Butrille, L., Batailler, M.: Seasonal regulation of structural plasticity and neurogenesis in the adult mammalian brain: focus on the sheep hypothalamus. Front. Neuroendocrinol. 37, 146–157 (2015)CrossRefGoogle Scholar
  10. 10.
    Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of liwc2015. Technical report (2015)Google Scholar
  11. 11.
    Piantadosi, S.T.: Zipfs word frequency law in natural language: a critical review and future directions. Psychon. Bull. Rev. 21(5), 1112–1130 (2014)CrossRefGoogle Scholar
  12. 12.
    Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: liwc and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)CrossRefGoogle Scholar
  13. 13.
    Walton, J.C., Weil, Z.M., Nelson, R.J.: Influence of photoperiod on hormones, behavior, and immune function. Front. Neuroendocrinol. 32(3), 303–319 (2011)CrossRefGoogle Scholar
  14. 14.
    Watson, D., Clark, L.A.: The panas-x: manual for the positive and negative affect schedule-expanded form (1999)Google Scholar
  15. 15.
    Winthorst, W.H., Roest, A.M., Bos, E.H., Meesters, Y., Penninx, B.W., Nolen, W.A., Jonge, P.: Self-attributed seasonality of mood and behavior: a report from the netherlands study of depression and anxiety. Depress. Anxiety 31(6), 517–523 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Intelligent Systems LaboratoryUniversity of BristolBristolUK
  2. 2.N-LABUniversity of NottinghamNottinghamUK
  3. 3.Henry Wellcome Laboratories for Integrative Neuroscience and Endocrinology, School of Clinical SciencesUniversity of BristolBristolUK

Personalised recommendations