With an advent of social networks, spamming has posted the most important serious issues among the users. These are termed as influential users who spread the spam messages in the community which has created the social and psychological impact on the users. Hence the identification of such influential nodes has become the most important research challenge. The paper proposes with a method to (1) detect a community using community algorithms with the Laplacian Transition Matrix that is the popular hashtag (2) to find the Influential nodes or users in the Community using Intelligent centrality measure’s (3) The implementation of machine learning algorithm to classify the intensity of users.The extensive experimentations has been carried out using the COVID-19 datasets with the different machine learning algorithms. The methodologies SVM and PCA provide the accuracy of 98.6 than the linear regression for using the new centrality measures and the other scores like NMI, RMS, are found for the methods. As a result finding out the Influential nodes will help us find the Spammy and genuine accounts easily.

随着社交网络的出现，垃圾邮件已在用户中发布了最重要的严重问题。这些被称为有影响力的用户，他们在社区中传播垃圾邮件，对用户产生了社会和心理影响。因此，识别这些有影响力的节点已成为最重要的研究挑战。本文提出了一种方法：（1）使用社区算法和拉普拉斯转换矩阵（流行的标签）检测社区（2）使用智能中心性度量（3）机器的实现来找到社区中有影响力的节点或用户学习算法来对用户的强度进行分类。已经使用具有不同机器学习算法的 COVID-19 数据集进行了广泛的实验。SVM 和 PCA 方法为使用新的中心性度量提供了比线性回归 98.6 的准确性，并且发现了这些方法的其他分数，如 NMI、RMS。因此找出有影响力的节点将帮助我们轻松找到垃圾邮件和真实帐户。