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Telegram Channel Market in Russia: Participants and Communications on the Platform

Student: Kseniia Tikhomirova

Supervisor: Ilya Makarov

Faculty: Faculty of Communications, Media, and Design

Educational Programme: Data Journalism (Master)

Year of Graduation: 2020

This paper concerns the community detection problem among Telegram channels. The dataset is received from TGStat service and included the information of 58k forwards between 100 politician Telegram channels. Authors implement modern clusterization approaches to solve the issue of the lack of information about social links. The primary purpose is to compare using structural features with strategy-based ones in clustering analysis. With strategy-based features, authors mean indicators designed according to the nodes' role in a network. They are ten indicators, which are calculated for each network's member per each message—this way, each Telegram channel receives a vector. Authors measure distances between vectors and, using this data, build spectral clustering and Louvain modularity models.

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