=Paper= {{Paper |id=Vol-2392/paper3 |storemode=property |title=Method of Improving the Information Security of Virtual Communities in Social Networking Services |pdfUrl=https://ceur-ws.org/Vol-2392/paper3.pdf |volume=Vol-2392 |authors=Ruslan Hryshchuk,Kateryna Molodetska,Yuriy Syerov |dblpUrl=https://dblp.org/rec/conf/coapsn/HryshchukMS19 }} ==Method of Improving the Information Security of Virtual Communities in Social Networking Services== https://ceur-ws.org/Vol-2392/paper3.pdf
Method of Improving the Information Security of Virtual
     Communities in Social Networking Services

      Ruslan Hryshchuk1[0000-0001-9985-8477], Kateryna Molodetska2 [0000-0001-9864-2463],
                         and Yuriy Syerov3[0000-0002-5293-4791]
  1
    Head of the Information Protection and Cybersecurity Department of Korolyov Zhytomyr
                             Military Institute, Zhytomyr, Ukraine
2
  Head of educational and scientific center of IT, Zhytomyr National Agroecological University,
                                       Zhytomyr, Ukraine
                     3
                       Lviv Polytechnic National University, Lviv, Ukraine

  dr.hry@i.ua1, kmolodetska@gmail.com2, yurii.o.sierov@lpnu.ua3



       Abstract. Today, social networking services have become one of the most
       powerful means of mass communication between their users – actors. Social
       networking services are increasingly acting not only as a mean for the content
       exchange in the virtual environment, but also as a mean for self-organization of
       citizens in their real life. Therefore, on the one hand, social networking services
       are an objective reality of modern life, on the other hand, as the experience of
       many countries of the world and, first of all, Ukraine shows, is a source of
       threats to its information security. For example, the result of unfriendly infor-
       mation operations in social networking services is the manipulation of public
       opinion, the spread of appeals that accentuate international, interreligious and
       interethnic conflicts, provoke terrorism, separatism and other manifestations of
       violence and crimes against humanity, peace and security. The article reveals
       one of the methods of countering such threats. The core method is to increase
       the information stability of virtual communities in social networking services to
       destructive information influences through their hidden artificially controlled
       formation. The formation of information resistant virtual communities is sug-
       gested to be carried out on the basis of the critical mass principle. This ap-
       proach, firstly, provides their further stable development (artificially controlled
       hidden increase in the number of participants) and, secondly, guarantees critical
       perception of the content of destructive matter. In addition, for those virtual
       communities that have unsatisfactory quality performance indicators, it is of-
       fered to use latent synergistic management in the form of directed information
       influence. The accuracy of the developed method has been proved experimen-
       tally. As a result of the study, it is found that the information stability of virtual
       communities in social networking services at the time of using the suggested
       method is higher in comparison with the natural uncontrolled processes of their
       creation.

       Keywords: social networking service, virtual community, actor, information
       security, method, information stability, synergetic management, critical mass.
1      Introduction

Despite the diversification of communication channels, social Internet services
(SNSs) have become the most popular segment of mass communication. At present,
SNSs not only consolidate the tools for messaging and multimedia content, but also
apply for the self-organization of society into virtual communities and the realization
of interactions in real life. The growing popularity of SNSs is inextricably linked with
the emergence of State Information Security (SIS) challenges in the information space
of virtual communities. So that, SNSs are actively used by the Russian Federation to
conduct a hybrid war against Ukraine. Such a form of confrontation is carried out by
combining technologies of cyber impact on critical infrastructure objects and informa-
tional impact on citizens in the information space, in particular, in SNSs. The purpose
of performing the information influence on the SNSs actors by the opposing party is
to manipulate the personality, group of people and masses, disseminate misinfor-
mation to influence social and political processes in the state, spread chaos among the
population, etc.
According to data [1] in 2018, the US Department of Justice has accused a Russian
citizen of attempting to interfere with the US political system, in the midterm elec-
tions that has taken place in November. Such an intervention has been carried out
using SNSs to discuss such issues as immigration, control and possession of weapons,
racial relations, LGBT and women’s rights march. The attackers have appeared to be
American political activists, creating thousands of fake accounts in SNSs. The objec-
tives of such actions have been polarization and the spread of antagonism in Ameri-
can society based on social and inter-racial contradictions, erosion of democracy and
limiting the rights and freedoms of citizens.
Taking into account the growing level of threats to the national security of the world’s
leading countries as a result of the destructive influence of threats to information se-
curity, the European Parliament has accepted a resolution with recommendations to
the EU Council and EU diplomacy focused on countering the propaganda “from Rus-
sia’s side and other hostile actors” [2]. The European Parliament has recommended
“to raise awareness about Russia’s disinformation campaigns,” which, according to
the parliamentarian, is “the main source of misinformation in Europe.” The European
Parliament offers to develop a “legal framework, both at the union level and interna-
tionally, to counter hybrid threats, cyberwar and information war” [2].
Therefore, there is an objective contradiction between the problem of the practice of
ensuring the sustainable development of the SNSs information space in the conditions
of its globalization, and the increasing number of threats to the SNSs and the problem
of science in developing effective approaches to counteract such threats, which guar-
antee a reduction of the level of destructive information influence on the SNSs actors.


