## 5 Discussion on Retweet Prediction Based on Heterogeneous Features  
In this study, several aspects related to the retweet-prediction task are investigated. The two main objectives were to explore the potential of multilayer representation of the social network for the retweet prediction and to analyse the possibilities of retweet prediction based on heterogeneous data sources. Overall, the multilayer network features perform better than text features for all six trained models. According to that, we can confirm that multilayer network representation of Twitter has great potential for retweet prediction. These findings are in line with results of the study ([PERSON] et al., 2019) in which [PERSON] et al. have shown that multilayer network features perform better in the task of disinformation classification on Twitter. Although this study modeled Twitter differently than the \(\mathcal{MF}\) method proposed here and used different network measures, all these results indicate that multilayer approach in the task of retweet prediction is worthy of further examination. Furthermore, according to the results presented in the previous section, we can conclude that the combination of multilayer network features with text features in general performs better than only one set of features in the task of predicting the number of retweets. We have to emphasize that the combination of features only slightly outperforms the multilayer network features. However, this is consistent for five of six models (only the RF algorithm has better performance in the case of multilayer network features). The potential of combination of features from heterogeneous data sources has been considered in several studies before ([PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al., 2019), and it has been shown that the combination of features is better than one set of features. Specifically, in ([PERSON] et al., 2019) authors showed that models based on multidimensional features extracted from author, tweet, and user outperform models based on the standard set of features. Our approach also combines features extracted from tweet with data related to author and user, but in a different way. To the best of our knowledge, the combination of multilayer network measures and text embedding as features for information-spreading prediction has not been examined previously. The feature analysis performed by the SHAP approach indicates that among multilayer network measures, the in-degree calculated from the follower layer and the in-degree calculated from the tweet layer have highest impact on the model. Furthermore, the impact of the in-strength and out-strength centrality measures of the retweet layer are also high. Besides that, as expected, the number of followers and some other metadata, such as the presence of media and URLs, have a positive influence on retweeting. These findings suggest that except for the standard measures, multilayer network measures may be valuable in retweet-prediction models. Here we need to emphasize that feature engineering and the selection of appropriate feature sets is an important step in all classification tasks, although some studies have examined the potential of using deep neural networks to avoid the manual construction of features. For example, in [12] authors proposed attention-based deep neural networks in the task of retweet prediction, and in [71] the authors applied graph representation learning to extract the structural attributes of the ego network and predict user retweet behavior. However, it is still worth examining different possibilities in the construction sets of features, especially the combination of features from heterogeneous data sources. In this way, it is possible to detect which data sources have higher influence to information spreading, and in the next step we can include these sources as an input into a deep neural network.