电邮这篇文章 
Predicting the Results of the R. Cattell Test Based on the Social Network User Profiles
- 作者: Ryazantcev G.E.1, Oliseenko V.D.2, Abramov M.V.2, Tulupyeva T.V.2,3
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隶属关系:
- Saint Petersburg State University
- St. Petersburg Federal Research Center of the Russian Academy of Sciences
- North-West Institute of Management Branch of the Russian Presidential Academy of National Economy and Public Administration
- 期: 编号 1 (2024)
- 页面: 56-66
- 栏目: Information processing and data analysis
- URL: https://bakhtiniada.ru/2071-8632/article/view/287322
- DOI: https://doi.org/10.14357/20718632240106
- EDN: https://elibrary.ru/ESXDTN
- ID: 287322
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详细
Digital footprints of users in the social network and the results of passing the 16-factor R. Cattell test. The method consists in applying statistical methods and relevant machine learning algorithms to personal data on the user's page. The main results of the experiment are the identification of a significant correlation between the factors evaluated by the R. Cattell test and digital footprints, and the construction of predictive models. The best results among the machine learning methods for predicting the results of the R. Cattell test were shown by gradient boosting algorithms with the maximum value of the F1-micro metric of 0.606, which was achieved on the factor “emotional sensitivity” (factor I). The practical significance of the work lies in the development of a tool for automatically predicting the results of the R. Cattell test based on the user's digital footprints. The theoretical significance lies in the development of a method for the automated evaluation of the expression of personality traits of social network users on their digital footprints.
作者简介
Grigorii Ryazantcev
Saint Petersburg State University
编辑信件的主要联系方式.
Email: st088141@student.spbu.ru
Student
俄罗斯联邦, Saint PetersburgValerii Oliseenko
St. Petersburg Federal Research Center of the Russian Academy of Sciences
Email: vdo@dscs.pro
Junior researcher
俄罗斯联邦, Saint PetersburgMaxim Abramov
St. Petersburg Federal Research Center of the Russian Academy of Sciences
Email: mva@dscs.pro
PhD, Senior researcher
俄罗斯联邦, Saint PetersburgTatiana Tulupyeva
St. Petersburg Federal Research Center of the Russian Academy of Sciences; North-West Institute of Management Branch of the Russian Presidential Academy of National Economy and Public Administration
Email: tvt@dscs.pro
PhD, Аssistant professor; Senior researcher
俄罗斯联邦, Saint Petersburg; Saint Petersburg参考
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