=Paper=
{{Paper
|id=Vol-2784/rpaper18
|storemode=property
|title=The Description of the Structure of Social Identity in the Information Space, Using Automated Data Processing Tools
|pdfUrl=https://ceur-ws.org/Vol-2784/rpaper18.pdf
|volume=Vol-2784
|authors=Boris Nizomutdinov,Anna Uglova,Aleksandr Tropnikov
|dblpUrl=https://dblp.org/rec/conf/ssi/NizomutdinovUT20
}}
==The Description of the Structure of Social Identity in the Information Space, Using Automated Data Processing Tools==
https://ceur-ws.org/Vol-2784/rpaper18.pdf
The Description of the Structure of Social Identity
in the Information Space, Using Automated Data
Processing Tools
Boris Nizomutdinov1[0000-0002-4090-9564], Anna Uglova2 [0000-0002-8072-0539],
Aleksandr Tropnikov3 [0000-0002-4179-536X]
1,3 ITMO University, Kronverksky Pr. 49, 197101, Saint-Petersburg, Russia
2 Herzen University, 48 Moyka Embankment, 191186 Saint-Petersburg, Russia
boris-wels@yandex.ru
Abstract. Recently, the topic of analyzing digital social identity has become
more relevant all over the world. The article presents the results of the second
stage of a research project involving the development of a methodology for au-
tomated analysis of digital social identity based on the VKontakte social network
in order to study the relationship between the visual component of the social pro-
file and the psychological characteristics of respondents. To achieve this goal, a
comparative analysis of the tools based on the use of machine learning technolo-
gies for the automated analysis of text and visual data that form the basis of the
information image of social identity was carried out. Using cluster analysis, vis-
ual identity strategies were identified. To identify digital factors mediating the
formation of social identity, a correlation analysis of data obtained by means of
automated analysis of graphic data and the results of psychodiagnostic research
was used. Visual identity strategies have formed multiple relationships with the
psychological characteristics of users. Based on the obtained relationships, a con-
clusion was made about the broad possibilities of automated analysis of digital
social identity.
Keywords: machine learning, cluster analysis, graphical data analysis, social
networks, social identity, visual self-presentation.
1 Introduction
At the present stage of development of social and psychological sciences, there is a lack
of scientific research devoted to the analysis of the formation of social identity in the
changing socio-cultural environment and the digitalization of modern society. Immer-
sion in the Internet space has a strong influence on the formation of motivational, reg-
ulatory, and reflexive spheres of the user's personality and can become both a protective
factor and risk factors for the destruction of social identity [5, 17]. The search is relevant
due to strengthening the impact of virtual images on real life and the network nature of
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
222
almost all social interactions of modern people, the automated analysis of which will
allow analyzing the social identity of users in real time [2, 12].
We can say that modern socio-psychological research needs to update the methodo-
logical model of experimental study of social identity through an interdisciplinary ap-
proach and the introduction of modern information technologies in the process of ana-
lyzing the forms and mechanisms of personality presentation in the virtual space [10].
Social networks are the most promising platform for exploring the inner world of users,
as well as individual and group social interactions [7]. The majority of Russian studies
that study people in the information space rely on traditional psychodiagnostic research
methods. This research is aimed at combining traditional psychodiagnostic methods
and modern automated methods of data processing and collection to further predict the
socio-psychological characteristics of the user based on the analysis of their social pro-
file without using time-consuming psychodiagnostic techniques [11].
Modern social networking sites are databases of publicly available information that
users themselves fill in. This information can provide a basis for analyzing socio-psy-
chological characteristics, identifying vulnerable social groups (people with suicidal
tendencies, people with antisocial habits and behavior) [1, 4].
Modern digital information processing technologies can be used to facilitate the
identification of these and many other human characteristics. Automated data collec-
tion, parsing of personal pages, and machine image processing-all these technologies
allow you to speed up and expand the methods of analyzing users of social networks
[14, 15].
