ABSTRACT
In this paper, we present the results of an initial experiment using emotion classifications as the basis for studying information dynamics in social media ('emodynamics'). To do this, we used Bert Emotion [18] to assign probability scores for eight different emotions to each text in a time series of 43 million Danish tweets from 2019-2022. We find that variance in the information signals novelty and resonance reliably identify seasonal shifts in posting behavior, particularly around the Christmas holiday season, whereas variance in the distribution of emotion scores corresponds to more local events such as major inflection points in the Covid-19 pandemic in Denmark. This work in progress suggests that emotion scores are a useful tool for diagnosing shifts in the baseline information state of social media platforms such as Twitter, and for understanding how social media systems respond to both predictable and unexpected external events. © 2022 Copyright for this paper by its authors.