TY - GEN

T1 - Methodology for Identifying Artificial Mobilization of Protest Activity in Social Networks

AU - Vitkova, Lidia

AU - GOLUZINA, Dariya

AU - Naumenko, K.

N1 - Publisher Copyright: © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2021

Y1 - 2021

N2 - The research is aimed at building a methodology detection of one of the new and extremely destructive social mechanisms for mobilizing political protest in the information and network society, which is the mechanism for mediating local incidents. Based on algorithms artificial intelligence it is planned to develop a methodology and software for diagnosing local incidents in social network. If this methodology is used, it will be possible to study a new and extremely dangerous phenomenon, which is a mass mobilization of protest caused not by endogenous, but exogenous factors, in some cases inspired by external stakeholders, more strictly and correctly. A distinctive feature of the proposed approach is the possibility of developing an automated system based on specially developed algorithms for monitoring potentially dangerous local incidents for socio-political stability. In the work is received information about the functions and potential of various machine learning models. Is made conclusion about which models are the most optimal for analyzing messages about protest activity. The methodology proposed by the authors is promising for further research and development, because it makes it possible to convert text values into numeric values and to add a sign of the presence of a call to messages.

AB - The research is aimed at building a methodology detection of one of the new and extremely destructive social mechanisms for mobilizing political protest in the information and network society, which is the mechanism for mediating local incidents. Based on algorithms artificial intelligence it is planned to develop a methodology and software for diagnosing local incidents in social network. If this methodology is used, it will be possible to study a new and extremely dangerous phenomenon, which is a mass mobilization of protest caused not by endogenous, but exogenous factors, in some cases inspired by external stakeholders, more strictly and correctly. A distinctive feature of the proposed approach is the possibility of developing an automated system based on specially developed algorithms for monitoring potentially dangerous local incidents for socio-political stability. In the work is received information about the functions and potential of various machine learning models. Is made conclusion about which models are the most optimal for analyzing messages about protest activity. The methodology proposed by the authors is promising for further research and development, because it makes it possible to convert text values into numeric values and to add a sign of the presence of a call to messages.

KW - INFORMATION SECURITY

KW - MACHINE LEARNING MODELS

KW - MEDIATIZATION

KW - NEURAL NETWORKS

KW - PROTEST ACTIVITY

KW - SOCIAL NETWORKS

KW - WORD PROCESSING

KW - Information security

KW - Machine learning models

KW - Mediatization

KW - Neural networks

KW - Protest activity

KW - Social networks

KW - Word processing

UR - https://link.springer.com/book/10.1007/978-3-030-71119-1

UR - http://www.scopus.com/inward/record.url?scp=85104830388&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-71119-1_46

DO - 10.1007/978-3-030-71119-1_46

M3 - Conference contribution

SN - 978-3-030-71118-4

T3 - Lecture Notes in Electrical Engineering

SP - 468

EP - 478

BT - Advances in Automation II - Proceedings of the International Russian Automation Conference, RusAutoConf 2020

A2 - Radionov, Andrey A.

A2 - Gasiyarov, Vadim R.

PB - Springer Nature

T2 - International Russian Automation Conference, RusAutoConf 2020

Y2 - 6 September 2020 through 12 September 2020

ER -