Abstract
The purpose of this study is to develop a method for automatic detection of hotbeds of social tension on the Internet. The main problem with such detection is that currently there is no general methodology for detecting signs of social tension in the digital space, while more and more communication processes are moving to the network. Within the framework of this study, based on the analysis of sources, attributes of social tension were formed that are characteristic of expression in the digital space. Cognitive models and an ontology of attributes were developed, on the basis of which attributes of social tension, characteristic of the initial stages of the development of the situation, were formed. The formed complex of attributes is based on the methods of automatic detection of psychoemotional states of the individual. The developed method of automatic social tension detection on the Internet makes it possible to detect hotbeds of information dissemination that are suspicious from the point of view of intensifying social tension with the aim of further information countermeasures.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Melnik, E., Korovin, I., Klimenko, A.: A cognitive assistant functional model and architecture for the social media victim behavior prevention. In: Silhavy, R. (ed.) CSOC 2019. AISC, vol. 985, pp. 51–61. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19810-7_6
Thakur, P., Shrivastava, D.R.: A review on text based emotion recognition system. Int. J. Adv. Trends Comput. Sci. Eng. 7(5), 67–71 (2018). https://doi.org/10.30534/ijatcse/2018/01752018
Oueslati, O., Khalil, A.I.S., Ounelli, H.: Sentiment Analysis for Helpful Reviews Prediction. Int. J. 7(3), 34–40 (2018). https://doi.org/10.30534/ijatcse/2018/02732018
Nurul, S.J., Siti, J., Kamaruddin, S.S., Ahmad, F.K., Angeli, A.C.: Social tension detection on social media textual data: a literature review (2020)
Sachdeva, A., Kapoor, R., Sharma, A., Mishra, A.: An approach towards identification and prevention of riots by analysis of social media posts in real-time. In: Proceedings of the 8th International Conference Confluence 2018 on Cloud Computing, Data Science and Engineering, Confluence 2018, pp. 581–586 (2018). https://doi.org/10.1109/CONFLUENCE.2018.8442714
Danakin, N.S., Dyatchenko, L.Y.A., Speranskij, V.I.: Mechanisms of social regulation and conflict management technologies. Social'no-politicheskij zhurnal [Socio-political journal] (3), 151–162 (1996) (In Russ.)
Muhanova, M.N.: Values and behavioral strategies of students (Modernizaciya social'noj struktury rossijskogo obshchestva). Modernization Soc. Struct. Russ. Soc. 271–265 (2008)
Varshney, A.: Ethnic conflict and civil society: India and beyond. World Pol. 53, 362–398 (2001)
Horowitz, D.: Direct, displaced and cumulative ethnic aggression. Comp. Polit. 6, 1–16 (1973)
Horowitz, D.: Ethnic Groups in Conflict, Second Edition [1985]. University of California Press, Berkeley (2000)
Horowitz, D.: The Deadly Ethnic Riot. University of California Press, Berkeley (2001)
Jha, S.: Trade, institutions and religious tolerance: evidence from India (2005). mimeo, Stanford University Justino, Patricia (2008)
Lester, A.: Essays in the theory of economic growth. PhD Thesis, MIT, Chapter 1 (2005)
Montalvo, J., Reynal-Querol, M.: Ethnic polarization, potential conflict, and civil wars. Am. Econ. Rev. 95, 796–815 (2005)
Rohner, D.: Reputation, group structure and social tensions. J. Dev. Econ. 96(2), 188–199 (2010). https://doi.org/10.1016/j.jdeveco.2010.10.008
Baranova, G.V., Alekhin, E.I.: Formation of social tension in the regions of the Russian Federation. Methods of analysis and forecasting of social tension. Vestnik VGU. Seriya: Lingvistika i mezhkul'turnaya kommunikaciya [Bull. VSU. Series: Linguist. Intercultural Commun. (2–2), 149–154 (2007)
Rich, G.: Ekman Paul (2013) https://doi.org/10.1002/9781118339893.wbeccp187
Acknowledgments
This study is conducted with the financial support of RFBR grants № 20-04-60485, № 18-29-22093.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Iakov, K., Klimenko, A., Safronenkova, I. (2022). A Method of Automatic Social Tension Detection on the Internet Based on Cognitive Analysis and Individual’s Emotional State. In: Silhavy, R. (eds) Artificial Intelligence Trends in Systems. CSOC 2022. Lecture Notes in Networks and Systems, vol 502. Springer, Cham. https://doi.org/10.1007/978-3-031-09076-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-031-09076-9_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09075-2
Online ISBN: 978-3-031-09076-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)