## 4 Proposed Model
### Users Weighting Process  
This contribution phase is divided into two main steps: SNA then ANN ranking and weighting processes. Data collected from the first phase about the newly added elements is mainly required to execute this phase. Follower/following lists and tweets' features are individually used as follows:  
1. Social Network Analysis (SNA) This step works with follower/following lists of opinion holders. Using SNA, users in a given social network can be provided some centrality measures representing their importance in this network. Owning higher centrality measures indicates more central, influential, and powerful users [4; 23]. The most considered centrality measures in this work are degree, betweenness, and closeness centrality. They measure the popularity of nodes through counting connections, how users control information diffusion in the network, and how quickly information spreads from users, respectively [23]. By applying some available SNA tools and using the collected follower/following lists, a graph of users can be constructed. In such a graph, a user is represented as a node, symbolized as \"\(u\)\", and his relations between other users are edges, symbolized as \"\(e\)\" per each, with directions, so-called directed graph \"\(G\)\". Moreover, centrality measures of nodes can be obtained. The directed graph and centrality measures are defined as [24]: \[G=(V,\,E)\] (1) where \(G\) is the social network graphically represented, as shown in Figure 3. \(V\) is the set of nodes representing users in the OSN: \(V=\{u_{1},\,u_{2},\,u_{3},\,\ldots,\,u_{n}\}\), and \(E\) is the set of edges representing directed relations between the graphically represented nodes in the OSN: \(E=\{e_{1},\,e_{2},\,e_{3},\,\ldots,\,e_{m}\}\). If two users (e.g., \(u_{1}\) and \(u_{2}\)) follow each other, then a directed edge between the two users is constructed: \(e_{u_{1}u_{2}}\in E\). \[C_{d^{-}}\left(u\right)=\left|\overset{\rightarrow}{N_{i}}\right|\] (2) where \(C_{d}{}^{-}(u)\) is the degree centrality (out-degree) of node \(u\) representing the number of outgoing edges from a node, and \(\overset{\rightarrow}{N_{i}}=\{\,j\,e\,V:(i,j)\,\,e\,E\}\) is a set of nodes that node \(i\) is connected to [12]. \[Bet(u)=\sum_{r\
eq u\
eq v\in V}\frac{|\{g_{rv}(u)\}|}{|\{g_{rv}\}|}\] (3)where \(\mathit{Bet}(u)\) is the betweenness centrality of node \(u\), \(|\{g_{rw}\}|\) denotes the number of shortest paths from node \(r\) to node \(w\), and \(|\{g_{rw}(u)\}|\) denotes the number of the shortest paths from node \(r\) to node \(w\) through node \(u\). \[Col(u)=\frac{1}{N+1}\sum_{w\in V,\ w\
eq u}\frac{1}{\mathit{S}u,\ w} \tag{4}\]  
where \(Col(u)\) is the closeness centrality of node \(u\), \(N\) is the number of nodes in \(V\), and \(g_{u,\ w}\) is the length of the shortest paths between the \(u\)th node and the rest of the network. A comparative analysis of three famous SNA software tools (i.e., Graphistry, Cytoscape, and the University of California at Irvine network (UCINET)) was conducted to choose the most suitable tool for this proposed work. According to our view and based on the evaluation process conducted in [4], Graphistry is the best relevant SNA software tool to adopt. Compared with the other tools, it was found to be the best in terms of visualization, scalability, and ease of use. Figure 3 shows the different representations of our directed graph using the three mentioned SNA software tools. As illustrated in Figure 3, we can easily identify bridges and popular users in Graphistry and Cytoscape while nothing can be identified using UCINET. Nodes in Graphistry are represented with large circle sizes with a large degree of centrality measures. The larger the degree of centrality of a node, the larger the circle size and vice versa. Moreover, Graphistry was easier for loading the data and building the directed graph. (2) Artificial Neural Network Ranking Process  
In this step, the collected data about tweets' reaction features (i.e., likes, counts, etc.)