Skip to main content
SpringerLink
Log in

Automating the Temperament Assessment of Online Social Network Users

  • Published:
Doklady Mathematics Aims and scope Submit manuscript

Abstract

Numerical data retrieved from the accounts of users of a popular Russian-language online social network have been used to automate the prediction of the PEN test (temperament test) results. This study aims to automate the assessment of personality traits of online social network users by comparing the test results and the content posted by the user on his or her account, using machine learning methods. Classifiers are constructed with CatBoost and random forest models for predicting the scores of extraversion–introversion and neuroticism. The theoretical significance of this result is the development of an approach to automating the assessment of human personality traits. The practical significance is the development of a program module to create an automated system for assessing the human personality traits through online social networks.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.

Similar content being viewed by others

Notes

  1. Psychological tests. https://vk.com/services?w=app7794698_ 8126979.

  2. Included in the list of public associations and religious organizations that are liquidated or prohibited by the court decision entered into legal force on the grounds provided for by Federal Law No. 114-FZ “On Combating Extremist Activities” of July 25, 2002.

  3. Blocked in Russia by the Prosecutor General’s Office demand dated February 24, 2022.

  4. Dynamics of the audience of social networks and instant messengers. https://blog.skillfactory.ru/auditoriya-soczialnyh-setej-i- messendzherov-v-2022-godu/.

REFERENCES

  1. F. Shen, S. S. Ren, X. Y. Zhang, H. W. Luo, and C. M. Feng, “A digital twin-based approach for optimization and prediction of oil and gas production,” Math. Probl. Eng. 2021, 3062841 (2021). https://doi.org/10.1155/2021/3062841

  2. A. Barni, D. Pietraroia, S. Züst, S. West, and O. Stoll, “Digital twin based optimization of a manufacturing execution system to handle high degrees of customer specifications,” J. Manuf. Mater. Process. 4 (4), 109 (2020). https://doi.org/10.3390/jmmp4040109

    Article  Google Scholar 

  3. H. Zhang, Q. Liu, X. Chen, D. Zhang, and J. Leng, “A digital twin-based approach for designing and multi-objective optimization of hollow glass production line,” IEEE Access, No. 5, 26901–26911 (2017). https://doi.org/10.1109/ACCESS.2017.2766453

  4. J. A. Marmolejo-Saucedo, “Digital twin framework for large-scale optimization problems in supply chains: A case of packing problem,” Mobile Networks Appl. 27, 2198–2214 (2022). https://doi.org/10.1007/s11036-021-01856-9

    Article  Google Scholar 

  5. F. Tao, Q. Qi, L. Wang, and A. Y. C. Nee, “Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0,” Correl. Comp. Eng. 5 (4), 653–661 (2019). https://doi.org/10.1016/j.eng.2019.01.014

    Article  Google Scholar 

  6. R. Sahal, S. H. Alsamhi, and K. N. Brown, “Personal digital twin: A close look into the present and a step towards the future of personalised healthcare industry,” Sensors 22, 5918 (2022). https://doi.org/10.3390/s22155918

    Article  Google Scholar 

  7. O. Alhendi, “Personality traits and their validity in predicting job performance at recruitment: A review,” Int. J. Eng. Manage. Sci. 4 (3), 222–231 (2019). https://doi.org/10.21791/IJEMS.2019.3.21

    Article  Google Scholar 

  8. A. O. Khlobystova, M. V. Abramov, and V. F. Stolyarova, “Exploring the possibility of predicting users’ career guidance preferences based on analysis of community topics and the gender in the online social network users’ profiles,” Nauchno-Tekh. Vestn. Inf. Tekhnol. Mekh. Opt. 23 (3), 564–574 (2023). https://doi.org/10.17586/2226-1494-2023-23-3-564-574

    Article  Google Scholar 

  9. P. L. Curşeu, R. Ilies, D. Vîrgă, L. Maricuţoiu, and F. A. Sava, “Personality characteristics that are valued in teams: Not always 'more is better'?,” Int. J. Psychol. 54 (5), 638–649 (2019). https://doi.org/10.1002/ijop.12511