In our pilot study, where the information image was analyzed, a theoretical model
of automated data processing of social network users was developed [6].
The purpose of this work is to develop a set of automated tools for analyzing the
factors of digital social identity formation, based on the analysis of text and visual in-
formation, and to correlate the data obtained in the automated mode from the social
profile with the results of a psychodiagnostic experimental study of subjects to identify
significant relationships between their personal traits and elements of the information
image.
2 Research methodology
Currently, there are practically no interdisciplinary Russian socio-psychological studies
devoted to automated analysis of the structure of digital changes in the social identity
of an individual. Existing international research provides a methodological basis for
combining psychological, sociological and informational approaches that will describe
the digital environment as one of the basic areas of life that set the direction of modern
human development (Mossberger K., Soldatova G.) and answer the question of the de-
gree of influence information technologies on the psychological state of the user, his
interpersonal, group and social relations [17].
223
2.1 Methodological model of the study
The study was conducted in several stages:
The task of the first stage was to analyze tools and programs designed to collect and
analyze data from their social networks. A comparative analysis of functions was
required to select suitable software and services options.
At the second stage, subjects were selected to conduct a survey and an experimental
study of socio-psychological characteristics. A psychodiagnostic study was con-
ducted. In addition, the respondents gave their consent to the analysis and processing
of their profile in the social network Vkontakte.
At the third stage, the authors parsed the profiles of users in the Vkontakte social
network who underwent a psychodiagnostic study at the second stage. Special ser-
vices were used for uploading, as well as available social network APIs. All pro-
cessing was carried out with the consent of the respondents and did not violate the
user's personal data processing policy. In the course of discharge were screened in-
active or disabled users, it is possible to reduce the inaccuracy of the data. This stage
allowed us to select the appropriate tools for data collection. In future research, we
plan to develop a script that allows you to automatically exclude unsuitable profiles
for analysis.
At the final stage, using the methods of mathematical statistics, using cluster and
correlation analysis, the relationships between the uploaded data from social profiles
and the socio-psychological characteristics of users were revealed.
The study involved 176 people belonging to the first period of adulthood (19–32
years). The average age of the subjects is 25.3 years. The study involved 58 % (103)
women and 42 % (73) men.
The traditional psychodiagnostic method was used to study the psychological char-
acteristics of the subjects, mediating the formation of social identity. We have devel-
oped a set of psychodiagnostic tools:
Maslow's needs satisfaction diagnostic test, which revealed the hierarchy of an indi-
vidual's basic needs that underlie social identity;
C. Schwartz's value questionnaire, aimed at analyzing social and individual values
that determine the direction of interaction in the network;
Self-presentation tactics scale (Lee S.-J., Quigley B., Nesler M., Corbett A.,
Tedeschi J.), which defines the leading strategies and tactics of self-presentation in
the information space.
The following automated methods were used in the study of a user's profile in a
social network:
methods of automated collection of publicly available information from profile
pages in the Vkontakte social network. As a result of data collection, an array was
obtained that represented statistical information about the user's social network pro-
file. Two approaches were used to collect information from social networks:
224
─ Parsing basic information of user profile pages in a social network vk.com, based
on collecting information via the API, as well as uploading HTML content, fol-
lowed by parsing and searching for the necessary profile information. At the mo-
ment, the privacy policy allows you to hide the main information, but even if all
information is hidden, you can upload your avatar, name, number of subscrip-
tions, songs, photos and videos. If the profile is open, the list of available infor-
mation is much wider.
─ classification Methods based on the supervised learning approach and semi-su-
pervised learning approach. The essence of the task is to correlate a set of ob-
jects/features with a predefined set of classes/categories. In this study, they were
used to automatically classify communities/groups in the Vkontakte social net-
work that a user belongs to by various topics in more than 30 areas (Science,
Design, Humor, Entertainment, Games, Travel, etc.);
methods for processing graphic and text data for automated analysis of graphic data,
aimed at identifying a number of specified parameters: the number of people in the
photo, people's emotions, objects present, composition, color correction, blurring,
etc.
