Abstract
Information from users’ profiles on social networking sites is an important data source for analysis of the users’ psychological characteristics. Texts, video and audio files, images, public pages can be easily accessible and analyzed. We consider the ways of estimating the users’ psychological characteristics on the base of his or her profile in the social network VKontakte. We compare different machine learning models for the analysis of user’s texts, such as linear regression, decision trees, random forest, support vector machine with linear, radial and sigmoidal kernels. Also we discussed the possible further stages of research including the sentiment analysis for better text description, the analysis of profile photo, and, finally, ways of combining all steps for estimating psychological characteristics of social networks users.
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
References
Baranyuk, V., Desyatkova, A., Smirnova, O.: Approaches to defining psycho-emotional characteristics of the information image of a social network user. Int. J. Open Inf. Technol. 4(8), 61–65 (2016)
Bondarchuk, D.: The use of latent semantic analysis in the tasks of classifying texts by emotional coloring. Bull. Res. Results 2(3), 146 (2012)
Bonilla, Y., Rosa, J.: # Ferguson: digital protest, hashtag ethnography, and the racial politics of social media in the united states. Am. Ethnol. 42(1), 4–17 (2015)
Boulianne, S.: Social media use and participation: a meta-analysis of current research. Inf. Commun. Soc. 18(5), 524–538 (2015)
Centola, D., van de Rijt, A.: Choosing your network: social preferences in an online health community. Soc. Sci. Med. 125, 19–31 (2015)
Dyatlova, E., Mikhina, M.: Visual images of Internet users: from photography to avatar. Bulletin of the Kostroma State University. Series: Pedagogy. Psychology. Sociokinetics 24(4), 84–86 (2018)
Krylov, A.A.: Psychology. Velbi (2008)
Krylova (Smirnova), O., Baranyuk, V., Ishin, I.: Analysis of audio and video collections of social network users to describe their information image. Int. J. Open Inf. Technol. 5(12), 30–38 (2017)
Kubarev, A., Kukushkina, O., Poddubny, V., Shevelyov, O.: Building a style sheet using text classification based on decision trees. Bull. Tomsk State Univ. Manag. Comput. Eng. Inf. 4(21) (2012)
Kulikova, V.: Text classification method based on neural networks. Innov. Dev. Modern Sci. Probl. Patterns Prospects 6, 37–40 (2017)
Lomotin, K., Kozlova, E., Romanov, A.: Machine learning methods application in the task of scientific text categorization on specialized texts base. Innov. Inf. Commun. Technol. 1, 410–414 (2017)
Nguyen, N., Tuzovsky, A.: Classification of texts based on the assessment of semantic proximity of terms. Georesour. Eng. 320(5). News of Tomsk Polytechnic University
Ovsyannikov, A., Gryzlov, I., Golubinsly, E., Smirnov, A., Vlasova, S.: Approach to automatic classification of short text messages based on modified bayes’s metod. Nauchnyye vedomosti Belgorodskogo gosudarstvennogo universiteta. Seriya: Ekonomika. Informatika 8(179), 30 (2014)
Obukhov, A., Churilova, E.: Modern approaches to the study of personality through narrative texts. XXI Century Teach. 4, 331–343 (2009)
Read, J.: Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the Student Research Workshop at the 2005 Annual Meeting of the Association for Computational Linguistics, Ann Arbor, Michigan, pp. 43–48 (2005)
Rubtsova, Y.: Building a body of texts to customize the tone classifier. Softw. Prod. Syst. 1(109), 72–78 (2015)
Park, G., et al.: Automatic personality assessment through social media language. J. Pers. Soc. Psychol. 108(6), 934 (2015)
Ryabikina, Z., Bogomolova, E.: Interrelation of personal characteristics of users of social networks of the Internet with features of their activity in the network. Polit. Netw. Electron. Sci. J. Kuban State Agrarian Univ. 109, 1041–1057 (2015)
Scherman, A., Arriagada, A., Valenzuela, S.: Student and environmental protests in chile: The role of social media. Politics 35(2), 151–171 (2015)
Segalovich, I., Titov, V.: Mystem [software]. https://tech.yandex.ru/mystem. Accessed 20 Mar 2019
Shchebetenko, S.: Settings on personality traits as a predictor of the activity of “Friends” of the user of the social network Vkontakte. Nat. Psychol. J. 4(24), 34–44 (2016)
Shevelev, O., Petrakov, A.: Classification of texts using decision trees and direct propagation neural networks. Bull. Tomsk State Univ. 290
Shipovskoy, V., Romanov, A.: Multi-class text classification using the support vector method. Electron. Tools Control Syst. 1–2, 56–57 (2017)
Skiba, S., Loiko, V.: Data mining of users’ social network profile for determination of typolgy of the consumer. part 1. Polythematic Netw. Electron. Sci. J. Kuban State Agrarian Univ. 107
Slave, E.: Social and psychological characteristics of users of the social network VKontakte. Sustainable development of Russia: challenges, risks, strategies. In: Materials of the XIX International Scientific and Practical Conference: to the 25th Anniversary of the Humanitarian University, Ekaterinburg, pp. 265–270 (2016)
Spyratos, S., Vespe, M., Natale, F., Weber, I., Zagheni, E., Rango, M.: Migration data using social media. JRC Science Hub
Tulupyeva, T., Suvorova, A., Azarov, A., Tulupyev, A., Bordovskaya, N.: Computer tools in the analysis of students’ digital footprints in social network: possibilities and primary results. Comput. Tools Educ. 5
Tulupyeva, T., Tafintceva, A., Tulupyev, A.: An approach to the analysis of personal traits reflection in digital traces. Vestnik psikhoterapii [The Bulletin of Psychotherapy] 60, 124–137 (2017)
Tulupyeva, T., Tulupyev, A., Yuschenko, N.: Social networks users value orientations expression in personal pages content (based on “vkontakte” network). Vestnik psikhoterapii [The Bulletin of Psychotherapy] 52, 37–50 (2014)
Ushkin, S.: Visual images of users of the VKontakte social network. Public Opinion Monit. Econ. Soc. Change 5(111), 159–169 (2012)
Voiskunsky, A.: Social perception in social networks. Bulletin of the Moscow University. Series 14: Psychology 2, 90–104 (2014)
Vorobev, N., Puchkov, E.: Classification of text using convolutional neural networks. Molodoy issledovatel Dona 6(9)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Budin, E., Smirnova, K., Suvorova, A., Tulupyeva, T. (2019). An Approach to Automation of User’s Profile Analysis. In: Alexandrov, D., Boukhanovsky, A., Chugunov, A., Kabanov, Y., Koltsova, O., Musabirov, I. (eds) Digital Transformation and Global Society. DTGS 2019. Communications in Computer and Information Science, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-37858-5_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-37858-5_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37857-8
Online ISBN: 978-3-030-37858-5
eBook Packages: Computer ScienceComputer Science (R0)