Abstarct: Due to the galloping growth of social networks and their usage as a crucial communication means in the world, their analysis is of a high importance. Identification of the most influential users, the perople capable of changing other users’ ideas and behavior in a virtual social network, is one of the most practical analyses in such networks. Such analysis is employed broadly in various fields including business, marketing and politics. The thesis is aiming at introducing a new method to identify the most influential users in Twitter social network. A model is presented in this study to discover the influential Twitter users posting on Paris attacks in 2015. Such twitterers have been identified using the features regarding to tweets and the users posting them. The features are extracted employing Twitter Application Programming Interface (API). The proposed model divides the features into four groups: 1) the features in regards to users’ interactions; 2) the features pointing to users’ profiles; 3) the features shaped by the users reading the tweet; 4) the features included in the tweet. After preprocessing the collected data, the weights assigned to both user and tweet features are measured separately deploying AHP algorithm. As the other step toward identification of the most influential users, tweets are clustered and the semantic relationships between pairs of the tweets in each cluster are extracted. Then time factor, as one of the most effective features in changing the influence of the model, is employed. Next, the model is created and finally, it is applied to the databaxse. Results reveal the importance of tweets score compared to users score. The correlation between the rank of users and average score of tweets is measured as 0.619. The number uncovers the direct relationship between the quality of tweets and the rank of users. Results also show the quality of a single tweet message is more influential than the number of tweets. In other words, bringing an increase in the number of tweets does not guarantee for a growth in user’s influence. The trend of users’ influence in a 21-day period discloses that 75 percent of the people ranked among the most influential users, cannot manage to hold their position for another day. It also shows 59 percent of the users ranked among the first 3 influential people lose their position as fast as they achieve it. The hashtags used in the first 5 influetial tweets of each day reveal a 20-percent allocation for #prayforparis. The next position goes to #isis with 3 percent. The correlation between the features related to users and user scores show a strong relationship of 0.99 between the number of followers and user scores. The maximum correlation between tweet scores and related features is recorded 0.37 for the number of favourites. The comparison between the proposed model and the other approaches toward identification of influential users reveals the comprehensiveness of the method introduced in this study. It covers the lacks of previous methods.