The COVID-19 pandemic has exposed the world's population to unprecedented health threats and changes to social life. High uncertainty about the novel disease and its social and economic consequences, together with increasingly stringent governmental measures against the spread of the virus, likely elicited strong emotional responses. We analyzed the digital traces of emotional expressions in tweets during 5 weeks after the start of outbreaks in 18 countries and six different languages. We observed an early strong upsurge of anxiety-related terms in all countries, which was related to the growth in cases and increases in the stringency of governmental measures. Anxiety expression gradually relaxed once stringent measures were in place, possibly indicating that people were reassured. Sadness terms rose and anger terms decreased with or after an increase in the stringency of measures and remained stable as long as measures were in place. Positive emotion words only decreased slightly and briefly in a few countries. Our results reveal some of the most enduring changes in emotional expression observed in long periods of social media data. Such sustained emotional expression could indicate that interactions between users led to the emergence of collective emotions. Words that frequently occurred in tweets suggest a shift in topics of conversation across all emotions, from political ones in 2019, to pandemic related issues during the outbreak, including everyday life changes, other people, and health. This kind of time-sensitive analyses of large-scale samples of emotional expression have the potential to inform risk communication. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
In response to the COVID-19 pandemic, governments have implemented a wide range of non-pharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset until the end of December 2020.