###### Abstract
Used in the context of a social network, a multilayer network can represent different actions within the social network such as follow, share, quote, mention, or reply as the separate network layer. Because each action has a different impact on information spreading, in this way it is possible to make a fine-grade differentiation between layers and to include all this information as predictors of retweeting. We have already shown that a multilayer network structure is fundamentally more expressive than individual layers in examples of modeling a multilayer language network [20] and multidimensional knowledge network [21]. In [22], the authors use multilayer network features for disinformation detection in US and Italian news spreading on Twitter. Inspired by these results, we decided to employ multilayer network features in the more general task of information-spreading prediction. However, in our approach, we construct a different multilayer network of Twitter and select different network measures to construct a multilayer network set of features. In addition, we combine multilayer network features with text features. To the best of our knowledge, this is the first time that anyone has attempted to use this set of multilayer network features in the task of retweet prediction and the first time anyone has attempted to combine multilayer network features with text features. We formalized our approach by introducing a multilayer framework for the representation of key elements of communications on social networks. The main objective of this study is to explore the potential of the multilayer network measures as the set of features in the task of retweet prediction. Additionally, we investigate whether the multilayer network features combined with text features perform better than just one set of features. Therefore, this study explores how message features extracted from heterogeneous data sources may affect tweet spreading in terms of retweeting. Multilayer network features are extracted from the multilayer model of the social network within which a message is spreading. For the purpose of retweeting prediction, we propose and construct a multilayer network representation with four layers representing actions of following, mentioning, replying, and a layer of tweets, and select several network measures from each layer. Text features are represented as a low-dimensional vector (embedding) that captures its semantics and structure. More specifically, we adopt a BERT-based language model, namely Cro-CoV-cseBERT [8] for representation of tweets as embeddings, which we use as a set of text features. We model the prediction problem as a binary classification task in which the first class contains tweets with just one retweet and the second class contains tweets with more than one retweet. Next, we explore the performance of different feature sets by conducting an extensive set of experiments in which we train six machine learning models in three different setups: (i) classification based on text features, (ii) classification based on multilayer network features, and (iii) classification based on text and multilayer network features. More precisely, we trained the following classifiers: random forest (RF), multilayer perceptron (MLP), light gradient boosting machine (LGBM), category-embedding model (CEM), neural oblivious decision ensembles (NODE), and attentive interpretable tabular learning (TabNet model). We evaluated the performance of trained classifiers on three different sets of features in terms of standard evaluation measures: accuracy, precision, recall, and F1 score on a large dataset of tweets. For this purpose, we prepared an empirical dataset of 199,431 tweets in the Croatian language posted during the pandemic period between 1 January 2020 and 31 May 2021. Our main research question is whether the use of multilayer network features in combination with features from heterogeneous data sources yields better results in terms of classification evaluation measures over just text features.