=Paper=
{{Paper
|id=Vol-3296/paper6
|storemode=property
|title=Personality Prediction from Social Networks: a Review of Works
|pdfUrl=https://ceur-ws.org/Vol-3296/paper6.pdf
|volume=Vol-3296
|authors=Nataliia Khymytsia,Serhii Holub,Maria Holub,Oleksandr Mоrushko
|dblpUrl=https://dblp.org/rec/conf/scia2/KhymytsiaHHM22
}}
==Personality Prediction from Social Networks: a Review of Works==
https://ceur-ws.org/Vol-3296/paper6.pdf
Personality Prediction from Social Networks: a Review of Works
Nataliia Khymytsiaa, Serhii Holubb , Maria Holubb and Oleksandr Mоrushkoa
a
Lviv Polytechnic National University, S. Bandera street 12, Lviv, 79013,Ukraine
b
Cherkasy State Technological University, Shevchenko Boulevard 460, 18006 Cherkasy, Ukraine
Abstract
Today, the potential of social networks for all types of communication is difficult to
overestimate. For business communication in virtual communities, it is important to take into
account the socio-psychological features of the participants in the communication process. This
actualized various aspects of network identity, virtual personality behavior, and online persona.
This study offers a review of the scientific literature on personality prediction based on the
analysis of different content generated in social networks. First, we analyzed the literature that
used methods for analyzing text and photos from social networks using different approaches.
The analysis emphasized that in the modern digital age, psychological aspects of
communication in social networks and methods of identifying the personality of social network
users based on their social activity and the practice of using language and images are very
much in demand and relevant. We have proposed a comparison table of existing personality
prediction methods based on relevant parameters. In addition, based on the analysis, a program
of future research in the field of intellectual analysis of content for the purpose of personality
prediction in social networks was determined.
Keywords 1
Social networks, Facebook, LinkedIn, Twitter, message, personality, personality prediction,
review of works, іntelligent monitoring, machine learning.
1. Introduction
Prediction of personality is scientific question that science tried to solve many years ago, nowadays
as well, because it is the person who acts in a unique way as the subject of activity and communication,
they are the builder and transformer of the social and material world, the creator of spiritual and material
values.
The problem of personality prediction has become especially relevant in the conditions of the
unprecedented dynamism of social processes, the scientific and technical revolution, and the mega-
popularity of social networks. Social networks Facebook, YouTube, Instagram, WhatsApp, Twitter,
which have the largest number of active users, have become a prominent platform for the exchange of
information of various kinds. On the other hand, it is consolidated data from social networks that is the
most valuable information for analyzing human actions and predicting personality.
The use of social networks provides scientists with the opportunity to optimize the search prediction
of an individual to determine the possible state of a person in the future. Such a forecast is based on the
conditional continuation into the future of the development trends of the subject in the past and present,
abstracting from the proposed solutions, actions on the basis of which it is possible to radically change
the trends, sometimes to cause self-fulfillment or self-destruction of the forecast. Information search
results.
SCIA-2022: 1st International Workshop on Social Communication and Information Activity in Digital Humanities, October 20, 2022, Lviv,
Ukraine
EMAIL: nhymytsa@gmail.com (N. Khymytsia); s.holub@chdtu.edu.ua (S. Holub); sashasokolovska92@gmail.com (M. Holub);
morushkoo@gmail.com (O. Morushko)
ORCID: 0000-0003-4076-3830 (N. Khymytsia); 0000-0002-5523-6120 (S. Holub); 0000-0002-0553-8163 (M. Holub); 0000-0001-8872-
2830 (O. Morushko)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
2. Information search results.
2.1. Review of studies on the definition and prediction of personality, its
psychological characteristics from social media content
It is believed that our personality is formed, stable and difficult to change. A number of well-known
scientists, including H. Jung and A. Augustinaviciute [1, p. 30], held the opinion that the psychological
type of a person is unchanged throughout life. Moreover, according to Jung, attempts to change a
person's psychological type inevitably lead to mental disorders of the individual. [1, 2].
A number of authors pay attention to the language stability of an individual, when the intention and
behavior of an each person individually or communities of people are aimed at consistent, unwavering
use of a certain language or language means in daily communication. In this context, the practice of
personality modeling based on five main traits ("Big Five" ("OCEAN") has become widespread. Such
a personality model represents:
Openness — the openness of experience. Appreciation of art, emotions, adventure, unusual ideas,
curiosity, and diversity of experience
Conscientiousness — conscientiousness. Tendency to be organized and reliable, to demonstrate self-
discipline, to act obediently, to strive for achievement, and to prefer planned rather than spontaneous
behavior.
