## 5 Discussion on Retweet Prediction Based on Heterogeneous Features
In this context, the next research direction of the proposed approach is to perform joint representation learning from heterogeneous data sources: multilayer network and text. Another important aspect of this research is the comparison of the performance of six different ML algorithms in the task of retweet prediction. We identify the CEM model as the one with the best performance according to all used evaluation measures in all three feature set scenarios, whereas the overall lowest performance is achieved in the case of TabNet model. Again, it has to be emphasised that differences across all algorithms are not so significant. The only significant difference is in the performance of models that use only text features in comparison to a multilayer network set of features which seems to be significantly better for multilayer network features (as well as for combined features) for all six models. This is again an indicator that multiyear network features have great potential in the analysis of information spreading. This research is an extension of our previous studies of online communication on social media during the COVID-19 pandemic. In [72], we compared the retweeting of COVID-19-related tweets and tweets that are not related to COVID-19. Our findings indicate that nearly 60% of tweets related to COVID-19 belong to the high-spreadable class, whereas less than 40% of non-COVID-19 tweets belong to this high-spreadable class. This suggests that tweet content may have a high impact on retweeting (spreadability), especially during a global crisis, such as the COVID-19 pandemic. In another study [73], we explored the potential of graph neural networks (GNNs) in the task of prediction if the user would tweet about COVID-19 or not. By using the proposed multi-Cro-CoV-cseBERT model for retweet prediction, we will further analyse the information-spreading patterns in the domain of COVID-19-related communication on Twitter. This research has several limitations that we plan to address in future work. First, our results are not directly comparable to other studies, because we modelled the task of retweet prediction as the binary classification task into two classes: (i) class of tweets with only one retweet and (ii) class of tweets with more than one retweet. In this way, we try to predict whether the amount of retweets would be poor or not, but we did not take into account tweets that are not retweeted at all. We decided to discard all tweets with no retweets because there are too many reasons why the tweet is not retweeted and this may negatively affect the prediction. We assumed that the prediction models would perform better if we concentrated only on the dataset of retweeted tweets in this first step. In addition, we used this setup because this way we ensured balanced classes of the dataset. However, in future research we plan to include tweets with no retweet into the prediction task. Another limitation is that we used only one dataset of tweets to compare the performance of features and ML models. However, this dataset is a representative sample of tweets in the Croatian language posted during the pandemic years 2020 and 2021, and our intention was to analyse the crisis-related communication in Croatia during the COVID-19 pandemic period. That is the reason why we trained and compared ML models on this specific dataset of tweets.