2.2 Methodological analysis of the tools
There is a certain array of developments based on the use of machine learning technol-
ogies in terms of the tools for the proposed research. Machine learning is a broad section
of the field of artificial intelligence, that studies methods of building learning algo-
rithms. There are three broad approaches in machine learning depending on the type of
feedback signals or data, transmitted to the training system [16]:
supervised learning and semi-automated learning: the machine receives a sample of
input data and expected results, set by the "teacher", the aim of the system is - to
study the general rules of bringing input data to the final result;
unsupervised learning: the system tries to find some structure in the data pattern by
itself and then to make some conclusion, and this approach does not provide for pre-
marked data sets.
reinforcement learning: a software application interacts with a changing environ-
ment, in which the system must perform some specific task without preliminary in-
dicating the final result.
At present time machine learning methods solve a fairly wide range of the following
standard tasks:
classification task (Sokolova M., Lapalme G.) is the most popular type of tasks in
machine learning, it is a mapping of a certain set of objects with class labels based
on a given finite training sample, divided into classes beforehand. There are binary
and multi-class classifications, as well as disjoint, intersecting, and fuzzy classes;
regression task differs from the classification, that the end result is a real number or
a numeric vector (a gap);
225
learning to rank task has a special feature in getting a lot of output data and automat-
ically sorting by response values; it is often used in information search and text min-
ing;
forecasting task is a set of objects that are time series segments that end at the mo-
ment when it is needed to make a forecast for the future value;
clustering task (Bakker B., Heskes T.) is to divide objects into clusters (disjoint
groups), using data on the similarity of paired elements and common signs;
outliers detection task is to detect a small number of objects that deviate from the
norm of the training sample; this approach allows to get rid of unwanted noises in
the sample, inaccuracies or errors in the data;
missing values task is to replace the missing values in the object matrix with their
predicted values.
Machine learning as an interdisciplinary field includes such related areas of
knowledge, as mathematical statistics, optimization methods, information extraction
methods, data mining. Research in the field of machine learning is carried out by con-
ducting experiments on model or real data to check the correctness of methods, confirm
hypotheses, get a list of statistically significant criteria, calculate statistical metrics.
As central methods (which are also often called approaches or algorithms) of ma-
chine learning are the following (Zhang C., Ma Y., Dietterich T.G.): linear and logistic
regression; SVM (support vector machines); decision trees; random forest; Naive
Bayes; boosting; neural networks; deep learning; K-means; KNN (k-nearest neigh-
bors); self-organizing maps etc.
Each of the presented methods has its own advantages and disadvantages, therefore
they can be used to solve completely different types of problems. Natural Language
Processing (NLP) it is used in combination with machine learning methods to perform
tasks such as, emotion detection, text tonality analysis, speech recognition, spam clas-
sification in emails, machine translation, speech recognition, and so on. NLP plays a
very important role in collection, processing and analysis data by converting natural
language into a format, that further used by machine learning methods to implement its
own algorithms. Thus, Goldberg Y. in his works, and also Batura T.V. in a review study
of automatic text classification methods and Zibert A.O. with Hrustalev V.I. provide
the main methods and approaches of natural language processing (NLP) in the frames
of working with neural networks of different architectures and with standard statistical
models for implementing deep learning methods, they also present the main results,
obtained during the implementation of these methods.
Among such methods and approaches can be distinguished the following: tokeniza-
tion; making a list of stop words; stemming; lemmatization; Named Entity Recognition;
“bag of words” model; function calculation TF-IDF; Word2Vec algorithms; etc.
