## 1 Introduction  
Social media play an important role in global crises, such as the COVID-19 pandemic. They serve as a key communication platform [1] and are potentially a source of valuable information [2]. During the last two decades, social media have amplified the spread of information, as well as misinformation and disinformation which may lead to an infodemic as a negative side effect [3]. Thus, social media monitoring (infoveillance) is needed for better understanding of crisis communication. In this light, natural language processing (NLP) offers a set of techniques and methods that contributes to the monitoring of crisis communication on social media. Namely, automatic keyword extraction, topic modelling, named entity recognition, text classification, sentiment analysis, fake news detection, etc., can be applied to different aspects of information monitoring. When dealing with a large amount of textual data, these techniques can perform more efficiently than humans [4]. For example, when dealing with data gathered from social networks, sentiment analysis based on machine learning (ML) provides insights into public attitudes. In the context of the pandemic, this may contribute to the implementation of appropriate public health responses [5]. Therefore, sentiment analysis (sometimes combined with topic modelling) is one of the most frequent NLP tasks implemented in COVID-19 information monitoring [6; 7; 8; 9; 10; 11; 12]. Focusing on the similar tasks, in this research we describe the implementation of an NLP based framework for the analysis of tweets written in the Croatian language, integrating the sentiment analysis, the topis analysis and the analysis of message spreading (retweeting). There are fewer studies focused on other languages, such as sentiment analysis of Polish [13] and Greek [14] tweets or topic modelling for tweets in the Italian language [15]. Studies limited to one language are important as a potential source of material for further comparative research of COVID-19-related communication in different languages. Thus, although the proposed framework covers only local aspects of online COVID-19 communication, limited to the Croatian language, it is a valuable contribution to the research in the context of monitoring the pandemic-related communication. The goal of this research was to develop a framework that includes language resources for the Croatian language and then to apply it to the analysis and characterisation of COVID-19-related tweets. The main purpose of developing the framework was to answer the following research questions:  
* [label=RQ1:]
* What sentiment was present in COVID-19-related tweets posted during the first three pandemic waves and how did the amount of negative sentiment change over time? * 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? * What is the distribution of sentiment polarity (in each thematic cluster and over time) and the distribution of retweets (across thematic clusters and sentiment)? As mentioned above, an essential prerequisite for answering these questions is to develop a language model and datasets for the domain of COVID-19 texts in the Croatian language. We decided to use and train a BERT variant of the language model. BERT (Bidirectional Encoder Representations from Transformers) [16] is a deep bidirectional Transformer that learns sentence representations. The BERT model was initially pre-trained on a large corpus of English texts, but the multilingual versions of BERT, mBERT [17], trained on 104 languages and XLM-R, trained on 100 languages [18] soon followed. Although mBERT and XLM-R achieve good results, it has been found that models trained on few languages (bilingual and trilingual models) or on one language (monolingual models) perform better than models trained on a large number of languages (multilingual models) [19; 20; 21].