2      Literature Review

Numerous studies of the specific use of SNSs actors for communicating on the Inter-
net [3-6] define them as a tool for the formation of a dynamic information space. In
turn, SNSs provide actors with the means of representing their identity in the virtual
space and the tools for creating different types of relationships with other actors
(friends, followers, members of virtual communities, etc.). Among the basic SNSs
functions, the following publications are highlighted [6]: management of the identifi-
cation of actors to meet the needs for referring to a selected category of users and
setting access rights to the profile; search of experts by means of proactive recom-
mendation of relevant SNSs actors based on the given criteria - surname, interests,
sphere of activity, etc.; contact management, which involves the functionality of in-
teraction with a certain set of actors, in particular limiting access to the profile, desig-
nating actors in posts and content of different types; awareness of the actors’ activity
in SNSs includes tracking content publications, events in the lives of actors, events,
participation in virtual communities; exchange of content between actors in SNSs
with the use of direct and indirect means. While the direct exchange, there is a com-
munication with the addressing of the content to the specifically defined actor. Indi-
rect exchange includes the publication of photo and video content, the creation of
public events and postings.
It is known that structurally SNSs represent a network of crosspoints – actors, which
are linked by relationships – connections. Considering the peculiarities of the pro-
cesses of interaction between actors, it is appropriate to describe SNSs as a complex
nonlinear dynamic system [7, 8]. In the case of attack to the SIS in SNSs, the interac-
tion of actors goes into chaotic dynamics, it becomes unpredictable and uncontrolled
not only in the virtual space, but also in the real life of a society [9, 10]. The publica-
tions [11-13] show that the effective direction of threats’ counteraction to the SIS in
SNSs is the use of synergistic management. The main point of such management is to
spread the content of directed matter in the information space of SNSs, and as a result
of this influence, the processes of self-organizing actors happen. As a result, the indi-
cators of the actors’ interaction in SNSs, in particular the demand for content of de-
structive matter, reach the desired values, and the virtual community moves to a stable
predetermined state of the information security. However, such studies are aimed at
the SIS operational counteraction of threats in the information space of SNSs under
the dynamic confrontation conditions. The task of forming the structures of actors’
virtual communities, that are resistant to destructive information influences in SNSs,
remains unresolved. This approach will allow to reduce the cost of resources for sta-
ble monitoring of SNSs information space and counteraction of threats to the SIS.
The analysis of academic research in the direction of artificially managed synthesis of
stable structures of virtual communities in SNSs has shown that this issue has not
been studied sufficiently [14-16]. The explicit direction of research is inextricably
linked with the use of the virtual community’s startup approach in SNSs. It is estab-
lished that to the basic conditions for a successful startup of the virtual community in
SNSs, which can be considered sufficient, it is appropriate to refer the following [17,
18]: the correct community’s preparation for promotion; maintaining a stable number
of actors who enter the community for a specified period of time; obligatory retention
of the 10% barrier of the carriers’ ideas of the virtual community; timely placement of
high-quality content; the choice of rational ways of distributing content about the
community, etc. At the same time, the success of the startup depends on whether the
virtual community gains a sufficient number of actors, that is, such a critical mass,
which will be sufficient for its further self-development. Therefore, this condition can
be attributed to the category of necessary ones, compliance of which is obligatory for
the startup of all virtual communities in SNSs.
Also, from the literature analysis on the study’s topic [19-23] it has been established
that the problem of the virtual community’s startup in SNSs on the critical mass prin-
ciple has not been worked out either theoretically or methodically until today. The
vast majority of studies [7, 20-22], etc., focus on mastering the issues of developing,
researching and analyzing the SNSs models. Also, in a number of scientific publica-
tions [3-6] and others, attention is focused on the study of issues related to the social
and humanitarian role of SNSs in the life of civil society.
Thus, the task of increasing the information stability of virtual communities in SNSs
to destructive information influence by combining the approaches of the virtual com-
munity’s startup in SNSs and the synergistic management of interaction between ac-
tors is a burning strand of the research.


3      Materials and Methods

3.1    Virtual Communities as Complex Dynamic Systems
Virtual communities in SNSs represent an association of actors that interact with each
other to implement interpersonal and group communication in the information space
[25]. As a result of such interaction, virtual community actors in SNSs create their
own informational content. In turn, structurally, virtual communities in SNSs consist
of a large number of actors and is a complex dynamic system with vertical and hori-
zontal links between them. Therefore, in Fig. [1] we demonstrate the virtual commu-
nity in SNSs as an unoriented graph.




