## 5 Results
### Analysis and Description of Clusters Related to COVID-19  
To answer the second research question (RQ2: What is the number of tweets present in the different thematic clusters related to COVID-19 and how do these trends change over the three pandemic waves?), we clustered the tweets into 10 clusters. Next, we explored the content of clusters by extracting tweets with representations closest to the center of the cluster in the learned representation space. We use the Euclidean distance for the quantification of the distance between representations. Tweets that are grouped together are semantically similar, describing a theme captured in a cluster, as listed in Table 3.  
Then, we explored the distribution of tweets across thematic clusters and how these trends were changing during the whole observed period. The results are shown in Figure 4.  
\begin{table}
\begin{tabular}{c l} \hline \hline \# & **Cluster Description** \\ \hline
0 & Informative facts about COVID-19 \\
1 & Education and implementation of the COVID-19 policies \\
2 & Coping with the pandemic \\
3 & Revolt against the COVID-19 policies and behaviour of citizens \\
4 & Public discussion regarding anti-pandemic policies and vaccines \\
5 & Impact of COVID-19 policies on economy and education \\
6 & Public comments on statements of the politicians and scientists \\
7 & Information about new daily COVID-19 cases \\
8 & Ironic comments of COVID-19 \\
9 & Short generic messages related to COVID-19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Clusters and their main themes.  
Figure 3: Sentiment (negativity) through time with a sliding window of 30 days. Positive direction on the y-axis represents negative sentiment.  
According to the results, the highest number of tweets belong to the cluster #4 related to the \"Public discussion regarding anti-pandemic policies and vaccines\" with 29.63% of tweets in total (cluster #4). It is followed by the clusters with themes related to \"Coping with the pandemic\" (cluster #2 with 18.37% of tweets in total) and \"Public comments on statements of the politicians and scientists\" (cluster #6 with 15.10% of tweets in total). The remaining 37% of tweets are distributed across the other 7 identified themes in such a way that there is no cluster with more than 7% of tweets.  
When we analyse the trends across the three waves, it seems that the number of tweets belonging to each cluster/theme is relatively constant during all three pandemic waves. The biggest change is present in cluster #2 (\"Coping with the pandemic\") which contains 26% of the tweets in the first pandemic wave, while in the next two waves, the percentage is smaller (16.12% in the second wave and 13.41% in the third wave). This can be explained by the fact that this theme was the most interesting in the early stage of the pandemic, while at a later point, the people had already learned how to cope with the pandemic. On the contrary, cluster #6 (\"Public comments on statements of the politicians and scientists\") has a higher number of tweets in the second and third wave than in the first wave. It seems that people needed to comment on politicians and scientists more intensely after the first wave.  
Figure 4: Percentage of tweets in the 10 clusters during 4 different time periods: chart **A** shows the overall tweet count percentages by cluster, chart **B** covers the first wave of the COVID-19 pandemic (1 January 2020–15 May 2020), chart **C** covers the second wave (16 May 2020–25 February 2021), and chart **D** covers the third wave (26 February 2021–31 May 2021).