    Article  Google Scholar 

  10. D. De Clercq, I. U. Haq, and M. U. Azeem, “Time-related work stress and counterproductive work behavior: Invigorating roles of deviant personality traits,” Pers. Rev. 48 (7), 1756–1781 (2019). https://doi.org/10.1108/PR-07-2018-0241

    Article  Google Scholar 

  11. C. Huang, “Social network site use and Big Five personality traits: A meta-analysis,” Comput. Hum. Behav. 97, 280–290 (2019). https://doi.org/10.1016/j.chb.2019.03.009

    Article  Google Scholar 

  12. R. B. Cattell and B. D. Gibbons, “Personality factor structure of the combined Guilford and Cattell personality questionnaires,” J. Pers. Soc. Psychol. 9 (1), 107–120 (1968). https://doi.org/10.1037/h0025724

    Article  Google Scholar 

  13. A. Furnham, S. C. Richards, and D. L. Paulhus, “The dark triad of personality: A 10 year review,” Soc. Pers. Psychol. Compass 7 (3), 199–216 (2013). https://doi.org/10.1111/spc3.12018

    Article  Google Scholar 

  14. G. Saucier and L. R. Goldberg, “What is beyond the big five?,” J. Pers. 66, 495–524 (1998).

    Article  Google Scholar 

  15. H. J. Eysenck and S. B. G. Eysenck, “Eysenck personality questionnaire-revised” (1984). https://doi.org/10.1037/t05461-000

  16. P. Kline, Handbook of Psychological Testing (Routledge, London, 2013). https://doi.org/10.4324/9781315812274

    Book  Google Scholar 

  17. Y. Mehta, N. Majumder, A. Gelbukh, and E. Cambria, “Recent trends in deep learning based personality detection,” Art. Intell. Rev. 53, 2313–2339 (2020). https://doi.org/10.1007/s10462-019-09770-z

    Article  Google Scholar 

  18. G. V. Sukhodol’skii, Mathematical Psychology (Gumanitarnyi Tsentr, Kharkov, 2006) [in Russian].

    Google Scholar 

  19. L. Li, A. Li, B. Hao, Z. Guan, and T. Zhu, “Predicting active users' personality based on micro-blogging behaviors,” PLoS ONE 9 (1), e84997 (2014). https://doi.org/10.1371/journal.pone.0084997

  20. M. Evin, A. Hidalgo-Munoz, A. J. Bequet, F. Moreau, H. Tattegrain, C. Berthelon, A. Fort, and C. Jallais, “Personality trait prediction by machine learning using physiological data and driving behavior,” Mach. Learn. Appl. 9, 100353 (2022). https://doi.org/10.1016/j.mlwa.2022.100353

  21. J. Zhao, D. Zeng, Y. Xiao, L. Che, and M. Wang, “User personality prediction based on topic preference and sentiment analysis using LSTM model,” Pattern Recogn. Lett. 138, 397–402 (2020). https://doi.org/10.1016/j.patrec.2020.07.035

    Article  Google Scholar 

  22. Y. Kim and J. H. Kim, “Using computer vision techniques on Instagram to link users’ personalities and genders to the features of their photos: An exploratory study,” Inf. Proc. Manage. 54 (6), 1101–1114 (2018). https://doi.org/10.1016/j.ipm.2018.07.005

    Article  Google Scholar 

  23. B. Omar and W. Dequan, “Watch, share or create: The influence of personality traits and user motivation on TikTok mobile video usage,” Int. J. Interactive Mobile Technol. 14 (4), 121–137 (2020). https://doi.org/10.3991/ijim.v14i04.12429

    Article  Google Scholar 

  24. M. Kosinski, D. Stillwell, and T. Graepel, “Private traits and attributes are predictable from digital records of human behavior,” Proc. Nat. Acad. Sci. 110 (15), 5802–5806 (2013). https://doi.org/10.1073/pnas.1218772110

    Article  Google Scholar 

  25. C. Stachl, F. Pargent, S. Hilbert, G. M. Harari, R. Schoedel, S. Vaid, S. D. Gosling, and M. Bühner, “Personality research and assessment in the era of machine learning,” Eur. J. Pers. 34 (5), 613–631 (2020). https://doi.org/10.1002/per.2257