Fig. 1. The block diagram of the virtual community in SNSs
In fig. [1] actors in SNSs are marked with circles, and the interrelationships between
them – with three types of lines: the solid line is used to represent stable relationships
that ensure the cooperation of information interaction under the influence of external
factors of the information space and remain unchanged (for example, friendship be-
tween actors); solid lines with arrows at the end point to flexible links between actors
that are capable of responding instantly to changes in the information space parame-
ters and are usually unidirectional (followers, etc.); weak links are marked by a dash-
and-dot line and are characterized by significantly limited information interaction due
to an inadequate level of common interests between actors.
Also, the figure shows two types of actors in SNSs. The first group of actors is repre-
sented by a solid circle – they are part of the virtual communities and interact with
other actors in them; the second group is marked by a dual circle – these users do not
belong to virtual communities, but they can monitor their activity. As a result of the
attack to the SIS in SNSs, the interaction of actors becomes unregulated and uncon-
trolled, which leads to the emergence of chaotic dynamics. Signs of chaotic dynamics
of actors’ interaction processes in SNSs are:

 appearance of high sensitivity to the initial conditions that characterize the infor-
  mation space;
 fractal motion marks of the depicting point in the system’s phase space;
 the increasing complexity of establishing links between actors and virtual commu-
  nities as a result of changing some of the parameters of the SNSs information
  space;
 the transitional chaotic reactions of actors and virtual communities to distributed
  content, non-periodic bursting of the content publication of destructive matter, or
  the irregularity of the distribution of such content from the moment it is thrown in-
  to the information space.

Therefore, the functioning peculiarities of virtual communities’ actors in SNSs in the
conditions of carrying out destructive information influence cause a conceptual prob-
lem, the solution of which is aimed at reducing the level of information entropy in the
system in order to stabilize the actors’ public opinion, increase their level of critical
perception of misinformation and, finally, regularization of interconnections between
members of the community and the decisions they make.


3.2    Information Stability of Virtual Communities in SNSs

It is known that in the broad sense, the immunity of social communities is the ability
to use their own resources to respond, to counteract and to continue functioning under
the influence of negative factors [25]. The publications [7, 9, 11, 12] have proved that
as a result of the attack to the SIS in SNSs, not only the promotion of content of de-
structive matter in the information space happens, but also the change in the relation-
ship between actors and, as a consequence, structures of virtual communities.
Therefore, under the information stability of the virtual community in SNSs, it is sug-
gested to understand its ability to respond and to recover from the impact of threats to
information security, to adapt to changes in the information space and to realize their
purpose of functioning. The category under consideration differs from the information
security concept of the virtual community considering the requirement for its sustain-
able development in the SNSs environment. Under the conditions of information con-
frontation with the use of SNSs, intruders use innovative technologies of information
influence on actors and, due to technological advantages and element of surprise, can
successfully achieve their objectives. At the same time, assisting the information se-
curity of virtual communities involves activities aimed at excluding the possibility of
implementing external and internal threats in the SNSs information space [9]. Conse-
quently, the achievement of the information stability of the virtual community in
SNSs provides preliminary readiness, adequate response and successful recovery to
the given state of the virtual community’s information security after the SNSs threat.
Factors that affect the information stability of virtual communities in SNSs are pre-
sented in Fig. [2].




Fig. 2. Components of the information stability of the virtual community

The achievement of the SIS in SNSs is possible as a result of the realization of an
effective state policy in the information sphere in accordance with the current legisla-
tion, the use of a complex measures aimed at meeting the contemporary challenges in
the information space and the means of controlling them. The guarantee of the SIS in
SNSs is primarily the responsibility of the relevant departments, which are deter-
mined by the current legislation of the certain state. In particular, the national interests
of Ukraine in the information sphere, the threats to their implementation, the direc-
tions and priorities of state policy in the information sphere are determined by the
Doctrine of Information Security [26].
Social norms in SNSs are represented by a set of generally accepted rules, standards,
relations between actors that provide the sequences of the virtual community, the
stability of the processes of interaction between actors in the context of attacking the
SIS [27]. Social sanctions in SNSs are a set of punishments in the legal field of the
state and the SNSs environment, which are used in the case of detecting deviations
from the generally accepted norms of interaction between actors and virtual commu-
nities in order to meet the respected community standards in the information space of
SNSs [27]. According to the work of Michael Scriven & Richard Paul [28], critical
thinking of actors in SNSs is an intellectual process for the active reflection, applica-
tion, analysis, synthesis and / or evaluation of content collected, or generated through
observation, experience, reflection, reasoning or interaction with other actors, which
prompts actions in the information space of SNSs.


3.3    The method of forming the informationally stable virtual communities

We envisage the formation of a stable virtual community using an approach based on
the virtual communities’ startup in SNSs. Considering the results of recent scientific
studies [3-7] and experimental using [16, 22, 23], as well as the basic notions of nu-
clear physics, we give the following definitions of the critical mass of the virtual
community in SNSs and its start-up:

 the critical mass M of the virtual community c  C in SNSs g  G is the mini-
  mum number of actors amin  A that are consuming and generating new content
   k  K , which provides the activation of the viral (virus) loop v and self-
  development e of the virtual community;
 the virtual community startup in SNSs is a newly created virtual community c  C
  in SNSs g  G , which twists under severe resource limitations omin  O ;
 viral loop v means the speed of content distribution between actors a  A of the
  virtual community c  C in SNSs g  G ;
 the critical state of the virtual community is the steady state of the virtual commu-
  nity c  C in SNSs g  G , in which the number of actors aconst  A does not
  change in time t  T ;
 criticality means the conditions under which the virtual community c  C in SNSs
   g  G supports the mechanism of self-development e .

