Formation and analysis of information cascades based on timed semantic influence

This study is devoted to the analysis of information flows and the search for hidden connections between Telegram news channels. The main goal is to develop a method to help identify sources with the most significant influence and model how information spreads on the network. The work proposes the Timed Semantic Influence (TSI) algorithm - it allows you to assess the connections between messages, taking into account not only the content but also the time of publication. Thanks to this method, the structure of news distribution is built in the form of tree-like cascades from the initial message to the channels that picked it up. As a result, the channel that most often triggers news waves was identified. Among the topics that caused the most excellent resonance after eliminating general air alerts, news about significant geopolitical events and international support for Ukraine dominated. The proposed approach opens up the opportunity to identify key players in the media space and determine the topics that most influence public opinion. Tabl.: 1. Bibliogr.: 11 items.