In this study, a social media analysis is conducted to examine the public discourse about the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic on Twitter. In particular, this study aims to examine (a) how the number of tweets varies as a function of the timeline of the pandemic and associated measures and (b) how the content of these tweets, including displayed emotions, changes. Therefore, 373,908 tweets and retweets from Belgium were collected from February 25, 2020 to the March 30. Time series analysis, network bigrams, topic models, and emotional lexica were deployed for analysis. The results showed that significant events related to the virus correlated with an immediate increase in the number of tweets addressing them. Furthermore, the Belgian Twitter discourse was characterized by positively connoted words, which also refer to European solidarity. These findings do not only stress the relevance of Twitter as a medium for public discourse during lockdowns, but also seem to indicate that the Belgian public supports policy measures that respect solidarity in Europe.

Figure 1 displays the response-based estimations for every predictor. The x-axis is mean-centered, indicating that zero represents the average probability to drink on a given day (30 %). The red line represents the mean prediction while the black lines indicate predictions for students' probability to drink with random intercepts.

Scholars have indicated that social media contribute to various health-related behaviors (e.g., substance use, body dissatisfaction) among adolescents. This study adds to the literature on health-related social media effects through theoretical advances supported by empirical evidence. First, we introduce the TAMT model, in which we assess the media environment along a continuum of two dimensions: the temporality (from ephemeral to persistent) and accessibility (from private to public) of message types. By combining these dimensions, we argue that there are four message types: ephemeral private, persistent private, ephemeral public, and persistent public. Second, we draw on the TAMT model to advance our knowledge of the role of social media in alcohol-related behaviors. We expected that, due to the distinctive characteristics of the four message types, they would be differently related to alcohol references and binge drinking. Based on cross-sectional data (N = 1,636, Mage = 15, SD = 1.17), we found that moderate alcohol references are encountered across all message types, while more extreme references are more likely to be prevalent in ephemeral public and ephemeral private messages. We show that exposure to moderate and extreme alcohol use references in ephemeral private and persistent private messages was associated with a higher probability of engaging in binge drinking, whereas exposure to ephemeral public and persistent public messages was not. Ephemeral private messages played the most crucial role in the association with binge drinking. These findings illustrate the importance of broadening the scope of research to ephemeral private environments when studying health-related behaviors. While we have illustrated the usefulness of the TAMT model against the background of two specific types of alcohol references, this new model can be extended to other behaviors (e.g., sexual risk-taking behaviors, cyberbullying).

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