Taking into account the given definitions it can be confirmed that the problem of the
virtual communities’ startup in SNSs occurs when there is a need for rapid activation
of the loop, which will ensure the self-development of the community under the con-
ditions of severe resource limitations. But in practice, the solution of this problem
involves solving the contradiction, which is to meet the needs of adjustment the high
requirements that are put forward to the pace of the loop activation of the newly cre-
ated virtual community with the involvement of a minimum number of actors, to the
severe resource limitations that are set. So that, solving the discovered contradiction is
suggested on the basis of the critical mass principle.
Since today there is no common approach for determining the critical mass for the
startup of virtual communities in SNSs, at a first approximation the principle of criti-
cal mass in the formalized form can be described as following

                             min  M  : a  amin , o  omin ,                                (1)

where amin means the minimum number of actors required to provide a successful
startup of the virtual community c in SNSs g , amin  A , c  C , g  G ; omin de-
scribes the minimum cost of resources that is enough for a successful startup of the
virtual community c in the social network g , omin  O .
In the direct formulation, the task of determining the critical mass of the virtual com-
munity (1) is incorrect [29]. We regularize the principle (1). To do this, we use the
metric of self-similarity – the Hurst index [30, 31]. Unlike the well-known metrics of
self-similarity, the Hurst index will provide not only the trendsetting in the sequence
of filling the virtual community with new actors, but also establish the nature of the
startup. In such a way, the value of the Hurst index H is going indirectly to answer
the question whether the virtual community of critical mass min  M  has reached a
minimum number of actors amin and dedicated resources omin (1) or not, that is
min  M   H . In Table [1], qualitative signs of the startup are defined by the quanti-
tative values of the Hurst index.

Table 1. The balance between the critical mass of the virtual community and the values of the
Hurst index while evaluating the quality of the virtual community startup
                                          Category of startup quality on the critical mass
   Resource assigned to a startup,     principle min  M  , depending on the change in the
             omin , days                         values of the Hurst index, H
                                        unsuccessful       accidental        successful
                  15                      0–0.330         0.331–0.668         0.669–1
                  20                      0–0.337         0.338–0.662         0.663–1
                  25                      0–0.342         0.343–0.657         0.658–1
                  30                      0–0.347         0.348–0.652         0.653–1
                  35                      0–0.351         0.352–0.647         0.648–1
                  40                      0–0.354         0.355–0.645         0.646–1
                  45                      0–0.356         0.357–0.643         0.644–1
                  50                      0–0.359         0.360–0.639         0.640–1
                  55                      0–0.362         0.363–0.637         0.638–1
                  60                      0–0.364         0.365–0.634         0.635–1
                  65                      0–0.366         0.367–0.633         0.634–1
                  70                      0–0.367         0.368–0.630         0.631–1
                  75                      0–0.368         0.369–0.629         0.630–1
                  80                      0–0.371         0.372–0.628         0.629–1
                  85                      0–0.372         0.373–0.627         0.628–1
                  90                      0–0.373         0.374–0.626         0.627–1
                  95                      0–0.374         0.375–0.624         0.625–1
                 100                      0–0.376         0.377–0.623         0.624–1
The calculation of Hurst indexes, which will determine the quality category of the
virtual community startup in the social network, is performed according to the expres-
sion

                                       lg  R S 
                                 H                    ,                             (2)
                                      lg  m   2 

where H is the Hurst index; S means average deviation of a number of observa-
tions; R is the dispersion of accumulated deviation; m is the number of observations.
Consequently, adherence to the critical mass principle provides the stable develop-
ment of the virtual communities’ dynamics, which in the future excludes the reduction
of the number of their actors, provides the stability of interaction relations between
them, the ability to perform functions of social control. Thus, the startup of virtual
communities in SNSs based on the critical mass principle serves as a guarantee of
increasing their level of information stability.
For a case when the virtual community startup in SNSs according to the data in tab. 1
is characterized by qualitative indicators “unsuccessful” or “accidental”, it is not ca-
pable of independent functioning and development. Achievement of a given level of
information stability by such virtual community is possible only with the constant
hidden information influence on the actors in SNSs. Such security actions require the
use of significant resources – financial, human and technical, etc. Therefore, we ana-
lyze the process of forming a virtual community of actors, which will be capable of
stable self-development by activating the loop in SNSs using synergistic management
[9].
Considering the high speed of growing the number of actors and virtual communities
in SNSs, the origin of evolutionary processes within the virtual communities, the
intensity of information exchange between SNSs and the external environment of its
functioning – the national and world information spaces, we use the model of micro-
biological system for the description of the actors’ interaction in SNSs [32]. At the
same time, the publication of the content of given matter is carried out by members of
the virtual community team in SNSs, acting as its administrators.
Therefore, we formalize the processes of interaction between actors of the virtual
community in SNSs in the form of the Mono model [13, 32, 33], which is a system of
ordinary differential equations

