=Paper=
{{Paper
|id=Vol-2914/Paper1.pdf
|storemode=property
|title=Concept Cognitive Ontology-Based Model’s of Information Security of Social Networks
|pdfUrl=https://ceur-ws.org/Vol-2914/paper1.pdf
|volume=Vol-2914
|authors=Dmitriy V. Anashkin,Kostyantyn A. Malyshenko
}}
==Concept Cognitive Ontology-Based Model’s of Information Security of Social Networks==
https://ceur-ws.org/Vol-2914/paper1.pdf
Social Media Content Analysis with Machine
Learning Tools*
Dmitriy V. Anashkin1[0000-0002-0750-1217], Kostyantyn A. Malyshenko 1[0000-0002-3453-2836]
1V.I. Vernadsky Crimean Federal University, Simferopol, Russian Federation,
docofecon@mail.ru
Abstract. The article is devoted to the development of a computer program that
will allow researching the content of social networks using computer analysis
methods to predict the information response of users to a new publication and
visualize a user graph. The aim of the study is the implementation of an algorithm
that allows to improves the methods of automatic collection of information from
a social network. It is proposed to improve the methods of collecting information
in combination with computer learning tools, in particular, using neural networks.
The work consists of formulating and solving the problem of using classical ma-
chine learning methods to predict the sentiment of a user reaction to new news in
the form of quantitative markers. The work on the algorithm was divided into
two stages. At the first stage of the study, a software solution was implemented
that builds a graph of a social network user using the internal means of the com-
puter mathematics system "Wolfram Mathematica". The process of modeling the
local user network of individual communities is described. Further work carried
out is based on the interaction of two network development environments,
namely "VKAPI" and "Wolfram Cloud". The second stage of the research is writ-
ing a special software module for collecting publications from the news feed of
the VKontakte social network at a given request. The final stage of the second
stage is checking and evaluating the performance of the trained model on new
data.
Keywords: Machine Learning, Social Media, Computer Mathematics, Wolfram
Mathematica, Social Graph, API, Recommendation System.
1 Introduction
In the modern world, a colossal amount of information is generated every minute, be it
data from satellites, statistics of cellular operators, or news reports. Thanks to the de-
velopment of network communication protocols, humanity has the opportunity to
quickly exchange a large amount of information and content, which in the later stages
provoked the emergence of social interaction networks.
* Copyright 2021 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
2
Social networks continue to confidently penetrate more and more distant areas of the
everyday life of any person. Today there are no longer just group networks with a user
interface, but full-scale commercial projects that accumulate the experience of a wide
range of narrow areas. These areas include social engineering, marketing, PR (adver-
tising), online education. The modern social network is an interdisciplinary field. It
brings together mathematicians, IT specialists, economists, and sociologists. It is worth
noting that the results obtained by specialists from different disciplines quickly find
their application in the social network. This is possible due to the close relationship
between its components.
Now, not a single commercially successful project is complete without an additional
platform to attract a potential audience. Therefore, with the active integration of social
media into global processes, the need for a thorough analysis of endlessly flowing con-
tent is increasing. It is of great value for research, as it allows you to draw useful con-
clusions for making important decisions on related issues.
There are many areas of social media analysis. T.V. Batura [12] identifies four main
branches: structural, resource, normative and dynamic. Each of these areas requires
close attention to one or another component of the global structure of social networks.
The very process of analyzing social networks involves the use of specific methods
and tools for working with a variety of heterogeneous data. This problem has become
the impetus for the development of a new branch of science, which is directly involved
in the description of connections of different densities and intensities that have arisen
in the course of social interaction and communication.
The science of social network analysis (SNA) is characterized by the following de-
fining properties:
1. Embodying the idea of the importance of social connections.
2. Collecting data reflecting social connections.
3. Use of graphic images to visualize the results.
4. Use of mathematical, statistical, and computational models.
Modern research requires a powerful and promising environment for programming,
processing, and visualization of information objects. In such an environment, tools are
required to send queries to the social network to obtain accessible and structured anal-
ysis results.
The computer mathematics system “Wolfram Mathematica” fully complies with
these requirements. It is known as the most powerful computing system in the world,
along with well-known competing software packages such as Matlab, Maple, and
SciLab. Mathematica differs from its predecessors and competitors primarily in that it
serves as a single platform for immediate deployment and is an effective solution for
conducting research and computing work and interactive demonstration of scientific
results of publishing quality. The environment "Wolfram Mathematica" allows the user
to solve time-consuming tasks and understand the most complex concepts, using a large
arsenal of ready-made tools and packages for the development and implementation of
new functions.
