  Social media generate data on human behaviour at large scales and over long
periods of time, posing a complementary approach to traditional methods in the
social sciences. Millions of texts from social media can be processed with
computational methods to study emotions over time and across regions. However,
recent research has shown weak correlations between social media emotions and
affect questionnaires at the individual level and between static regional
aggregates of social media emotion and subjective well-being at the population
level, questioning the validity of social media data to study emotions. Yet, to
date, no research has tested the validity of social media emotion macroscopes
to track the temporal evolution of emotions at the level of a whole society.
Here we present a pre-registered prediction study that shows how
gender-rescaled time series of Twitter emotional expression at the national
level substantially correlate with aggregates of self-reported emotions in a
weekly representative survey in the United Kingdom. A follow-up exploratory
analysis shows a high prevalence of third-person references in
emotionally-charged tweets, indicating that social media data provide a way of
social sensing the emotions of others rather than just the emotional
experiences of users. These results show that, despite the issues that social
media have in terms of representativeness and algorithmic confounding, the
combination of advanced text analysis methods with user demographic information
in social media emotion macroscopes can provide measures that are informative
of the general population beyond social media users.