                           dx  t 
                                       Q  x  Dx,
                           dt
                                                                                    (3)
                           dQ  t   DQ   Q x  DQ;
                                                     
                           dt
                                            0



where x  t  stands the number of actors in the created virtual community; Q  t  de-
scribes a part of team members’ publications in the virtual community; Q0 means a
part of new publications on a given topic in the virtual community, published by team
members; D indicates the speed of new members’ appearing of the virtual communi-
ty in the absence of artificial influence on actors;  1 indicates the coefficient, which
specifies a part of publications on a given topic, which has influenced the formation
of public opinion of the virtual community actors;   Q  x means the increase in the
number of actors in the virtual community due to the impact of published content by
                          Q
team members,   Q   z        ;  Dx stands the reducing the number of virtual
                         kz  q
community actors;   Q  x stands the number of publications that has influenced
the formation of public opinion of virtual community actors; DQ0 describes the inten-
sity of publications that has caused interest in actors of SNSs and has encouraged
them to become participants in the virtual community; DQ means the intensity of
publications that hasn’t caused interest in actors of SNSs and hasn’t encouraged them
to become participants in the virtual community.
Limitation 1. The growth rate of a virtual community’s participants depends only on
the part of team members' publications in the virtual community.
Limitation 2. The participants’ publications of a virtual community team are distin-
guished by content, form of material feed, content type, but are joined together by a
common narrative, which must be get across to actors in direct or latent form to influ-
ence their public opinion.
We perform the synthesis of such synergetic management u  t  , which will provide
the formation of a virtual community of actors in SNSs, capable of further sustainable
functioning with the aim of spreading and promoting a strategic narrative aimed at
counteracting destructive information influence. Then the system of differential equa-
tions (3) will take the form where the part of new publications on a given topic in the
virtual community, published by the team members Q0 will be specified by the con-
trolling action u  t 

                           dx  t 
                                       Q  x  Dx,
                           dt
                                                                                     (4)
                           dQ  t   Du t   Q x  DQ.
                                                    
                           dt

Artificially controlled self-organization of actors in SNSs in the virtual community
will be achieved through the entry of a dynamic invariant into the system (3). The
chosen dynamic invariant should consider the peculiarities of the processes of actors’
social communication in SNSs. From the studies [8, 34] it is known that the increase
in the number of actors of the virtual community at a certain stage of its development
is slowed down and asymptotically reaches the boundary level. It is rational to for-
malize this phenomenon in the form of a differential equation of logistic type

                              dx  t                   x 
                                           xsup x 1 
                                                     x 
                                                               ,                      (5)
                                dt                      sup 
where  stands the parameter that determines the desired rate of increasing the num-
ber of the virtual community actors; xsup indicates the limited number of participants
in the virtual community at the stage of its development.
The use of the model (5) to describe the processes of creating a virtual community in
SNSs provides the visibility of their demonstration, in comparison with other models
of logistic type, presented in publications [11-13].
Consequently, in accordance with the concept of synergistic management of the ac-
tors’ interaction in SNSs, suggested in [11], and taking into account (5), the parameter
of the system’s order is its attractor, to which all the phase trajectories of the con-
trolled system (4) will follow, takes this form

                                                              x 
                          t     Q    xsup 1               D.              (6)
                                                             xsup 
                                                      

The physical content of the function   t  is to reduce the need for the content of the
specified matter to be provided by the members of the virtual community of SNSs
   Q  by increasing the number of the virtual community members to the critical
                                                        x 
level [35] in accordance with the expression  xsup 1 
                                                     x 
                                                               and the speed of the new
                                                        sup 

members’ appearing in the virtual community D .
According to [11, 13] expression (6) must satisfy the functional equation

                                      d  t 
                                  T                t   0 ,                        (7)
                                        dt

where stands the time during which a synergically controlled virtual community will
have transient processes to a given state of the SIS in SNSs.
The substitution of the attractor’s model (6) in the differential equation (7), consider-
ing the system (4), allows us to specify the analytical form of the model of synergetic
management

                                                  kz  Q 
                                                              2

               Du  t    x   Q   DQ 
                                                    zQ
                                                                    x Q   Dx 

                                                       x       
                              
                            1
                                 Q     xsup 1        D                     (8)
                            T                       x          
                                                       sup     

In order to achieve a stable state by the current virtual community in SNSs, we will
perform a study of the system of differential equations (4) on the stability using the
Lyapunov’s function method [36]. For the number of actors in the virtual community
in SNSs 0  x  1 the derivative from the Lyapunov’s function V   0 , if xsup  1
where   D , when   0 and D  0 . In the case xsup  1 of system (4) is stable
where   0 and D  0 or   0 and D  0 . In the same way, other conditions of
the system’s stability (4) are specified.
As a result of the synergistic management’s influence (7), the processes of actors’
self-organization will be started in SNSs, and after a while the system will reach the
point of a burst of synergistic effect [11], in which the virtual community will move
to a steady state. The coordinates of the splash point of the synergistic effect acquire
the following values

                                                   1
                              x1  xsup  D  xsup   ,                             (9)
                                                    

                                                      x1 
                                       xsup  1           D
                             kz                      xsup 
                      Q1                                          .
                             z      1            x      
                                  1    xsup 1  1   D 
                                        
                                     z                  
                                                xsup      


In Fig. [3] a BPMN diagram of the worked-out method for improving the information
stability of virtual communities in the Aris environment is presented, and its main
stages are specified.