Extraversion — extraversion. Energy, positive emotions, sociability and ability to activate in the
company of others and talkativeness. Extroverted people tend to be more dominant in social settings,
unlike introverted people, who can act shyer and reserved in this environment.
Agreeableness — benevolence. Tendency to be sympathetic and cooperative. It is also an indicator
of a trusting and altruistic nature, as well as whether a person is generally well-mannered or not.
Neuroticism — neuroticism. Tendency to be prone to psychological stress. A tendency to easily feel
unpleasant emotions such as anger, anxiety, depression, and vulnerability.
The practical application of the model ("OCEAN") is proposed in works [3-5].
Today, the mega-popularity of social media and online social networks has led to numerous studies
on the problem of online identity, online persona, and identity recognition. In the work of scientists
Tommy Tandera, Derwin Suhartono, RiniWongso, Yen Lina Prasetio [6], a prediction system that can
automatically predict the user's personality based on his actions on Facebook is proposed. The authors
used a personality model representing five main traits: Openness Conscientiousness Extraversion
Agreeableness Neuroticism. The data set used in this study is the sample data for the myPersonality
project, and the second data set consists of 150 manually collected customers. Facebook API Graph is
used in the data collection process. Custom branding is then done manually by entering users' email
addresses into Apply Magic Sauce.
Majumder, S. Poria, A. Gelbukh, and E. Cambria [7] proposed a new technique for document
modeling based on CNN feature extractor. The researchers used James Pennebaker and Laura King's
stream-of-consciousness dataset. A sentence-essay filter transformation was used to obtain a sentence
model in the form of n-gram feature vectors. Also, for classification purposes, this study uses a neural
network fully coupled with one hidden layer.
These researchers also work in the field of multimodal sentiment analysis. They developed a novel
feature-combining strategy that works in a hierarchical fashion, first combining two-by-two modalities
before combining all three modalities. The implementation of the method proposed by the researchers
is publicly available in the form of open code [8].
D. Xue [9] developed a method of machine learning label distribution (LDL), with which it is
possible to recognize the symbols of microblogs. The researcher processed 994 profiles and microblogs
of active Sina Weibo customers, and obtained 113 characteristics. All characteristics were divided into
three categories: profile-based static characteristics, profile-based dynamic characteristics, and content-
based microblog characteristics.
In the work of researchers N. Alsadhan and D. Skillicorn [10], a new method of personality
prediction based on short text processing was developed and applied. The data set used consists of four
elements. Three elements are marked by "OCEAN" personality traits and one with Myers Briggs
personality types. Python is used to obtain grids of archival words by selecting the 1000 most regular
words in each corpus and requiring each word to occur multiple times.
In his work, M. Tadesse [11] used a set of information about the myPersonality project to investigate
the existence of social network structures and linguistic characteristics related to personal relationships.
The study also analyzed and compared four machine learning models and made a correlation between
each set of features and character traits.
C. Li, J. Wan, and B. Wang [12] extracted and analyzed social information from the Chinese
microblogging service Weibo. The purpose of the study was to predict personality traits by exploiting
user textual information. The work of these scholars used correlation analysis and principal component
research to select usage information, and therefore used multiple correlation models, a gray prediction
model, and therefore a multitask model to predict and analyze the results.
In a scientific study, A. Laleh and R. Shahram [13] suggested a model that, on the basis of processed
activities of Facebook network users in the format of preferences, is able to predict the assessment of
their Big Five personality traits. In the study, the LASSO algorithm was used to select the best
characteristics of Facebook users and predict the Big Five model.
In a scientific study, Farnadi [14] proposed a deep learning strategy that extracts and uses data in
different ways. The hybrid user profiling framework used, which provides a joint representation of all
modalities, integrates three sources of feature-level information.
Author Y. Wang [15] investigated the relationship between language characteristics and
psychological characteristics of Social Media users. The new language tool was designed to extract
three categories of features, namely n-gram packets, POS tags, and word vectors. While evaluating
these characteristics, the use of language for different symbols was observed.
Author Gjurkovic [16] ran a large-scale data set labeled with MBTI types, obtained and analyzed a
rich collection of characteristics from this data set, and trained and evaluated comparative models for
personality prediction. Three different classifiers were used, support vector machine (SVM), 2-regular
logistic regression (LR), and three-layer multilayer perceptron.
