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Algorithms to Identify and Analyze a Network of Information Impact Catalysts on Twitter

Student: Popov Vladimir

Supervisor: Alexander Chepovskiy

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2020

This work is dedicated to the development of algorithms for constructing weighted social graphs based on the import of data from the Twitter network. Corresponding data structures have been developed and software that allows to build a weighted graph based on predefined tweets has been implemented. The configurations specified by the operator to account the main actions of Twitter users determine the edges and their weight in the graph. The application of these algorithms is shown on the example of actual posts related to the actions of the authorities of the city of Moscow in May as part of the fight against the new coronavirus infection Covid-19. By identifying the leaders of the opinions of user groups in the graph, an example of analysis of the catalysts for the corresponding information impact is shown. Keywords: social network, Twitter, data import, social graph. This graduate work consists of 3 chapters, 6 sections, 12 drawings and 4 tables.

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