DETECTION OF FAKE PROFILES IN SOCIAL NETWORKS WITH THE APPLICATION OF CLUSTERING METHODS - Problems of Information Technology

DETECTION OF FAKE PROFILES IN SOCIAL NETWORKS WITH THE APPLICATION OF CLUSTERING METHODS - Problems of Information Technology

DETECTION OF FAKE PROFILES IN SOCIAL NETWORKS WITH THE APPLICATION OF CLUSTERING METHODS - Problems of Information Technology

DETECTION OF FAKE PROFILES IN SOCIAL NETWORKS WITH THE APPLICATION OF CLUSTERING METHODS - Problems of Information Technology

DETECTION OF FAKE PROFILES IN SOCIAL NETWORKS WITH THE APPLICATION OF CLUSTERING METHODS - Problems of Information Technology
DETECTION OF FAKE PROFILES IN SOCIAL NETWORKS WITH THE APPLICATION OF CLUSTERING METHODS - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№1, 2021

DETECTION OF FAKE PROFILES IN SOCIAL NETWORKS WITH THE APPLICATION OF CLUSTERING METHODS

Khayala V. Ahmadova

Social network has millions of active users, as it offers a number of opportunities for social network users to make new friends, read the news, get useful information, and have fun. Having millions of active users of social networks creates conditions for the implementation of malicious purposes, such as manipulation of people, various types of challenges, discrediting people or organizations. In this case, fake profiles operating as groups such as troll profiles, sibyl accounts, sockpuppets, bot accounts, etc. are widely used. When classifying the algorithms used to detect fake profiles, the problems such as, data must have labels, the time spent classifying many profiles, and so on. arise. This article uses k-means, Gaussian Mixture, agglomerative clustering, spectral clustering algorithms to group fake profiles on social networks. Since clustering algorithms perform worse than classification methods in detecting fake profiles, this article discusses in which data the clustering methods used to detect fake profiles give better results. During the application of the algorithms, open access datasets containing profile-based data are used. Based on the results obtained during the performance evaluation of clustering methods using evaluation metrics such as, adjusted rand index, homogeneity, completeness, etc. the agglomerative clustering algorithm shows better results than other applied clustering algorithms (pp.83–94).

Keywords: Fake profile, clustering, k-means, agglomerative clustering, feature
DOI : 10.25045/jpit.v12.i1.07
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