Based on a number of ongoing studies in the field of identifying the relationships of
social networks and personal characteristics of users (Kosinski M., Zhang C., Settanni
M., Azucar D., Marengo D.), we can identify several areas for determining the relation-
ship of data obtained from social networks and personal qualities of users [9]:
processing of photo images
semantic analysis of text user "posts"
226
analysis of statistical data.
Collecting information of this scale, as well as analyzing it, requires considerable
effort. One of the ways to speed up this process is to use cloud computing technologies
that can remotely accept and process requests for analysis of various data: both text and
graphic. They can be used to perform both complex cognitive transformations on data
(search for individual objects in the image, compose text descriptions of photos), and
complex analytical functions (counting the frequency of word use, highlighting the pre-
vailing speech patterns, determining the subject of messages).
Such cloud platforms are developed by large companies that specialize in software
development and distribution: Microsoft, Google, and Amazon. Their cloud solutions
provide various functions that can automate the process of analyzing a user's profile in
social networks to varying degrees.
Microsoft's multi-functional Azure cloud platform is one of the largest open services
offering remote data processing services. The functionality of the service is designed
for individual users who use the capabilities of computers for personal purposes, as well
as for entire companies that conduct extensive monitoring studies.
The core module of Azure is the intelligent cognitive Services interface, which com-
bines most of the complex and complex functions of machine data processing. Among
them: face recognition (for the presence of certain emotions, estimated age, skin color),
computer vision (analysis of photos for the presence of specific objects, drawing up
text descriptions) [3].
Over the past 4 years, several large and dozens of pilot studies have been conducted
related to the application of the functionality of this platform. Most of these studies
were aimed at identifying various relationships between the identified characteristics
of photos and personal traits of a person. For example, various correlations were estab-
lished between the technical data of the image (blurring, the presence of a face in focus,
the photo's sharpness) and the age of the person, the relationship between the number
of faces in the photos and the personality traits of extroverts [8].
Working with Microsoft Azure Cognitive Services was performed using the API
connection using the available methods. A special script was prepared for this purpose.
The service allows you to perform 20 transactions per minute, with a total of 5,000 and
50,000 transactions per month for the Computer vision and face Recognition services,
respectively.
Google also provides services for the allocation of computing power and services
United by a common Google Cloud Plat-form platform. As a direct competitor to Mi-
crosoft and their Azure platform, Google provides similar data processing services: the
Cloud product Cloud Vision repeats most of the functions of Azure and is used for
complex operations on graphic and text data (image analysis, pattern detection, ma-
chine text analysis).
Like its counterparts, Cloud Vision is able to identify faces in images, analyze the
emotions displayed, capture distinctive features in the form of hair or headwear, iden-
tify objects and situations in the background, and create a set of "tags" for images. Un-
like Microsoft Azure, Google Cloud Vision specializes in analyzing existing objects
when processing an image, rather than simply fixing them. Thus, the set of final tags
227
for the two services differs: the Microsoft service lists all possible objects in the image
and displays a brief description of what is happening, while the Google service displays
an already analyzed set of tags obtained from certain objects. Amazon Web Services is
a platform for providing machine data processing services from Amazon. This platform
specializes in providing services for organizing cloud databases, serverless computing,
development tools, virtual servers, and storage. Currently, Amazon Web Services is
focused on providing infrastructure and platform services. However, AWS provides
Amazon Rekognition technology for image and video mining.
Amazon Rekognition provides the standard functionality of the machine image pro-
cessing service. You can use it to determine faces, objects, tags, the degree of decency
of the image, the color scheme, and a number of other technical characteristics.
In the course of conducting experimental testing and studying analytical articles of
consulting companies, it was found that this service is inferior to the described two
previous ones in the accuracy of determining the displayed objects.
Amazon Rekognition can only capture objects in an image, but not analyze the over-
all picture, unlike Google Cloud Vision. Also, the technology from Amazon is not able
to make a meaningful description of the image as a competitive service from Microsoft.
However, the overall accuracy of fixed tags is relatively low.