3
In addition, the flexibility of the system and the ease of mastering the syntax of the
programming language makes it possible to use Wolfram technologies for educational
purposes. We cannot ignore the presence in the system of special means for interacting
with the API of most well-known social networks. For example, "Wolfram" offers the
user ready-made personal analytics for Facebook and Instagram in the form of his social
graph and diagrams reflecting user statistics (activity, friends, check-ins, etc.).
The purpose of the research is to write and implement an executable program in the
computer mathematical system "Wolfram Mathematica", which allows solving the
problem of using machine learning and computer analysis methods in the compilation
of predictive models trained on open information sources of social networks.
2 Building and Analyzing a Social Graph
2.1 Subject Area Overview
Since social media can be of different structures and sizes, there is no one way to rep-
resent them across all applications. However, the network model can be defined as
some object - a substitute for the original object, retaining the fundamental properties
of the network, but adapted for certain tasks. In this case, the original object is a social
network. The application of the models is because any social network with its internal
interconnections has a complex branching structure. As a rule, a network is a closed set
containing at least n≥2 elements that are connected by a certain type of connection.
Mathematically, the concept of a network is described using graph theory and its appli-
cations. Graph (G) is a mathematical object uniquely determined by three non-empty
sets (V, E, I), where V is the set of vertices of the graph, E is the set of graph edges, I
is the set of incidence relations.
Since social media can be of different structures and sizes, there is no one way to
represent them across all applications. However, the network model can be defined as
some object - a substitute for the original object, retaining the fundamental properties
of the network, but adapted for certain tasks. In this case, the original object is a social
network. The application of the models is because any social network with its internal
interconnections has a complex branching structure.
As a rule, a network is a closed set containing at least n≥2 elements that are con-
nected by a certain type of connection. Mathematically, the concept of a network is
described using graph theory and its applications. Graph (G) is a mathematical object
uniquely determined by three non-empty sets (V, E, I), where V is the set of vertices of
the graph, E is the set of graph edges, I is the set of incidence relations.
𝑉⊂𝑋↔ℕ
𝐺={ 𝐸 ⊂𝑉×𝑉 }
𝐼: ∀𝑡 ∈ 𝐸 → 𝐼 (𝑡) = {𝑥, 𝑦}
In modern computer sociology, the concept of a node in a graph is replaced by the
concept of an actor (user), respectively, the edges are considered as connections be-
tween these same actors. Note that actors are separate individual, corporate, or collec-
tive social units. Thus, a new concept is brought up for consideration a social graph.
4
A social graph is a kind of graph, the nodes of which are represented by social objects
(user profiles with various attributes, communities, media content), and the edges are
represented by social connections between them. This type of network, such as an im-
plicit social graph, has become widespread in practice. This is a graph that does not
indicate the explicit social connections of the actor, but it can be deduced by consider-
ing the interactions between the user and the social unit (group, friend).
The foundations of social graph analysis were laid back in 1994 in the book Social
Network Analysis: Methods and Applications by S. Wasserman and K. Faust [10]. The
principles outlined in this book are used in network analytics to this day.
Let's present a basic list of graph network models that can be found in the literature
today:
– Real-world networks. One of the main characteristics inherent in this type of net-
work is the fact that connections between nodes depend on a certain probability. In real
networks, such as social networks, this means that the likelihood of communication
between two nodes is higher if there is any relationship between them (friendly, pro-
fessional). However, social networks differ from other types of networks, mainly be-
cause they are easier to divide into communities. Consequently, this characteristic af-
fects the distribution of degrees, usually with a positive correlation of degrees and a
high level of clustering.
– Random networks. From a theoretical point of view, random networks, best known
as random graphs, can be considered the intersection of graph theory and probability
theory. Any random graph can be described as a probability distribution or as a random
process that will be used to create connections between any pair of nodes in the graph.
Therefore, to generate a random graph, it is only necessary to randomly add (following
a predetermined probability distribution) consecutive edges between any pair of nodes.
Random graphs are commonly used to compare the structure and properties of any
graph.
– Small world networks. Many real-world networks have two main properties: the
first is related to the distance between two nodes (which is usually small), while the
second is related to the transitivity or clustering factor of these networks (which is usu-
ally relatively high). However, when real networks, such as those associated with social
networks, are analyzed, it is quite common to find one property that does not appear in
random graphs. This property is related to the degree of transitivity, which in these real
networks has a higher value than in random graphs. This property appears due to the
social behavior of the network (usually the user's friends are likely to be friends too),
which means that the transitivity on the graph or the value of the clustering coefficient
will have more importance on social networks.