Fig. 3. BPMN-chart of the worked-out method


4      Experiments

We will undertake an experimental study of the worked-out method for increasing the
information stability of actors in SNSs on the example of virtual communities c1 and
c2 in SNSs Facebook. The names of virtual communities c1 and c2 are not disclosed
due to their commercial secrecy.
As inputs for estimating the critical mass for the startup of virtual communities c1 and
c2 in SNSs Facebook, the experimental data received in 2014 have been used and
presented in Table [2].
  Table 2. Input data from the startup of virtual communities c1 and c2 in SNSs Facebook

                                                  Virtual community
                                       c1                                    c2
  Day        Date
                                 actor’s number                     actor’s number
                         increase    decrease      total    increase    decrease     total
  1       08.03.2014         8            0          8         15            0        15
  2       09.03.2014         5            0         13         41            0        56
  3       10.03.2014        21            0         34         33            0        89
  4       11.03.2014        38            0         72        106            0       195
  5       12.03.2014        43            0        115         39            0       234
  6       13.03.2014        32            0        147         46            0       280
  7       14.03.2014         3            0        150         27            0       307
  8       15.03.2014         2            0        152         47            0       354
  9       16.03.2014        42            0        194          2            0       356
  10      17.03.2014        63            0        257         19            0       375
  11      18.03.2014        57            0        314         10            0       385
  12      19.03.2014        84            0        398          8            0       393
  13      20.03.2014        48            0        446         35            0       428
  14      21.03.2014        90            0        536         13            0       441
  15      22.03.2014        21            0        557         42            0       483
  16      23.03.2014        23            0        580         19            0       502
  17      24.03.2014         3            0        583         15            0       517
  18      25.03.2014         2            0        585        154            0       671
  19      26.03.2014        20            0        605         50            0       721
  20      27.03.2014         5            0        610         61            0       782
  21      28.03.2014        23            0        633         32            0       814
  22      29.03.2014         0            0        633          4            0       818
  23      30.03.2014         0            0        633        179            0       997
  24      31.03.2014        82            0        715         86            0       1083
  25      01.04.2014         9            0        724         49            0       1132
  26      02.04.2014        26            0        750         60            0       1192
  27      03.04.2014        16            0        766         17            0       1209
  28      04.04.2014        46            0        812         65            0       1274

Promotion terms of virtual communities’ actors c1 and c2 are presented in Table [3].

                Table 3. Terms of the virtual communities’ startup c1 and c2

                                                                      Virtual community
   Sufficient conditions for promotion of the virtual community
                                                                        c1           c2
 The correct preparation of the virtual community for promotion         +            +
 Keeping a stable number of actors entering the virtual community       +            +
 for a specific period of time
 Obligatory keeping of the 10% barrier to the idea of a virtual         –            +
 community
 Timely publication of high-quality content                             –            +
 Choosing rational ways to spread information about the virtual         +            +
 community
Calculation of the corresponding metrics of self-similarity (2) for virtual communities
c1 and c2 has been carried out according to a well-known method [30, 31]. Results of
R/S-analysis of the virtual communities’ functioning is presented in Fig. [4] (a), (b).
The analysis of the obtained results allow us to draw the following conclusions:

1. the values of Hurst indexes for virtual communities c1 and c2 are HC1  0.487 and
   HC2  0.653 accordingly. In Fig. [4] the corresponding values of the indexes H C1
  and H C2 are indexes of degree in the equations of approximation;
2. comparison of the obtained results with the data of Table [1] shows that for re-
   source omin  28 days that is dedicated to the creation of virtual communities, vir-
  tual community c1 has no critical mass min  M  . The Hurst index for this com-
  munity characterizes the process of filling it with new actors, as a process of ran-
  dom nature.




                                               (a)




                                               (b)

 Fig. 4. Results of R/S- analysis: the virtual community c1 (а); the virtual community c2 (b)


  In the second case - for a virtual community c2 , the obtained results allow to ap-
  prove that the startup is successfully hold. The process of filling the virtual commu-
  nity c2 with an accuracy of 99.73% is persistent, that is, it has signs of a trend.
  Thus, the critical mass min  M  for a given virtual community for certain condi-
  tions will reach amin  1274 actors. As a result of the successful creation of the vir-
  tual community c2 in SNSs Facebook on the critical mass principle min  M  (1),
  (2) the activation of the virtual loop v has been provided and the mechanism of
  self-development e has been launched. The adduced conclusion is confirmed by
  the practice, as of 11/18/2014 as a result of the launch of the virtual community’s
   c2 self-development mechanism e on the principle of critical mass min  M  , the
  number of actors in it is 2211. As a result, this virtual community becomes resistant
  to destructive information influence, is able to perceive such content critically, uses
  in its activity the mechanisms of social control and social norms to neutralize the
  threats to the SIS in SNSs. Also, an additional factor that increases the information
  stability of the virtual community is the state actors’ actions to provide SIS.