Based on a number of studies (Sophie W.F., Xenos S., Ryan T.) [9, 13, 18], it can
be established that linguistic features can be used to recognize personal characteristics.
The described methods can be used not only for analyzing handwritten or typewritten
texts, but also texts left by users of social networks.
Most of the research conducted on this topic uses the program “Linguistic Inquiry
and Word Count", which allows you to calculate the proportion of certain parts of
speech in the text, the number of words longer than a certain value, the number of
punctuation marks used, and the number of words from various lexical and semantic
categories. Further, the data obtained are correlated with the data obtained during test-
ing of the authors of the text and during processing, the correlation between certain
linguistic features and personal characteristics is highlighted.
The main problem is the lack of publicly available and well-developed Russian lan-
guage libraries for this type of program. However, using online services that are similar
in functionality, you can process more text information with less effort.
Microsoft Azure, previously described as a platform for providing cloud computing
services, also has a text message analysis service. The data obtained can be used to
determine a person's attitude to certain objects or events, the subject of their text mes-
sages, the range of interests and the General tone of publications.
ISPRAS API is a non-commercial product developed by the Ivannikov Institute of
system programming of the Russian Academy of Sciences. This product is specialized
in natural language processing and analysis. Currently, there are several demos of var-
ious software solutions, one of which is aimed at semantic text analysis – Text Pro-
cessing. This software product is implemented on a non-commercial basis and offers
more impressive functionality than its analogues. The data obtained during the pro-
cessing of text messages can be used not only to determine the user's Hobbies or their
relationship to certain objects, but also to find relationships between the frequency of
use of certain parts of speech and personal traits.
228
One of the most well-known companies engaged in machine analysis of user data
was Cambridge Analytica (CA), created on the basis of research by Kosinski M. Kosin-
ski M. conducted research using social media apps, asking users to take various psy-
chological tests. By collecting users ' data with their permission, he was able to prove
that there is a link between a person's online activities and their real "alter ego". For
example, using 68 "likes", you can determine the user's gender, age, sexual orientation,
and political preferences with a certain degree of probability. Based on these studies,
the system used by the CA was developed. Using user data from social networks and
based on the results of a study by Kosinski M., SA specialists were able to predict their
information images. Further, information images were segmented according to various
parameters (political preferences, psychological traits, etc.), after which data about
sorted users were sold to third companies to demonstrate targeted political advertising
that was created specifically for certain categories of people.
Using this experience, many Russian companies began to offer their services for
identifying and segmenting the user population. So, the head of Sberbank said that the
company intends to use the methods of Kosinski M. to identify customers with a high
risk of late payments.
At the moment, there is a wide range of solutions on the Russian and international
market, which is represented by various companies, such as: Palantir Technologies,
Cambridge Analytica (abolished on 01.05.2018), Me-dialogia, Search-IT, I-tech, etc.
The solutions provided by these companies are used in various business areas
credit scoring in banking organizations;
recruitment of personnel in HR departments;
personalized advertising;
various types of recommendation systems.
In addition to commercial applications, these technologies are also used in political
agitation, propaganda, and identification of potentially dangerous or vulnerable social
groups
3 The results of the study
At the first stage of automated analysis of social network profiles the visual content of
users using the Microsoft Azure Cognitive Services platform was analyzed, which al-
lowed to identify 227 tags, found on users' avatars. We selected the main 46 tags (ob-
jects), that are found in the majority of subjects. The tag data set represents the basic
visual identity of Vkontakte users, which can become a unique source of information
about a person's social life, gender, age, and political characteristics for various types
of socio-psychological research.
To identify the main strategies for building visual identity, presented in photos that
combine tags obtained during automatic analysis, cluster analysis (Ward's method) was
used (Figure 1).
229
Fig. 1. Categorical structure of visual identity of users of the Vkontakte social network.
