## 5 Results
### Statistics across the Clusters  
In the last part of the analysis, we analysed sentiment and retweeting across clusters aiming to answer the third research question (RQ3: What is the distribution of sentiment polarity (in each thematic cluster and over time) and the distribution of retweets (across thematic clusters and sentiment)?). Sentiment is calculated as the average sentiment in each cluster (i.e., negative sentiment is represented as 1, and the rest is represented as 0, as defined in Section 5.2). Figure 5 shows cluster statistics sorted by the presence of negativity. We report the total number of tweets in each cluster, the average number of retweets of a tweet for each cluster, the percentage of retweets for each cluster, and the negativity across clusters. According to these results, we determined that the three most negative clusters refer to tweets related to the \"Revolt against the COVID-19 policies and behaviour of citizens\" (cluster #3 with 0.89 negativity score), \"Public discussion regarding anti-pandemic policies and vaccines\", (cluster #4 with 0.804 negativity score) and \"Ironic comments of COVID-19\" (cluster #8 with 0.673 negativity score). If we take into account the topics, the high amount of negativity is not surprising for clusters #3 and #8. However, it is not the case with cluster #4 which is dedicated to the discussion of anti-pandemic policies and vaccines. In some previous studies of COVID-19 tweets in English related to the vaccination [33; 34], the attitudes were far more positive or neutral than negative. The most non-negative cluster of tweets is \"Information about new daily COVID-19 cases\" (cluster #7 with negativity score 0.055). The reason is that this cluster contains only tweets about new cases with no subjective messages and these tweets are always classified as neutral. The clusters related to the \"Informative facts\" (cluster #0 with negativity score 0.113), \"Education and implementation of the COVID-19 policies\" (cluster #1 with negativity score 0.122) and \"Coping with the pandemic\" (cluster #2 with negativity score 0.67) are also detected as highly non-negative. This may indicate that there is a degree of optimistic attitudes in tweets related to education, implementation of COVID-19 policies and coping with the pandemic in general. The graph visualised in Figure 6 shows how sentiment of each cluster changes over the observed period. From the graph, it can be seen that the sentiment is uniformly distributed for most of the clusters throughout all three waves. The most negative sentiment is consistently present in clusters #3, #4, while in cluster #8, the negative sentiment is not uniformly distributed. The fluctuations in cluster #8 may be caused by the small number of tweets, since it is the cluster with the smallest number of entries The clusters identified as non-negative (#7, #0 and #1) also show uniform distribution during the three pandemic waves. Sentiment in cluster #5 focused on the \"Impact of COVID-19 policies on economy and education\" varied over the time, similar as in the cluster #8. Sentiment in this cluster was less negative in the end of the first wave (in May and July of 2020) and, then, it has  
Figure 5: Number of tweets (count), average retweet count, retweet count percentage, and average sentiment (negativity) by cluster. Ordered by the average sentiment where the negative class is 1, and the neutral and positive are 0.  
suddenly increased to negative at the beginning of the second wave (in August of 2020). Later, at the end of the second wave, the negativity is lower again. It seems that the topics related to economy and education are the most prone to changes in accordance to the pandemic waves. The analysis of sharing these tweets in terms of retweeting shows that the percentage of retweeting is generally low. There is only one cluster with a high average number of retweets.