Consequently, to manage the processes of creating a virtual community c1 , in which
the self-development mechanism has not been launched due to the use of the critical
mass principle, we apply synthetic synergistic management (8). In this case, the pa-
rameters of a controlled system of differential equations (4) acquire the following
values  z  0,5 , kz  0,5 ,   0,3. The speed of the appearing of the virtual com-
munity’s new members in the absence of artificial influence, we define as the coeffi-
cient of linear regression equation for a number of data of the total number of actors
in Table [2], which comes to the fore D  0,031103 actors / day. The duration of
transitions in a synergetically controlled virtual community T  1 day. Initial number
of actors in the virtual community x  0  0,008 103 , a part of team members’ publi-
cations in the virtual community Q  0   2% . Let the desirable growth rate in the
number of virtual community actors figures up to   0, 05 103 actors per day, and
the limited number of actors in the community at the stage of its development is –
 xsup  5 103 actors. Then, the modeled results of the synergistic impact on the virtual
community will look like as in Fig. [5], and a change in the part of publications of
team members in a controlled virtual community – in Fig. [6].




                                                              3
Fig. 5. The number of virtual communities in SNSs, 10 actors: a synergetically controlled
process of formation x  t  ; an uncontrolled process of formation xexp  t 
Fig. 6. Dynamics of the part of team members’ publications in a controlled virtual community

The data analysis in Fig. [4] shows that in the case of a synergically controlled infor-
mation impact on virtual community actors in SNSs, their number over the period of
t  40 days increases to 3,17 103 . In this case, in the absence of such influence in
 t  28 days after the experimental data, the number of actors will equal 0,812 103 ,
and with its use – 2,843 103 . It should be noted that the synergistically controlled
information impact on the actors of the investigated virtual community in the early
stages t  0;15 provides a lower rate of growth in the number of participants and is
a bit inert. However, at the next stages, t  16;40 the process of attracting new ac-
tors is much more intense. At the same time, the part of the team members' publica-
tions in the virtual community in SNSs Q  t  on the first day is 28% and gradually
decreases to 2.2% in t  40 days. The virtual community that is being synthesized
will independently distribute multimedia content with a specified narrative, which
will counteract the threats to the SIS in SNSs.
Consequently, the effectiveness of synergetically controlled information influence on
virtual community actors in SNSs increases in 3,5 times in comparison with the natu-
ral processes of their creation, and the virtual community formed in such a way is
capable of further self-development through self-organization processes and informa-
tionally sustainable against the impact of IS threats.


5      Conclusions

For the first time, to provide the SIS in SNSs, a method for increasing the information
stability of virtual communities is worked out, based on a combination of the critical
mass principle and the principle of synergistic management of the actors’ interaction.
At the same time, the information stability of the virtual community in SNSs is re-
duced to the ability of responding and recovering after the impact of threats to infor-
mation security, adapting to changes in the information space and realizing their pur-
pose of functioning. It is proved that adherence to the critical mass principle provides
stable development of the virtual communities’ dynamics and their information stabil-
 ity, which in the future excludes the reduction in the number of their actors and pro-
 vides further self-development. For virtual communities that are characterized by
 unsatisfactory qualitative indexes of the startup, the self-organization of actors in
 SNSs is due to the influence of synergistic management. In such a virtual community
 there are coherent collective processes and the directed self-organization of the com-
 munity and the parameters of interaction processes between actors. Such a virtual
 community is informationally resistant to the impact of threats to the SIS in the in-
 formation space of SNSs. In the future it is planned to investigate the influence of the
 promotion conditions of the virtual community in SNSs to its critical mass.