Cluster analysis allowed us to identify and describe the main strategies for building
visual identity in the social network:
Business, portrait self-presentation (black, black and white, black hair, white, lip,
lipstick, eyes, long hair, portrait, hair), intended to create a certain distance between
the user and the viewer;
Visual storytelling (car, drawing, dress, flower, ground, plant, tree, footwear, stand-
ing, hiking, sky, jacket, wall), intended to convey an emotional and imaginative mes-
sage through the natural surroundings, to please audience
Staging of feminine self-presentation (clothing, face, fashion, fashion accessory,
girl, human face, outdoor, person, smile, woman, female), it reflects a regulated so-
cially-oriented identity and presents a set of popular clichés, to create a normative
image;
Staging of male self-presentation (glass, glasses, selfie, indoor, posing, man, male),
it is also a reflection of a regulated socially-oriented identity;
230
Anonymous self-presentation (design, logo, minimalistic, text), intended to indicate
the user's unwillingness to expand the circle of social contacts, aimed at a narrow
circle of users.
At the next stage using correlation analysis we identified reliably significant connec-
tions between the identified strategies for building visual identity and elements of real
social identity studied in the course of psychodiagnostic research:
Business, portrait self-presentation is more often used by subjects who spend a large
amount of time on a social network (r=0,16, p⩽0,05), with a strong need for profes-
sional development and self-actualization (r=0,22, p⩽0,05);
Users who use the "visual storytelling" strategy are more likely to indicate that their
profile matches their real Self (r=0,17, p⩽0,05), the value of self-esteem is important
to them (r=0,17, p⩽0,05), it is important for them to demonstrate the attributes of
their identity in the photo. Also the value of unity with nature (r=0,18, p⩽0,05) is
significant to them what is successfully demonstrated through visual self-presenta-
tion.
Users focused on staged female self-presentation, use a large number of social net-
works (r=0,19, p⩽0,05), to manifest the preferred values of “finding the meaning of
life” (r=0,17, p⩽0,05), "values of self-respect» (r=0,26, p⩽0,05), “the values of unity
with nature” (r=0,16, p⩽0,05), "the values of accepting life» (r=0,16, p⩽0,05), “the
value of honesty” (r=0,19, p⩽0,05), “self-affirmation needs” (r=0,19, p⩽0,05), “the
need for self-actualization” (r=0,17, p⩽0,05), “self-value” (r=0,19, p⩽0,05). We can
say that this group of users actively develops the informational space, that merges with
their real life and is used to assert their value and significance as a person.
Users, who use staged male self-presentation choose such behavior tactics as “bul-
lying” in interaction with other people (r=0,16, p⩽0,05), they do not see value in the
“sense of belonging to a group” (r=-0,17, p⩽0,05), for them other people opinion is not
significant (r=-0.20, p⩽0.05), they do not feel the need to be modest (r=-0.22, p⩽0.05),
which may indicate a desire to present a traditional stereotypical masculine identity.
Anonymous self-presentation is preferred by users who use a minimal number of
social networks (r=-0.17, p⩽0.05), for whom the value of freedom is important (r=0.16,
p⩽0.05) and the “value of self-respect” (r=-0.22, p⩽0.05), “value of obedience” (r=-
0.19, p⩽0.05) and “self-acceptance” (r=-0.18, p⩽0.05) are not significant.
At the next stage, for a qualitative value-semantic analysis of the content of users'
social identity, we performed an automatic classification of communities/groups in the
Vkontakte social network that the user belongs to. There were 31 main categories that
can be assigned to groups that are most frequently visited by users. Categories, in turn,
can be divided into 9 groups of values, that form the basis for the formation of social
identity:
1. Professional values (professional communities, job search);
2. Traditional family values (family, parenting, home improvement);
3. Value relationships (community for dating, finding a partner).
4. Values of social communication (local communities, communities of interests);
231
5. The value of diversity, of novelty (travel, leisure, entertainment, community
events);
6. Information values (mass media, public pages);
7. Hedonistic values (entertainment news, watching movies online, photos, music
groups, online stores, services);
8. Values of personal growth (scientific communities, creative communities, literary
communities);
9. Values of beauty and health (information about healthy lifestyles, nutrition, sports,
beauty).
