Abstarct: Nowadays, identifying influential users in social networks has become one of the most popular topics. The popularity of this issue is related to various reasons such as cost reduction and targeted advertising. However, there are many challenges for researchers in this field. These challenges include the large number of users in social networks and their activities, how to define social influence, selecting appropriate metrics to measure the influence and limited access to information due to privacy of users in social networks. The purpose of this dissertation is to find influential people in Twitter by providing a suitable definition for the concept of influence and also selecting the most appropriate metrics to measure the influence. In the proposed method for calculating the influence of each user, first the influence of each tweet is calculated. The effect of each tweet in this way is equal to the number of people who can potentially view that tweet. This requires identifying the overt and covert audience of each tweet. We search for similar Tweets to check if the user was affected by someone else's tweets or not. To find similar tweets, the LSH algorithm is used to reduce the amount of calculations and time cost due to the high volume of data. According to the proposed method, the effect of each user is equal to the average effect of that user's tweets. But by comparison, to rank users baxsed on their influence if the average influence of their tweets is equal, the amount of user influence is more if the variance of the effect of their tweets is less. Also, if both the mean and variance are equal for both users, the user with more tweets will be more effective. Due to the proposed method and the required metrics, as well as the lack of a suitable databaxse and the impossibility of data collection, appropriate and relevant data were extracted and used from the sharing of two databaxses ArchiveTeam and Twitter Social Graph. Finally, according to the results, the proposed method is able to calculate the users' influence more accurately and realistically, using metrics that are more efficient than the previous ones