Abstract
The paper considers determination of destructive information impacts and personal tendencies of young generation that predispose them to uncritical comprehension of the content with destructive components. An application of traditional manual and semi-automatic methods seems ineffective because of the huge amount of information in the Internet space. The paper proposes an approach using the technologies of psychological examination and artificial intelligence. It incorporates the technique to determine the tendency of social networks’ users to acquire destructive information, the technique for classification of the social networks communities considering an existence of destructive impacts, and the technique for hypothetical detecting changes in the tendency of users to acquire information that may contain destructive components when interacting in social networks. The paper describes the experiments on highlighting the relation between the information that users provide in social networks and some of their psychological traits and states that may cause predisposition for non-critical acquisition and digestion of potentially destructive information.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ego-structure test developed by gĂĽnter ammon. https://www.psychol-ok.ru/statistics/ista/. Accessed 18 June 2019
Borisov, P.M.: Verbal characteristics of the concept of a destructive personality. Bull. Moscow Reg. State Univ. Ser. Ling. 2, 78–92 (2010)
Dos Santos, C., Gatti, M.: Deep convolutional neural networks for sentiment analysis of short texts. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 69–78 (2014)
Firat, A., Chatterjee, S., Yilmaz, M.: Genetic clustering of social networks using random walks. Comput. Stat. Data Anal. 51(12), 6285–6294 (2007)
Fromm, E.: The anatomy of human destructiveness, vol. 2337. Random House (1975)
Handcock, M.S., Raftery, A.E., Tantrum, J.M.: Model-based clustering for social networks. J. Roy. Stat. Soc. Ser. A (Statistics in Society) 170(2), 301–354 (2007)
Karnaushenko, L.V.: Destructive informational and psychological impact on a mass audience: legal aspects of counteraction. Vestnik Krasnodarskogo universiteta MVD Rossii 2(36), 157–161 (2017)
Kotenko, I., Chechulin, A., Komashinsky, D.: Categorisation of web pages for protection against inappropriate content in the internet. Int. J. Internet Protoc. Technol. 10(1), 61–71 (2017)
Lin, H., Jia, J., Guo, Q., Xue, Y., Li, Q., Huang, J., Cai, L., Feng, L.: User-level psychological stress detection from social media using deep neural network. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 507–516. ACM (2014)
Lipton, Z.C., Kale, D.C., Elkan, C., Wetzel, R.: Learning to diagnose with LSTM recurrent neural networks. arXiv preprint arXiv:1511.03677 (2015)
Machajdik, J.,  Hanbury, J.: Affective image classification using features inspired by psychology and art theory. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 83–92. ACM (2010)
Pizzuti, C.: Ga-net: a genetic algorithm for community detection in social networks. In: International Conference on Parallel Problem Solving from Nature, pp. 1081–1090. Springer (2008)
Segalin, C., Celli, F., Polonio, L., Kosinski, M., Stillwell, D., Sebe, N., Cristani, M., Lepri, B.: What your facebook profile picture reveals about your personality. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 460–468. ACM (2017)
Stepanov, A.V., Kim, L.M.: Destructive behavior and the problem of the culture of perception of information flows. Eurasian Union Sci. 7–6, 154–156 (2014)
Tulupyeva, T., Tulupyev, A., Abramov, M., Azarov, A., Bordovskaya, N.: Character reasoning of the social network users on the basis of the content contained on their personal pages. In: Biologically Inspired Cognitive Architectures (BICA) for Young Scientists, pp. 31–38. Springer (2016)
Voroshilova, M.B.: Cognitive arsenal and communication strategies of contemporary nationalist discourse. Polit. Linguist. 3(49), 242–245 (2014)
Yang, Z., Guo, J., Cai, K., Tang, J., Li, J., Zhang, L., Su, Z.: Understanding retweeting behaviors in social networks. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1633–1636. ACM (2010)
Zlokazov, K.V.: Destructiveness and personal identity. Science year-book of the Institute of Philosophy and Law of the Ural Branch of RAS, vol. 14(1) (2014)
Zlokazov, K.V.: Content analysis of destructive texts. Polit. Linguist. 1(51), 244–251 (2015)
Zlokazov, K.V.: Destructive behavior in various contexts of its manifestation. Vestnik Udmurtskogo universiteta. Series: Philosophy, Psychology, Pedagogy, 26(4) (2016)
Acknowledgements
The reported study was funded by RFBR, project number 18-29-22034 mk.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Branitskiy, A. et al. (2020). The Common Approach to Determination of the Destructive Information Impacts and Negative Personal Tendencies of Young Generation Using the Neural Network Methods for the Internet Content Processing. In: Kotenko, I., Badica, C., Desnitsky, V., El Baz, D., Ivanovic, M. (eds) Intelligent Distributed Computing XIII. IDC 2019. Studies in Computational Intelligence, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-32258-8_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-32258-8_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32257-1
Online ISBN: 978-3-030-32258-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)