After analyzing the percentage of communities in which network users most often
belong, we can say that users most often belong to local communities that manifest their
territorial affiliation to a social group in a particular district, city, or country (23.37%
of the subjects). The second place is occupied by professional communities (17.39% of
the subjects), which corresponds to the need of the subjects of the first period of mature
age (20-30 years) to develop and consolidate their professional identity. The third place
was taken by communities related to the satisfaction of hedonistic needs (groups dedi-
cated to cooking 11.24%, online stores 10.97%), which indicates the close interweaving
of their real and virtual lives and the use of social networks to organize their social
space. The fourth place is shared by groups dedicated to creativity (9.71%), family
(8.50%), and design (8.49%).
Thus, we can say that belonging to certain communities and visual self-presentation
reflect the internal solidarity of the user with certain ideals of modern society and allows
us to describe social identity as a multi-level structure that is reflected in the information
space.
4 Conclusion
In general, the study of digital social identity corresponds to the current task of funda-
mental psychology, which is to develop new approaches to the study of patterns of
personality formation in the information society. The existing range of needs of socio-
psychological and social practice in science-based technologies for supporting digital
communication relates to the need to create and test tools that allow you to build pre-
dictive models of behavior on the Internet that leads to the formation of social identity.
Research interest in the subsequent stages of the work should be focused on finding
non-obvious patterns and correlations in the source data by using machine learning
methods to speed up the getting and processing of information about social identity in
the information space, as well as evaluating the predictive reliability of the model based
on conducting and analyzing the results of a longitudinal study.
References
1. Cote, J.E., Schwartz, S.J.: Comparing psychological and sociological approaches to identity:
Identity status, identity capital, and the individualization process. Journal of Adolescence,
(25), 571–586 (2002), https://doi.org/10.1006/jado.2002.0511, last accessed 28.11.2020.
232
2. Emelin, V.A.: Vysshie psikhicheskie funktsii v kontekste tsifrovykh. Tsifrovoe obshchestvo
v kulturno-istoricheskoi paradigme, kollektivnaia monografiia. Pod redaktsiei T.D. Mart-
sinkovskoi, V.R. Orestovoi, O.V. Gavrichenko. Moskva, 177–181 (2019),
https://istina.msu.ru/projects/8804361/, last accessed 28.11.2020.
3. Kaiqi, H., Qiao, W., Zhenyang, W.: Natural color image enhancement and evaluation algo-
rithm based on human visual system. Computer Vision and Image Understanding, I (103),
52–63 (2006), https://doi.org/10.1016/j.cviu.2006.02.007, last accessed 28.11.2020.
4. Katz, J.E., Rice, R.E.: Social consequences of Internet use: Access, involvement and inter-
action. Cambridge, MA: The MIT Press, (2002), https://mitpress.mit.edu/books/social-con-
sequences-internet-use, last accessed 28.11.2020.
5. Letov, E.V. Setevaia identichnost v kontekste kulturnykh protsessov informatsionnogo ob-
shchestva: avtoreferat diss. … kand. filosof. nauk., 19 s. (2014), https://static.freerefer-
ats.ru/_avtoreferats/01007498640.pdf, last accessed 28.11.2020.
6. Nizomutdinov, B., Tropnikov, A., Uglova A.: Application of social networks user’ digital
fingerprints to predict their information image. Proceedings of the 13th International Con-
ference on Theory and Practice of Electronic Governance (ICEGOV2020), Athens, Greece,
April 1–3, 2020. ACM New York, NY, USA (2020),
https://doi.org/10.1145/3428502.3428635, last accessed 28.11.2020.