– Scale-free networks. This type of network is usually defined based on its power
distribution. In these networks, the degree of distribution of nodes will follow the
strength of the law (at least asymptotically). That is, the fraction P (k) of nodes in the
network that have k connections to other nodes goes to large values of k as P (k) ∼ k -
γ, where γ is a parameter whose value is usually in the range 2 <γ <3, although some-
times it can go beyond these boundaries.
5
As practice shows, visualization is one of the advantages of using graphs for analyz-
ing social networks. There are three methods for visualizing such graphs: the "planari-
zation" method, the "orientation" method, and the "directed forces" method. The es-
sence of the "planarization" method is that if a graph is not planar (that is, it has inter-
sections of edges in its two-dimensional projection), it is artificially made by it. To do
this, artificial "pseudo" -verts are placed at the intersection points of the edges, and then
one of the methods for constructing a planar graph can be applied to the graph. The
"orientation" method is based on a similar principle: an undirected graph is artificially
transformed into a directed one, after which one of the directed graph visualization
methods can be applied to the graph.
Thus, the choice of a method for visualizing a social graph will significantly affect
its metrics (density, division into clusters) and informative value.
2.2 Integration of Wolfram Mathematica and Social Network API
To get started, it is necessary to integrate the computer mathematical system and the
program interface of the social network. The authors decided to use the API of the
Russian-language social network "VKontakte" for the work. VKAPI has some ad-
vantages, thanks to which it is possible to carry out complex work with open data of a
social network while saving time on various intermediate operations (for example, in-
stalling and configuring additional software). So, connecting and using the VKontakte
API is not difficult for any registered user of this social network. In addition, the API
query format is relatively simple and scalable to provide detailed data useful for re-
search.
The first stage of the VKontakte API integration is the creation of a web application
that processes the received requests to send them to the server. After filling out the
form, the user is prompted to select the type of application to create. For the full and
convenient use of all the capabilities of the VKAPI program interface, the type of the
created application "Standalone" was chosen. Next, you need to get a unique client
access key, which will later be used to generate a request to the social network interface.
The access token is provided to the user through the OAuth2 proprietary authorization
protocol. To do this, a link is generated in the address bar of the browser used, contain-
ing information about the newly created application and a request to grant the necessary
rights to work with the server's functionality. The rights "wall", "friends" and "offline"
were obtained for the conducted research.
The responses returned by the server to the client request are in JSON (JavaScript
Object Notation) format. It is a cross-platform textual data exchange format that has a
nested structure and uses the key: value construction as the main syntax. This format is
especially convenient for presenting lists and compiling data samples based on prede-
fined features.
After receiving a unique key (token), you can proceed to the second stage of inte-
gration of the platforms used. To do this, a token variable is declared in the Wolfram
Mathematica notebook (in the format of a text string) and equated to the value of the
received client access key (access_token). We implement a function that allows you to
work with a repository of methods stored on the server (Fig. 1).
6
Fig. 1. Wolfram Mathematica code that makes a request to the VKontakte social network API.
Wolfram Mathematica has a set of tools that allow you to make POST / GET requests
and process JSON files, formatting the return values of these files into rules of the form
Rule [{1} → {2}]. Characters that appear as pattern names on the left are treated as a
local rule. Any character or expression on the right side of the rule is considered as its
condition. Mathematica allows you to combine data types within the same list in a sim-
ple enumeration. This turns out to be extremely convenient when processing data re-
ceived from a social network.
3 Social Graph Programming
Obtaining information about a user of a social network, his profile data is carried out
by similar means. Let's say you need to get a photo of the user, his name, and surname.
For this, the users.get method will be used with the user.id option passed in. Next, we
list the fields whose values we want to get using this method. We need fields "photo",
"first_name", "last_name". To represent the values returned in JSON format as a list,
let's write a function that clears the response from the "key" parameter (Fig. 2).
Fig. 2. A function that collects additional information about a user using his online identifier.
The program module contained in the body of the function accepts as input the condi-
tions specified in the form of boolean variables (true, false). By default, the Photo var-
iable is set to false, which means that when requesting data from the API, the value of
this field will not be returned. Further, the "VKInfo" option will be used when "draw-
ing" a social graph to replace its vertices with photos from the profiles of the corre-
sponding friends.
7
The main characterizing motive for finding a user in a social network is his interac-
tion with friends or social groups of different sizes. Therefore, to reflect a visual picture
of the user's social circle, it is necessary to get a list of his friends. In VKAPI, this
function is called "friends.get". The response of the parser (query tool) is a list of user
identifiers that are in a network "friendship" with the element being examined.
