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Diurnal patterns in Twitter sentiment in Italy and United Kingdom are correlated
Sheng Wang1, Stafford Lightman2, Nello Cristianini3
1School of Computer Science, University of Bristol, Bristol, United Kingdom.
Abstract:
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.
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