The paper studies the community detection problem on Telegram channels. The dataset is received from TGStat service and includes the information of 58k forwards between 100 politician Telegram channels. We implement modern clustering approaches to solve the problem of missing social links. Our study is based on a combination of structural features with strategy-based attributes, including indicators designed according to the nodes' role in a network. Authors provide ten novel indicators, which are calculated for each network's member per each message in order to vectorize a Telegram channel with regard to its strategy of information spread and the way of contacting other channels. Authors construct a metric-based graph of channel relations and cluster channels representations using network science techniques. Obtained results are studied using quantitative and qualitative analysis showing promising results in applying joint network-based and KPI-based models for the stated problem.