Despite all efforts to mitigate mis- and disinformation, they continue to be a substantial problem. This project contributed to the literature base on mis- and disinformation about social media with an analysis of the interaction effects between temporal rhythms of disinformation and social media usage in the context of the COVID-19 pandemic.

Specifically, how mis- and disinformation spread on Twitter varied throughout the day. Furthermore, it turns out that there are individual differences in users' propensity to spread mis- and disinformation on Twitter, which are based on their activity patterns.

We analysed a comprehensive dataset, examining the reliability of information relating the COVID-19 pandemic shared on Twitter. We clustered users into pseudo-chronotypes based on their activity patterns on Twitter throughout the day, identified times of waking and prolonged waking states per cluster as well as times of increased susceptibility.