## 2 Related Work  
Research that is based on analyzing social media data are gaining massive attention [4; 6]. In the literature, this research type is performed to serve many applications (e.g., market, health care, etc. ), and they are mostly classified into structural-based and content-based analyses [4]. The structural-based analysis deals with the main social network structure, studies nodes (i.e., users) interactions, and the network topology. The content-based analysis deals with content being created by nodes and shared on social media [4]. Figure 1 shows the classification of social media data-based research. In marketing applications, business owners and researchers are concerned about the structural-based analyses that include SNA to find influencers [10]. In line with that, in 2017, most research found OSNs' influencers based on graph theory by measuring network topology (e.g., degree of centrality, betweenness, etc.) [4; 11]. [PERSON] et al. [12] proposed measuring influence based on considering not only the structural-based analysis of social media data (i.e., graph theory), but also the influence of users' tweets by considering retweets, replies, and favorite counts. They believed that the content of tweets itself can influence people as much as their authors. They tested their proposed model, and it was found to be a better method than the compared methods. In 2020, concerning the community detection under the same analysis type, [PERSON] et al. [7] highlighted the problem of detecting opinion leaders on dynamic social graphs. They attempted to consider the dynamic nature of online social networks (OSNs) while detecting opinion leaders on social media. They proposed a new model that collected Facebook posts and then filtered them based on the included opinion words and the text type (i.e., status). They considered posts important when having higher opinion words and social reactions. Accordingly, influuencers were the ones whose posts had the highest combined score. Experiments on real data collected from Facebook were performed. The validation of the model was compared with some previous work, and the results showed good performance of this model when considering the dynamic nature of the network. It was found that the isolated application of one analysis, whether it was structural or content-based, can cause information incompleteness, essential patterns, and knowledge loss [4]. This caused a few researchers to combine both analyses for different application purposes. In 2021, [PERSON] et al. [13] attempted to combine both the content-based analysis (i.e., SA) with the structural-based analysis (i.e., SNA) by designing a new sentiment link analysis using a graph network to predict users' attitudes towards an entity. They considered the problem of determining users' attitudes towards entities without the normal analysis of text sentiment. They tried to solve the problem of the undecided polarity of tweets posted by their authors on social media due to the short text or unclear wording to retrieve users' real attitudes. They also tried to predict the hidden attitude when users did not share their opinions on social media. They modeled the information network to retrieve unknown sentiment links based on users' social relationships, user attributes, and movie attributes. Experiments were conducted using movies reviewed from Weibo gaming proving the effectiveness of this method on some state-of-art methods. In the same year, [PERSON] et al. [14] highlighted the importance of predicting election results based on three approaches by surveying 38 papers on election prediction. The three approaches were: the volumetric approach (counts of posts, likes, etc. ); the content-based analysis in the form of the SA approach (polarity of posts); and the structural-based analysis in the form of SNA (measure centrality of supporters). They found that most research depended on SA, either alone or combined with a volumetric approach. Only one paper combined the three approaches.