Scientists in [17] presented a study of different dimensionality reduction methods for extracting
hidden features from the text content of tweets for predicting the personality traits of Twitter users. In
particular, the researchers tested: PCA, LDA, and NMF and proved that the latent characteristics of the
LDA technique are very complete, as it gives the best results in predicting three personality traits and
gives acceptable results in the other two traits.
In the study of the group of authors [18], an automatic method of identifying a person based on
Twitter is proposed. Researchers use such a machine learning algorithm as SGD, two ensemble learning
algorithms, and other techniques.
Author Lei Zhang [19] proposed a new situation-based interaction learning model. In this technique,
the obtained situation is characterized by the DIAMONDS lexicon and the calculated interaction.
Author Hassanein [20] combined several images of users' texts with several semantic indicators in
Facebook status updates to predict the personality of their users. The author, Ahmad [21], analyzed
tweets using the DISC system (dominance, influence, relevance, stability). They grouped together more
than 1 million tweets and analyzed them for the sentiment.
In the study [22], topical issues of formalization and definition of specific roles of users of social
networks are considered. The authors proposed a special system of user activity indicators. Activity
indicators were taken as a basis for distinguishing special groups of users, such as opinion leaders,
opponents, and trolls. For each group of users, special marks were developed in the form of
mathematical expressions using the main proposed values. Another group of signs was based on
linguistic influence techniques that are purposefully used in online communication.
In another work of these authors, a group of individual identification data, its network identification,
and groups of state security characteristics were considered. Two components are included in the model
to describe network characteristics : user activity log and social portrait. Characteristics that describe
the level of user interaction with other users and communities are considered [23].
The work [24] presents the results of research into the processes of using information technologies
of multi-level intelligent monitoring for the classification of text messages on the Internet in social
networks. Two classes of text messages (class of intruders and non-intruders) were formed from the
content of the Facebook network selected by experts. The authors investigated the processes of
decomposition of text messages of social networks, adaptive formation of dictionaries of signs, use of
an agent approach to the construction of a classifier, and its use for the recognition of intruders. In this
work, hypotheses regarding the existence of participants in Ukrainian-speaking social communities who
have a common style of presentation of messages are experimentally confirmed.
A separate direction of scientific research is the sociometric analysis of the content of groups and
communities in social networks with the aim of predicting effective team activity. The article by Ion
Georgiou, Ronald Concer, Andrej Mrvar describes the methodology for analyzing the compatibility
and diversity of different, interrelated, structural configurations of groups that are oriented towards
achieving a certain goal based on sociometric principles and methodological and measurement
standards [25].
The study [26] proposed an algorithm that uses the community structure of a social network and
forms a team by choosing a leader together with neighbors within the community. A community-based
team building strategy called TFC leads to a scalable approach that builds teams in a reasonable amount
of time across very large networks. Experimentation is conducted on a well-known DBLP dataset,
where the task is treated as writing a research paper and the title words are treated as skills. The problem
of forming a team boils down to finding possible authors for this work who possess the necessary skills
and have the lowest communication costs.
The paper [27] presents an analysis of the advantages of the Agent SocialMetric web tool, which is
based on the sequential analysis of social networks with intelligent dialogue agents. Researchers Jiamou
Liu, Ziheng Wei in their work proposed a game model of cohesion, which is not only based on a social
network, but also reflects people's social needs [28]. This model is presented as a type of joint activities
in which all participants can gain popularity through the strategic formation of groups.
Based on the analysis of the Twitter social network, Martin Grandjean highlighted the structure of
relationships and identified users with a special position. His work also shows that language groups are
key factors for the justification of clustering in the network [29].
The study [30] examines the definition of the psychological type of a person through social networks
with the help of social analysis. Methods of visual and verbal determination of a person's sociotype are
used. Practical examples of this method are given. They clearly demonstrate that knowledge of certain
features of the body or the style of human speech with great probability helps to predict how they can
behave in certain situations.
In [31], the authors recommend using Jung's basis as criteria for selecting personnel for vacant
positions. With the help of four pairs of dichotomous signs, the psyche of a person is determined with
high probability and, on this basis, their ability to be involved in the performance of a certain type of
work is assessed. For this, the authors suggest using the following methods of analysis: visual, verbal,
and, if possible, testing. The study also describes an algorithm for using these methods to form teams,
assess their cohesion, and determine the optimal leader. As a source of information for such analysis, it
is suggested to use photos and text messages from social networks Facebook and LinkedIn.
