ON THE APPLICABILITY OF CLUSTERING ALGORITHMS FOR COMBATING SPAM IN SOCIAL NETWORKS
The article is devoted to the possibility of processing spam and unwanted content in social networks and other services where users create their own content (UGC - User-generated content) by using the data clustering approach. Users are creating more and more content that needs to be processed by services, including through manual moderation. One of the options for optimizing moderation, including manual moderation, is clustering. Many scientific papers are devoted to the problem of data clustering. The paper presents a mathematical formulation of the data clustering problem. A General approach to applying data clustering is described. The existing clustering algorithms are reviewed: k-means, k-medians, expectation maximization, FOREL, c-means, iearic, minimal covering tree, and layered. Advantages and disadvantages are described, as well as input data and results of these algorithms. Possible types of objects in social networks that can be clustered to successfully combat unwanted content, such as text information, images, and accounts in the service, are considered. There are well - known cases of using the clustering approach of social networks and other UGC services to combat spam and unwanted content.