Thus, based on the above remarks, let us single out the general algorithm for con-
structing a social graph:
1. Formation of the study group.
2. Interaction with the provider of the social network (data parsing).
3. Getting a list of the user's friends.
4. Formation of an egocentric graph.
5. Finding common friends of the researched user and users in the circle of inter-
action.
6. Building a communication graph.
7. Combining the communication graph and the egocentric graph.
8. Clearing the graph from side elements (blocked, deleted users, users who have
denied access to the profile).
9. The choice of laying the network. Selecting the graph clicks and dividing it
into clusters.
Research into egocentric (personified) networks is limited to the study of the social
connections of one individual. This approach provides representative examples of the
social environment of individual elements and is compatible with statistical generaliza-
tion methods for large networks. Thus, the ability to calculate the most likely environ-
ment for a particular social unit increases.
4 Research of a Social Network Using Artificial Intelligence
Methods
In recent years of active development of science, many new research opportunities have
appeared. Increasingly, the use of the latest developments and algorithms of machine
vision is observed when performing various tasks. Such a direction of computer science
as artificial intelligence has managed to exert a strong influence on scientific research
and has become a prerequisite for the emergence of a new tool for specialists in the
field of data analysis [5]. But applying machine learning requires a persistent source of
data. Currently, the popularity of social media is growing and it is they that can act as
a regularly updated data bank for scientists and analysts.
Data that represents information about a user on social networks can be schemati-
cally divided into 4 target types: profile, behavior, text content, and visit monitoring
data. To extract the features of the model, it is enough to assess the relative influence
of the feature, reliability, and generalization of the potential for classification.
8
Some social media data is easy to sort, classify, and structure (these include forms,
historical data), but the bulk of the content is unstructured and requires more careful
analysis. Also, when collecting data, you can face the following problems: limited ac-
cess or blocking for automatic collection of information, data privacy - the user has the
right to set privacy settings, which makes many profiles attributes inaccessible from the
outside, poor data structure - some social networks either do not provide an API or
install restrictions on working with it, which makes it impossible to use it by third-party
programs.
The data required for machine representation is obtained in several stages. First, a
corpus of texts for research is collected, which are pre-cleared of special characters to
avoid errors in the operation of the machine algorithm.
In the social network "VKontakte" for the study, thematic publications were selected
that correspond to the user's request. For the sample, publications were used with the
keyword "Eurovision Song Contest". For this, the API method "newsfeed.search" was
used, which searches for publications in the database. The data was received in JSON
format using an algorithm that works up to n≤200 publications, where 200 is the max-
imum value returned by the function, as stated in the official documentation, and was
set with the keys "text" (publication text), "id" (record identifier), "Owner_id" (identi-
fier of the user or group that published the news). Then the results were split into two
fixed tables. The first table consisted of the texts of records in Russian and was a corpus.
The second nx2 table mapped the publication to a pair of "id" and "owner_id" values.
Further work with the corpus assumed its literal translation into English. Further,
each element of the corpus was divided into a list consisting of all the words found in
the text.
The paired values "like" and "comment" was obtained after sending the second table
to the API system and processed by the methods "likes.getList" and "wall.getCom-
ments", respectively.
The authors of this work, through the internal functions of "Wolfram Mathematica",
implemented a multilayer neural network (multilayer perceptron), which predicts user
reactions based on transformations of vectors of fixed dimensions.
MLP (Multi-Layer Perceptron) network consisted of three functional layers:
«Embedding Layer» – embedding layer. It stores the words of all words in the dic-
tionary. A dictionary is a matrix consisting of words and their vector representations,
while words that are close in semantic meaning have a close location in the R n-dimen-
sional space. The dimension Rn is a tunable parameter, and the larger n, the higher the
stability of the dictionary and the better the model.
A ready-made model stored on the Wolfram Neural Net Repository under the name
GloVe 50 Dimensional Word Vectors Trained on Wikipedia and Gigaword 5 Data was
used like a dictionary. This model was released in 2014 by the Computer Science De-
partment of Stanford University and was trained using an original method called Global
Vectors (GloVe). It encodes 400,000 tokens as unique vectors in a 50-dimensional
space, with all tokens outside the dictionary being encoded as a null vector.
«Recurrent Layer» – a layer with directed links between elements. The output of the
neuron in this layer can be fed back to the input. This structure allows you to create a
9
structural semblance of "memory" and sequentially process incoming data for their sub-
sequent storage. The recurrent layer allowed us to process the sequence of vectors in-
cluded in the proposal to build a vector for the entire publication, which helped to form
a dataset for training a linear layer with R2 - directional vector at the output and R50 at
the input.