    Article  Google Scholar 

  26. Y. Mehta, N. Majumder, A. Gelbukh, and E. Cambria, “Recent trends in deep learning based personality detection,” Art. Intell. Rev. 53, 2313–2339 (2019). https://doi.org/10.1007/s10462-019-09770-z

    Article  Google Scholar 

  27. M. Kosinski, S. C. Matz, S. D. Gosling, V. Popov, and D. Stillwell, “Facebook as a research tool for the social sciences: Opportunities, challenges, ethical considerations, and practical guidelines,” Am. Psychol. 70 (6), 543 (2015). https://doi.org/10.1037/a0039210

    Article  Google Scholar 

  28. B. Plank and D. Hovy, “Personality traits on twitter or how to get 1,500 personality tests in a week,” Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (2015), pp. 92–98. https://doi.org/10.18653/v1/W15-2913

  29. J. W. Pennebaker and L. A. King, “Linguistic styles: Language use as an individual difference,” J. Pers. Soc. Psychol. 77 (6), 1296 (1999). https://doi.org/10.1037//0022-3514.77.6.1296

    Article  Google Scholar 

  30. M. Gjurković, M. Karan, I. Vukojević, M. Bošnjak, and J. Šnajder, “PANDORA talks: Personality and demographics on Reddit,” Proceedings of the 9th International ACL Workshop on Natural Language Processing for Social Media (ACL, Pennsylvania, 2021), pp. 138–152.

  31. M. A. Kosan, H. Karacan, and B. A. Urgen, “Predicting personality traits with semantic structures and LSTM-based neural networks,” Alexandria Eng. J. 61 (10), 8007–8025 (2022). https://doi.org/10.1016/j.aej.2022.01.050

    Article  Google Scholar 

  32. S. Başaran and O. H. Ejimogu, “A neural network approach for predicting personality from Facebook data,” Sage Open 11 (3) (2021). https://doi.org/10.1177/21582440211032156

  33. H. Gu, J. Wang, Z. Wang, B. Zhuang, and F. Su, “Modeling of user portrait through social media,” in 2018 IEEE International Conference on Multimedia and Expo (ICME) (2018). https://doi.org/10.1109/icme.2018.8486595

  34. S. Titov, P. Novikov, and L. Mararitsa, “Full-scale personality prediction on VKontakte social network and its applications,” in 25th Conference of Open Innovations Association (FRUCT) (2019). https://doi.org/10.23919/FRUCT48121.2019.8981513

  35. M. A. Stankevich, N. A. Ignat’ev, I. V. Smirnov, and N. V. Kiselnikova, “Personality traits prediction from VKontakte social media,” Vopr. Kiberbezopasnosti 4 (32), 80–87 (2019). https://doi.org/10.21681/2311-3456-2019-4-80-87

    Article  Google Scholar 

Download references

Funding

This work was supported by the ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

Author information

Authors and Affiliations

  1. Laboratory for Theoretical and Interdisciplinary Computer Science, St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    V. D. Oliseenko, A. O. Khlobystova, A. A. Korepanova & T. V. Tulupyeva

  2. Chair of State and Municipal Administration, Northwestern Institute of Management, Russian Presidential Academy of National Economy and Public Administration, St. Petersburg, Russia

    T. V. Tulupyeva

Authors
  1. V. D. Oliseenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. O. Khlobystova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. A. Korepanova
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. T. V. Tulupyeva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to V. D. Oliseenko, A. O. Khlobystova, A. A. Korepanova or T. V. Tulupyeva.

Ethics declarations

The authors of this work declare that they have no conflicts of interest.

Additional information

Publisher’s Note.

Pleiades Publishing remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Oliseenko, V.D., Khlobystova, A.O., Korepanova, A.A. et al. Automating the Temperament Assessment of Online Social Network Users. Dokl. Math. 108 (Suppl 2), S368–S373 (2023). https://doi.org/10.1134/S1064562423701041

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1064562423701041

Keywords:

Search

Navigation