## 2 Related Work
### Multilayer Network-Based Approach in Social Networks Representation  
As can be seen from the previous section, there are many possibilities in combining heterogeneous data sources for feature engineering in the task of retweet prediction, and almost all of them include some features extracted from the social network, involving author-centered properties, user-centered properties or a combination of both. However, there is still a dearth of research studies that represent a social network as a multilayer network which can capture different users' actions (such as reply, quote, mention, and follow) as separate layers. As we claim in the introduction section, network properties extracted from different layers provide more detailed insight into online communication, and based on this it is possible to better predict the action of retweeting. Thus, we expect that multilayer network measures combined with text features have great potential in the prediction of retweeting. One such study carried out by [PERSON] et al. [22] modeled Twitter as a multilayer network including four layers: mention, reply, retweet, and quote layer. They applied multilayer network representation of Twitter in the task of disinformation detection in US and Italian news spreading over Twitter. More precisely, they used multilayer network measures for classifying news articles pertaining to disinformation vs. mainstream news by solely inspecting their diffusion mechanisms on Twitter. They trained a logistic regression model to classify disinformation vs. mainstream networks on two large-scale datasets of diffusion cascades (tweets for United States and Italy). The proposed approach has a high accuracy (AUROC up to 94%) in the task of disinformation detection and suggests that a similar approach based on multilayer networks might be possible for the task of information-spreading prediction in general. There are some other studies that used multilayer networks to model different aspects of online social networks communication. Thus, [PERSON] et al. [37] modelled various kinds of interactions (specifically, retweeting, mentioning, and replying) as separate layers aiming to characterize interactions in online social networks during exceptional events that cause a large number of tweets (such as the discovery of the Higgs boson). They showed that a multilayer approach can reveal the presence of statistical regularities across different events, suggesting that there are some universal properties of online social networks during exceptional events. In [38], the authors proposed a method based on a multilayer approach-capable of identifying influencers on online social networks. The layers represent users, items, and keywords, along with the intralayer interactions among the actors of the same layer. [PERSON] and [PERSON] proposed a model for the representation of multilayer networks and applied this model to two online social networks [39]. Their results confirmed that considering a multilayer network model allows us to extract results that do not correspond completely to the ones that can be obtained from each network layer separately. In [40], the authors explored two online platforms, Twitter and Foursquare, analyzing the geosocial properties of links. They represented the two platforms as a composite multilayer online social network, wherein each platform represents a layer in the network. According to their results, by using the multilayer approach it is possible to successfully predict links across social networking services. It is also worth mentioning that in [41; 42; 43] the authors investigated the spreading patterns in multilayer networks. However, they did not apply machine learning algorithms, and their approaches were based on diffusion modeling. All of these studies of the multilayer network-based approach in modeling social networks are valuable, and they have proven the potential of multilayer networks when it comes to capturing important properties of online communication. In our research, we adopted some of these ideas.