2.1.1. Setting the Task.
The purpose of the work is :
make an overview of studies that investigate various aspects of the problem of personality
prediction from various content in social networks;
determine directions of future research in the field of intellectual content analysis for the purpose
of predicting personality in social networks.
2.1.2. Review of the various methods used to predict personality from social
media content
A forecast is a scientifically based conclusion about the future state of an object. A state is a set of
properties of an object. This means that a forecast is a scientifically based conclusion about how the
properties of an object will change in the future.
The condition of the object can change significantly under the influence of internal and external factors.
Today, one such external factor is the social media environment.
Analysis of the content created by the user of social networks is the primary source for identifying a
person's character. Researchers have intensified their research on predicting a user's personality based
on his activity in social networks. Based on the analyzed content from social networks, scientists have
proposed various methods and approaches for personality prediction.
The results of a review of works that investigate various aspects of the problem of personality prediction
from various content in social networks are presented in Table 1.
Table 1
Review of works that investigate various aspects of the problem of personality prediction from various
content in social networks
Author Summary Dataset Method
Tommy Constructed a scheme myPersonality with a Both open and closed
Tandera(2017) to predict big five. The profiles dictionaries are used for
Facebook users of 150 Facebook machine learning of
personality users were studied. models, in particular for
through deep learning Information was their deep learning.
architectures collected manually the study used MLP,
LSTM, GRU, CNN+1D
and LSTM with CNN 1D
deep learning
architectures
Navonil Majumder On the basis of the myPersonality with Method for determining
(2017) text from social Big five model the presence or absence
networks, the of Big Five traits.
author's personality A new document
type is determined. modeling technique is
Pre-processing and proposed.
filtering of input data, The first layers of the
selection of features network process each
and classification are sentence of the text
carried out. Two types separately; then the
of features are used: a sentences are combined
fixed number into a document vector.
stylistic features at The method includes
the document level the following steps:
and word-by-word • Pre-processing:
semantic features sentence splitting, data
that are present cleaning and unification.
combined into a • Extraction of functions
variable-length input at the document level.
text representation. • Filtering.
• Extraction of functions
at the word level.
• Classification using
deep CNN. Its initial
layers process the text
hierarchically.
Di Xue Proposed a deep myPersonality with A method for
(2017) learning primarily Big five model recognizing the identity
based approach for of the Big Five
personality microblogs in a Chinese
recognition from text language environment is
posts. proposed with a new
machine learning
paradigm called label
distribution learning
(LDL). One hundred and
thirteen features are
extracted from the
profiles and microblogs
of 994 active Sina
Weibo users. Eight LDL
algorithms and nine
non-trivial conventional
machine learning
algorithms are applied
to train Big Five
personality trait
prediction models.
Experimental results
show that two of the
proposed LDL
approaches outperform
the others in predictive
ability, and the most
predictive one also
achieves relatively
higher performance
among all algorithms.
N. Alsadhan (2017) A way to predict Four types of The methodology is
personality from small corpora, based on the use of the
amounts of text was three of which are Python program, which
implemented marked was used to obtain
with Big Five traits of word matrices of the
personality, and one document. The sampling
marked with MBTI. was based on the
following approaches:
the 1,000 most common
words in each corpus
were selected; at least
40 repetitions of each
word were required.
Chaowei Li Focused on the Weibo users, social Correlation analysis and
(2017) characteristics of the data and principal
social network and questionnaire with Component analysis to
user behaviour, and Big five model select useful
established three information and then to
comparative analysis predict and analyze the
prediction models. results using the
multiple
regression model, the
gray prediction model
and the multitasking
model.
Laleh The LASSO algorithm myPersonality with R is used for
(2017) is used to Big five model implementation
choose the finest After standardizing the
characteristics and to data, model receives XT
predict big five rain, YT rain to train the
personality model. To find the
characteristics for optimized value of the
facebook users. hyper-parameter in
LASSO model, the cross
validation method has
been used.
Matej Gjurkovic Extracted a number of MBTI9k with MBTI A feature of the
(2018) language and user type technique is the use of
activity characteristics three different
and conducted a classifiers: SVM, 2-
preliminary MBTI regularized logistic
dimension regression (LR) and
analysis. three-layer.
