###### Abstract  
Retweet prediction is an important task in the context of various problems, such as information spreading analysis, automatic fake news detection, social media monitoring, etc. In this study, we explore retweet prediction based on heterogeneous data sources. In order to classify a tweet according to the number of retweets, we combine features extracted from the multilayer network and text. More specifically, we introduce a multilayer framework for the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships, as well as other key properties of communication on Twitter. Next, we select a set of local network measures from each layer and construct a set of multilayer network features. We also adopt a BERT-based language model, namely Cro-CoV-cseBERT, to capture the high-level semantics and structure of tweets as a set of text features. We then trained six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category-embedding model, neural oblivious decision ensembles, and an attentive interpretable tabular learning model for the retweet-prediction task. We compared the performance of all six algorithms in three different setups: with text features only, with multilayer network features only, and with both feature sets. We evaluated all the setups in terms of standard evaluation measures. For this task, we first prepared an empirical dataset of 199,431 tweets in Croatian posted between 1 January 2020 and 31 May 2021. Our results indicate that the prediction model performs better by integrating multilayer network features with text features than by using only one set of features. retweet prediction; multilayer network; natural language processing; text features; multilayer network; Twitter data +
Footnote †: journal: Medical  
course of global crises, particularly in the context of epidemics, climate change, migration crises, economic crises, or wars. For example, the outbreak of the COVID-19 disease caused a significant increase in social media usage among the public, and it seriously affected the public's understanding of the COVID-19 risk [7]. In some countries, there were many negative attitudes toward vaccines and anti-pandemic measures promoted on social networks [8]. Therefore, information-spreading analysis during a global crisis is of great importance as one of the steps of social media monitoring (infoveillance). Twitter is one of the largest social networks with around 330 million monthly active users [9]. Consequently, it is one of the most studied social networks. Recently, it has frequently been used for monitoring and tracking different aspects of healthcare information and public disease [8; 10; 11]. Among all the user behavior on social media, retweets are considered to be one of the primary ways of spreading information on Twitter [12; 13]. There is a large number of studies that deal with the prediction of information spreading on Twitter and other social networks. Many complex factors may influence the patterns of information spreading. Thus, different studies propose different sets of features for retweet prediction. Previous methods studied the problem by using various linguistic features, personal information of users, or network properties [12]. In some recent studies, authors combined heterogeneous data sources: information content, network structure, dynamics of spreading, information metadata, and other properties that can be referred to as heterogeneous data sources [1; 13; 14; 15; 16; 17]. However, there are properties that have not been fully explored in the task of retweet prediction. One less-studied approach is the use of multilayer network properties as features, especially in combination with other features from heterogeneous data sources. The multilayer network is a formalism that captures various sorts of relationships over network data [18; 19].