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Diurnal variations in indicators of emotion have been reliably observed in Twitter content, but confirmation of their circadian nature has not been possible due to the many confounding factors present in the data. We report on correlations between those indicators in Twitter content obtained from 9 cities of Italy and 54 cities in the United Kingdom, sampled hourly at the time of the 2020 national lockdowns. This experimental setting aims at minimizing synchronization effects related to television, eating habits, or other cultural factors. This correlation supports a circadian origin for these diurnal variations, although it does not exclude the possibility that similar zeitgebers exist in both countries including during lockdowns. [ABSTRACT FROM AUTHOR]

Diurnal variation in psychometric indicators of emotion found in Twitter content has been known for many years. The degree to which this pattern depends upon different environmental zeitgebers has been difficult to determine. The nationwide lockdown in the United Kingdom in spring 2020 provided a unique government-mandated experiment to observe the temporal variation of psychometric indicators in the absence of certain specific social rhythms related to commuting and workplace social activities as well as many normal home-based social activities. We therefore analyzed the aggregated Twitter content of 54 UK cities in the 9 weeks of complete lockdown, comparing them with the 10 weeks that preceded them (as well as with the corresponding weeks of 2019). We observed that the key indicators of emotion retained their diurnal behavior. This suggests that even during lockdown there are still sufficient zeitgebers to maintain this diurnal variation in indicators of emotion. [ABSTRACT FROM AUTHOR]

The state of an individual's mental health depends on many factors. Determination of the importance of any particular factor within a population needs access to unbiased data. We used publicly available data-sets to investigate, at a population level, how surrogates of mental health covary with light exposure. We found strong seasonal patterns of antidepressant prescriptions, which show stronger correlations with day length than levels of solar energy. Levels of depression in a population can therefore be determined by proxy indicators such as web query logs. Furthermore, these proxies for depression correlate with day length rather than solar energy.Declaration of interestNone.