Andriy Peleshchyshyn Modeling of Facebook users status Two components are
(2018) information influence Manual datasets included in the model to
in the social data describe network
environment of the characteristics from the
Internet is presented, point of view of state
namely the interest of security: user activity
current topics for log and social portrait.
users based on the The atomic components
analysis of user of the model are
activity and their reduced to
reaction to measurement values
publications. The and data that are
activity of users as suitable for direct
respondents was storage in a relational
studied and important database. A typical data
factors of the analysis model is provided in
of reactions to entity-relationship
publications were diagram format.
determined, on the
basis of which a
mathematical model
was built to
determine regularities
and predict the
impact on opinion in
the social
environment of the
Internet.
Serhii Holub The information Intelligent Monitoring Classification
(2020) technology of multi- System (IMS) technology
level intelligent Facebook data text messages in social
monitoring was used text messages of up networks
to classify text to one hundred
messages. The characters, selected
processes of by an expert
decomposition of text
messages of social
networks, adaptive
formation of
dictionaries of signs,
use of an agent
approach to the
construction of a
classifier and its use
for recognition of
certain groups of
persons are studied.
Оleksandr Morushko On the basis of Jung's Jung's basis The sociological analysis
(2020) "coordinate system", Facebook end method used in the
four pairs of linkedIn data works consists in
dichotomous signs text messages determining the
were used to photo content, psychological type using
determine the human selected by an expert visual signs obtained
psyche. The following from the photo content
methods of analysis of social network users,
were used: visual, or using word-markers
verbal. The algorithm of text messages that
for using these allow diagnosing Jung's
methods for forming dichotomous signs.
teams, assessing their
cohesion and
determining the
optimal leader is
described.
2.1.3. Future directions of research
The application in the practice of Internet communications of knowledge about the laws and
mechanisms of the functioning of the human psyche in different time periods, the use in online
marketing and online management of the achievements of psychological science and modern methods
of processing Internet content in combination with a personally oriented position and humanistic
orientation of methods and technologies, able to meet the needs of the individual and allow him to
maximize his potential.
The sources of information about the future behavior of an individual, which are the basis of
forecasting, should be the following components:
evaluation based on experience, analogies of ways of development of the projected object;
extrapolation of known trends;
a model of the state of the object in the future, based on taking into account the changes (desired
or expected) of those indicators, the prospects for the development of which are sufficiently known.
Forecasting methods provide scientifically based forecasts of the future, namely:
expert evaluations;
extrapolation;
modeling;
use of analogies.
In recent years, the problem of identifying a person using content in online social networks has
attracted great interest [32, 33, 34], but systematic research is just beginning. At the same time, the
greatest attention is paid to the problems related to the identification of an individual using the text
written by the user in social networks. In particular, most studies distinguish different types of features
extracted from textual data used in social media posts. Among the linguistic features, the following are
distinguished: LIWC - Linguistic query and number of words, speech acts, published tags, feelings [35].
Non-linguistic features for text identification are distinguished as follows: structural, behavioral,
temporal [35].
The relevance of studies that use consolidated data and various types of content generated in various
social networks to analyze user behavior is growing.
In our opinion, the promising directions of future research in the field of intellectual analysis of
content for the purpose of predicting personality in social networks may be:
study of the process of classifying the authors of messages with their psychological states;
detection of a person's psychological state based on his cognitive reflections of text messages
in social networks;
prediction of personality as a consequence of the application of governing influences;
forecast of the individual's condition based on the results of complex intellectual monitoring of
this individual.
3. Conclusions
The purpose of this article was to present an overview of scientific research in which the task of
identifying an individual in social networks is set, based on the analysis of user-written text and other
content, and to identify future directions for scientific interdisciplinary research on personality
prediction. As a result of our research, we have analyzed the various methods or techniques used to
predict personality. We reviewed the various studies conducted on social network profiles for the
purpose of automatic and expert identification of a person.We elaborated on the dataset and
methodology used for each study.
Thus, in the post-industrial, post-modern, and digital era, which is formed in the context of three
technological directions - high-hum (high humanitarian), high-tech (high technical), and high-sensory
(high sensory-technological), the psychological component of the individual in general and its behavior
in the virtual environment is of increasing interest to researchers. Professional interdisciplinary research
on personality prediction based on the analysis of various content generated in social networks becomes
the basis for the development of effective techniques that meet the needs of strategic areas of online
marketing and online management.
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