7. Nizomutdinov, B.A., Tropnikov, A.S., Uglova, A.B. Razrabotka prognosticheskoi modeli
informatsionnogo obraza polzovatelia s primeneniem avtomatizirovannykh sredstv
obrabotki dannykh iz sotsialnykh setei. Nauchnyi servis v seti Internet, (21). 532–540
(2019), https://www.elibrary.ru/item.asp?id=41256649, last accessed 28.11.2020.
8. Pianesi, F., Mana, N., Cappelletti, A., Lepri, B., Zancanaro, M.: Multimodal recognition of
personality traits in social interactions. Proc of the 10th International Conference on Multi-
modal Interfaces, 53–60 (2008), https://doi.org/10.1145/1452392.1452404, last accessed
28.11.2020.
9. Settanni, M., Azucar, D., Marengo, D.: Predicting the Big 5 personality traits from digital
footprints on social media: A meta-analysis. Personality and Individual Differences, 150–
159 (2017), https://doi.org/10.1016/j.paid.2017.12.018, last accessed 28.11.2020.
10. Soloveva, L.N. Tsifrovaia identichnost kak novyi vid identichnosti cheloveka informatsion-
noi epokhi. Obshchestvo: filosofiia, istoriia, kultura (2018), doi: 10.24158/fik.2018.12.6,
last accessed 28.11.2020.
11. Tropnikov, A., Uglova, A., Nizomutdinov, B.: Development of a prognostic model of the
user's information image using automated tools for processing data from social networks.
Communications in Computer and Information Science, 1038, 405–413 (2019),
https://link.springer.com/chapter/10.1007%2F978-3-030-37858-5_33, last accessed
28.11.2020
12. Uglova, A.B., Koroleva, N.N., Bogdanovskaia, I.M., Lugovaia, V.F.: Strategii virtualnoi
samoprezentatsii sovremennykh rossiiskikh uchitelei v sotsialnykh setiakh. Pisma v Emis-
siia.Offlain: elektronnyi nauchnyi zhurnal, (10), 2773 (2019), http://www.emissia.org/of-
fline/2019/2773.htm, last accessed 28.11.2020.
13. Waterloo, S.F., Baumgartner, S.E., Peter, J.: Norms of online expressions of emotion: Com-
paring Facebook, Twitter, Instagram, and WhatsApp. SAGE, (20), 1–19 (2017),
https://doi.org/10.1177/1461444817707349, last accessed 28.11.2020.
14. Voiskunskii, A., Evdokimenko, A., Fedunina, N.: Setevaia i realnaia identichnost:
sravnitelnoe issledovanie. Psikhologiia. Zhurnal Vysshei shkoly ekonomiki, 10(2), 98–121
(2013), https://publications.hse.ru/articles/101407407, last accessed 28.11.2020.
233
15. Vartanova, E.L.: The media and the individual: economic and psychological interrelations.
Psychology in Russia: State of the Art, 6(1), 110–118 (2013), http://psychologyin-
russia.com/volumes/index.php?article=2089, last accessed 28.11.2020.
16. Vapnik, V.N.: An Overview of Statistical Learning Theory. Neural Networks, IEEE Trans-
actions on., 10(5), 988–999 (1999), https://doi.org/10.1109/72.788640, last accessed
28.11.2020.
17. Voiskounsky, A.Ye.: Psychology of computerize ation as a step towards the development
of cyberpsychology. Psychology in Russia: State of the Art., 6(4), 150–159 (2013),
http://psychologyinrussia.com/volumes/pdf/2013_4/2013_4_150-159.Pdf, last accessed
28.11.2020.
18. Xenos, S., Ryan, T.: Who uses Facebook? An investigation into the relationship between
the Big Five, shyness, narcissism, loneliness, and Facebook usage. Computers in Human
Behavior, 27(5), 1658–1664 (2011), https://doi.org/10.1016/j.chb.2011.02.004, last ac-
cessed 28.11.2020.