## 4 Proposed Model
### Users Weighting Process
is combined with the centrality measures of their opinion holders obtained from the SNA process to help rank opinion holders based on their tweet's influence as well as their topological influence in the social network. Opinion holders obtain scores from 1 to 100. When centrality measures are high or/and their tweets obtain high reactions, then opinion holders obtain a high score and are highly ranked. ANN is used for this step; Figure 4 shows the steps of the ANN scoring process. ANN adoption was due to the complexity of this behavioral data to be mathematically represented. Figure 5 shows, in practice, the correlation between a sample data of action-based records (\(X\)-axis) and the resultant users'  
Figure 3: The constructed directed graph using different SNA tools. score (\(Y\)-axis) when plotted. It can be noticed that, for example, authors of low action-based tweets' counts can highly score and vice versa. Thus, there is no certain guidance rule for scoring users based on their tweets' action-based counts. Figure 4: The training and scoring processes of the constructed ANN. Figure 5: Correlation between users’ scores and their tweets reactions. A trial and error approach was followed and resulted in a constructed feed-forward ANN with 10 neurons in its hidden layer. In the training process, a sample of 100 to 10,000 records per input was applied to improve training performance and decrease scoring errors. Figure 6 illustrates the impact of applying 100 and 10,000 training samples on the ANN process error, where error seemed to decrease as training samples increased. Another observation is the dependency on the out-of-degree measure rather than other centrality measures. The output of the constructed ANN is a user score of 100; a user with a score of 100 is a highly ranked user, while one with a score of 1 is a lowly ranked one. These output scores are normalized to represent users' important weighting; high-scoring users are highly ranked with a higher importance weight than others in the same social network. Users' importance weighting results in classifying opinion holders into 4 main classes or types as follows: Micro, Macro, Mega, and A-Listers Influencers [25; 26; 27; 28]. Figure 7 summarizes how opinion holders are classified in this proposed work and their influence levels. Figure 6: (**a**) A training performance of 9.6461 with 100 Samples, (**b**) a training performance of 4.1030 with 10,000 Samples. Micro influencers: This category is of a low influence level that may include: personal users, secondary actors, retweeters, or silent followers. These users exhibit poor behavior in their social networks. Neither active nor sparing, they use their account when they like to and for their purposes (i.e., entertainment, news, and learning). Thus, Micro influencers can possess low reactions to their writings and low centrality measures as shown in Figure 7; Macro influencers: Their influence level lies between low and moderate influence. It includes actors with important content, builders, or even trolls. They work on building and growing their relationships and increasing their social network engagement through creating interesting content or even inflammatory conversation. Thus, they can possess high reactions to their writings as shown in Figure 7. They can become mega influencers with time based on their ability to gain audience trust; Mega Influencers: This category's influence level can lie between moderate and high influence levels. It may include business users, brokers, and newscasters. They are followed by a large sector for being the source of information. Thus, they possess high centrality measures as shown in Figure 3.0 They record an active presence on social media, providing service, marketing, advertising, etc. ; A-list influencers or potential influencers: They own the highest influence level. They are extremely popular and have between thousands and millions of followers. They include: celebrities, the most recognizable people on earth that tend to act, sing, play football, etc.