Skip to main content
SpringerLink
Log in

Approximate Analysis of Deviant Behavior on Social Media

  • Conference paper
  • First Online:
Intelligent Computing

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 283))

Abstract

The paper presents an intelligent solution for social media analysis dedicated to identify deviant behavior online. The challenge of definition of the deviant behavior is formulated in terms of user online activity dynamic analysis using the models of non-even time series. To solve this problem it is proposed to develop a new approach based on approximation of the user behavior using semantic and statistical analysis and intelligent technologies of classification and clustering. The neural network was introduced to identify the emotional connotation of posts on social media. The proposed approach of approximate analysis was implemented in intelligent software for identification of deviant behavior.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Baesens, B.: Analytics in a Big Data world: The Essential Guide to Data Science and its Applications. Wiley, 232 p. (2014)

    Google Scholar 

  2. Al-khateeb, S., Agarwal, N.: Deviance in Social Media (2019). https://doi.org/10.1007/978-3-030-13690-1_1

  3. Pehlivan, B.M.: How social deviance is represented and controlled by mass media: a field study on children’s animated movies. EUL J. Soc. Sci. VII(II), 162–173 (2016)

    Google Scholar 

  4. Coletto, M., Aiello, L.M., Lucchese, C., Silvestri, F.: On the behaviour of deviant communities in online social networks. In: Proceedings of ICWSM 2016, pp. 72–81 (2016). arXiv:1610.08372

  5. Hussain, M., Obadimu, A., Bandeli, K.K., Nooman, M., Al-khateeb, S., Agarwal, N.: A framework for blog data collection: challenges and opportunities. In: The IARIA International Symposium on Designing, Validating, and Using Datasets (DATASETS 2017) (2017)

    Google Scholar 

  6. Kadushin, C.: Understanding Social Networks: Theories, Concepts, and Findings. Oxford University Press, Oxford, 264 p. (2012)

    Google Scholar 

  7. Wei, W., Joseph, K., Liu, H., Carley, K.M.: Exploring characteristics of suspended users and network stability on Twitter. Soc. Netw. Anal. Min. 6, 51 (2016)

    Article  Google Scholar 

  8. Shang, Y.: Resilient consensus for expressed and private opinions. IEEE Trans. Cybern. 51(1), 318–331 (2021)

    Article  Google Scholar 

  9. Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  10. Zhao, Z., Zheng, P., Xu, S., Wu, X.: Object detection with deep learning: a review. IEEE Trans. Neural Netw. Learn. Syst. 30(11), 3212–3232 (2019)

    Article  Google Scholar 

  11. Narynov, S., Mukhtarkhanuly, D., Omarov, B.: Dataset of depressive posts in Russian language collected from social media. Data Brief 29, 105–195 (2020)

    Article  Google Scholar 

  12. Ivaschenko, A., Khorina, A., Isayko, V., Krupin, D., Bolotsky, V., Sitnikov, P.: Modeling of user behavior for social media analysis. In: Proceedings of 2018 Moscow Workshop on Electronic and Networking Technologies (MWENT), 4 p. (2018)

    Google Scholar 

  13. Ivaschenko, A., Khorina, A., Sitnikov, P.: Online creativity modeling and analysis based on big data of social networks. In: Advances in Intelligent Systems and Computing, vol. 858, pp. 329–337. Springer, Cham (2019)

    Google Scholar 

  14. Prokhorov, S.: Applied Analysis of Random Processes. Samara Scientific Center of RAS, 582 p. (2007)

    Google Scholar 

  15. Ivaschenko, A., Sitnikov, P., Khorina, A.: Multi-Agent Approximation of Social Media User Behavior. Modern Informatization Problems in Economics and Safety. Science Book Publishing House, Yelm, pp. 35–39 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Samara State Technical University, Molodogvardeyskaya, 244, Samara, Russia

    Anton Ivaschenko & Arkadiy Krivosheev

  2. Samara National Research University, Moskovskoye shosse, 34, Samara, Russia

    Anastasia Stolbova

  3. ITMO University, Kronverksky Pr. 49, bldg. A, St. Petersburg, Russia

    Pavel Sitnikov

Authors
  1. Anton Ivaschenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arkadiy Krivosheev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anastasia Stolbova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pavel Sitnikov
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ivaschenko, A., Krivosheev, A., Stolbova, A., Sitnikov, P. (2022). Approximate Analysis of Deviant Behavior on Social Media. In: Arai, K. (eds) Intelligent Computing. Lecture Notes in Networks and Systems, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-030-80119-9_33

Download citation

Publish with us

Policies and ethics

Search

Navigation