«Sequence Last Layer» – the last layer of the network. Used to convert a sequence
of token vectors into a sentence vector based on the semantic arrangement of words in
the dictionary. The advantage of this layer is that if a word from the text is absent in the
dictionary, then this is compensated by the transformation.
The last step in building a neural network model was adding a Linear Layer, trained
on vectors of submitted publications and pairs of values from the table (like a com-
ment). In this case, the output value was a vector of dimension R ^ 2, which describes
the number of user tags (like and comment, respectively) under the newly published
post.
The parameters of the neural network implemented in the Wolfram Mathematica
language are shown in Fig. 3. A detailed scheme of the algorithm that predicts user
reaction to new news is shown in Fig. 4.
Fig. 3. Parameters of a neural network that converts text into a 50-dimensional vector.
The objective function of the neural network, implemented by the authors, was op-
timized using the "Adam" (Adaptive moment estimation) method. It combines the idea
of motion accumulation and weaker renewal of weights for typical signs.
10
Embedding Layer. New data
A layer to convert each word to a (English
50-dimensional vector. Pre-la- text)
beled correspondence dictionary.
Subject re-
quest.
Recurrent Layer
Layer for converting word vec- Forecast
Form encoding
URL. tors to 50-dimensional text vector.
Trained neural
"Bag of words" network
Algorithm oper-
ation newsfeed.
search
Training.
Automated translation
(English).
𝑛 ≤ 200
publications on
the topic in the Association
format JSON. (50-dimensional
Key text. vector - 2-dimen-
sional vector).
Algorithm operation
likes.getList. Key count.
Table 𝑛 × 2. Table n × 2. A pair of
Key id, like values, a com-
owner_id ment on the text 𝑛.
Algorithm operation
wall.getComments.
Key count.
Fig. 4. Diagram illustrating a neural network prediction algorithm for social network content.
The presented algorithm was chosen due to its clear advantages: it is simple to im-
plement, computationally efficient, and not memory-intensive. In addition, it is well
suited for tasks that involve working with large amounts of data. In practice, this algo-
rithm is currently used by default and has clear advantages over "SMSProp".
11
Thus, the above-described neural network construction method can be used to pre-
dict user marks under a newly published news item. To predict the number of "I like"
marks and the number of comments, the text of publications on a given topic is vector-
ized, after which it is converted into a vector of dimension (R50) R50. Next, a training
sample is compiled, consisting of text vectors and the corresponding publication eval-
uation vectors of dimension R2. New data, on which it is required to make a forecast, is
submitted in the form of vectorized text, after which a prediction can be obtained on
the output layer in the form of a pair of values "like" and "comment".
5 Discussion
The study of network interactions of users, as well as the information that is trans-
mitted between them, is an important task of modern information technology.
The rapid development of means of semantic processing of natural language has
become the impetus for the development of a new wave of technological innovations
that are successfully implemented in human everyday life. Voice assistants, automatic
text recognition systems are now indispensable attributes of many digital systems and
devices. The dynamics of the development of natural language processing systems have
been repeatedly considered in scientific works. The authors of publications [6, 7] de-
scribe the dynamic properties of such systems and emphasize the importance of the
consistent application of algorithms to achieve the stated result.
Computer analysis allows scattered data to be adapted for global use. This oppor-
tunity has become feasible thanks to the development of mathematical and program-
mable systems that present visual data in a visual form. The authors of the work set
their particular task in the development of an algorithm for visualizing the graph of a
social network user. Special literature was studied [1, 8], which made it possible to
actualize the main issues of the problem being solved and come close to its direct im-
plementation.
The authors of this article in two stages obtained two software models that allow
working with the API of the social network. A similar approach to the collection and
use of information from social networks has already been considered by the authors of
works [1, 2]. However, the fundamental difference between this study and the previous
ones lies in the fact that an integrated approach to the implementation of software so-
lutions has been produced. Also, software was used to carry out the work, the use of
which required the preparation of a complex algorithmic code.
6 Conclusions
Based on the open information of the social network "Vkontakte", as well as the
available search algorithms and information objects in it, programmable solutions were
implemented that allow operating the information received at a high level.
The result of the first stage of the research was a software solution that builds a graph
of a user of a social network using the internal means of the computer mathematics
12
system "Wolfram Mathematica". The second stage of the study made it possible to im-
plement a software module for collecting publications from the news feed of the VKon-
takte social network at a given request.
In the future, it is planned to refine and improve the presented models for their pos-
sible real implementation. Also, the development vector on the topic under considera-
tion is planned to present a software package that, based on feed-forward neural net-
works, will automatically generate news summaries based on information collected
from the social network using the methods presented in the article.
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