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Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases

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Advances in Intelligent Data Analysis XVI (IDA 2017)

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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.

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  1. 1.

    The data for purchases of over-the-counter Health products comes from a major UK health and beauty retailer, and has been made available to the University of Nottingham (JG), under an NDA agreement that restricts naming of the retailer. The raw data cannot be published, however examples of health and fatigue related products for which data has been made available are detailed in the “Appendix - Examples of health and fatigue related products”, but have been partly anonymized to respect this agreement.


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

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Correspondence to Fabon Dzogang or Nello Cristianini .

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Greeting messages. The signal about mood could be skewed by the presence of large amounts of standardised greeting messages in specific seasons, which make use of mood related words, while not expressing the mood of the writer. These standard greeting messages were removed from the data as follows: we ignored any Twitter post containing the word happy, merry, good, lovely, nice, great, or wonderful followed by either of christmas, halloween, valentine, easter, new year, mothers’ day, fathers’ day, and their variants (e.g. starting with a leading # or separated by a dash, a space or ending with ’s when applicable) was not considered for analysis.

Table 4. Most popular mood words extracted from Twitter based on the LIWC word lists. Our indicator of fatigue on the social platform is based on the PANAS word list, formed by just four words.

We verified that posts matching this pattern were indeed concentrated in very specific days (the expected ones for each holiday).

Examples of mood and fatigue keywords. Table 4 illustrates the most popular words on Twitter for each indicator of mood and for fatigue. The mood keywords are based on the LIWC word lists, those for fatigue are based on the PANAS word list.

Fig. 4.
figure 4

Top 10 most popular Health products presented for the searches anxiety, stress, and fatigue on the retailer’s online web interface.

Examples of health and fatigue related product. Fig. 4 gives an example of the most popular products for the searches anxiety, stress, and fatigue on the UK Health and Beauty retailer’s online web interface, as queried on May 2017.

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Dzogang, F., Goulding, J., Lightman, S., Cristianini, N. (2017). Seasonal Variation in Collective Mood via Twitter Content and Medical Purchases. In: Adams, N., Tucker, A., Weston, D. (eds) Advances in Intelligent Data Analysis XVI. IDA 2017. Lecture Notes in Computer Science(), vol 10584. Springer, Cham.

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