 References
 1. Official        website      of       the      U.S.      Department         of Justice (DOJ),
    https://www.justice.gov/opa/press-release/file/1102316/download
 2. The          official       website        of       the        European           Parliament,
    http://www.europarl.europa.eu/news/en/press-room/20190307IPR30695/eu-prepares-itself-
    to-fight-back-against-hostile-propaganda
 3. Popescu, E., Ghita, D.: Using social networking services to support learning. In: Internation-
    al Conference on Web-Based Learning, pp. 184–193. Springer, Berlin, Heidelberg (2013)
 4. Salehan, M., Kim, D. J., Koo, C.: A study of the effect of social trust, trust in social net-
    working services, and sharing attitude, on two dimensions of personal information sharing
    behavior. The Journal of Supercomputing 74(8), pp. 3596–3619 (2018)
 5. Osaka, K., Toriumi, F., Sugawara, T.: Effect of direct reciprocity and network structure on
    continuing prosperity of social networking services. Computational social networks 4(1):2
    (2017)
 6. Richter, A., Koch, M.: Functions of social networking services. In: Proceedings of the Inter-
    national Conference on the Design of Cooperative Systems (COOP 2008), pp.87–98. Carry-
    le-Rouet, Provence, France (2008)
 7. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge
    university press, Cambridge, England (1994)
 8. Missaoui, R., Sarr, I.: Social Network Analysis  Community Detection and Evolution.
    Springer International Publishing, Switzerland (2014)
 9. Hrabar, I., Hryshchuk, R., Molodetska, K.: Bezpekova synerhetyka: kibernetychnyi ta in-
    formatsiinyi aspekty, (in Ukrainian). Zhytomyr National Agroecological University, (2019)
10. Hryshchuk, R.: Osnovy kibernetychnoi bezpeky, (in Ukrainian). Zhytomyr National Agroe-
    cological University, Zhytomyr, Ukraine (2016)
11. Hryshchuk, R., Molodetska, K.: Synergetic control of social networking services actors’ in-
    teractions. In: Szewczyk R., Kaliczyńska M. (eds) Recent Advances in Systems, Control and
    Information Technology. SCIT 2016. Advances in Intelligent Systems and Computing, vol.
    543, pp. 34–42. Springer, Cham (2017)
12. Hryshchuk, R., Molodetska-Hrynhchuk, K.: Methodological foundation of state’s infor-
    mation security in social networking services in conditions of hybrid war. Information &
    Security: An International Journal 41, pp. 55–73 (2018)
13. Kolesnikov, A. A.: Sinergeticheskoe metody upravlenija slozhnymi sistemami: teorija sis-
    temnogo sinteza, (in Russian). Editorial URSS, Moskow (2005)
14. Sørensen, L. T., Skouby, K. E.: Requirements on next generation social networking – A us-
    er’s perspective. Wireless personal communications 51(4), p. 811 (2009)
15. Chen, R., Sharma, S. K.: Self-disclosure at social networking sites: An exploration through
    relational capitals. Information Systems Frontiers 15(2), pp. 269–278 (2013)
16. Yan, W., Zhang, Y.: User behaviors and network characteristics of US research universities
    on an academic social networking site. Higher Education, pp. 1–20 (2018)
17. Wiersma,      W.:      Critical   Mass    in    Collaborative    Hypertext    Environments,
    http://wybowiersma.net/pub/essays/Wiersma,Wybo,Critical_mass_in_collaborative_hyperte
    xt_environments.pdf.
18. Hryshchuk, R.: Startap virtualnykh spilnot u sotsialnykh merezhakh za pryntsypom
    krytychnoi masy, (in Ukrainian). Zakhyst informatsii, spetsialnyi vypusk pp. 19–25 (2015)
19. Xie, J., Sreenivasan, S., Korniss, G. et al.: Social consensus through the influence of com-
    mitted minorities. Phys Rev E. 84, 1, pp. 1–9 (2011)
20. Onyshchenko, O., Horovyi, V., Popyk, V. et al.: Sotsialni merezhi yak chynnyk rozvytku
    hromadianskoho suspilstva, (in Ukrainian). Natsionalna akademiia nauk Ukrainy, Natsion-
    alna biblioteka Ukrainy im. V. I. Vernadskoho, Kyiv, Ukraine (2013)
21. Khondker, H.: Role of the New Media in the Arab Spring. Globalizations 8(5), pp. 675–679
    (2011)
22. Sazanov, V.: Social'nye seti – publichnaja sfera, (in Russian). Laboratorija SVM, Moskow
    (2012)
23. Bryant, J., Thompson, S., Bruce, W.: Finklea Fundamentals of Media Effects. McGraw-Hill
    series in mass communication and jornalism, Waveland Press (2013)
24. Rheingold, H.: The virtual community: Finding commection in a computerized world. Addi-
    son-Wesley Longman Publishing Co., Inc. (1993)
25. Bosher, L., Chmutina, K.: Disaster risk reduction for the built environment. John Wiley &
    Sons (2017)
26. Doktryna          informatsiinoi       bezpeky          Ukrainy,       (in       Ukrainian),
    http://zakon2.rada.gov.ua/laws/show/47
27. Molodetska, K.: Mekhanizmy synerhetychno kerovanoi samoorhanizatsii aktoriv u sotsi-
    alnykh internet-servisakh, (in Ukrainian). Upravlinnia rozvytkom 4(4), pp. 1–13 (2018)
28. Scriven, M., Paul, R.: A statement by Michael Scriven Richard Paul. In: 8th Annual Interna-
    tional Conference on Critical Thinking and Education Reform (1987)
29. Ljem, Ch.: Hadoop v dejstvii, (in Russian). DMK Press, Moskow (2012)
30. Hurst, H.: Long-term storage capacity of reservoirs. Transactions of American Society of
    Civil Engineers 116: 770 (1951)
31. Hurst, H., Black, R., Simaika, Y.: Long-term storage: an experimental study. Constable,
    London (1965)
32. The Social Biology of Microbial Communities. In: Workshop Summary, Institute of Medi-
    cine, Board on Global Health, Forum on Microbial Threats National Academies Press
    (2012)
33. Arzamastsev, A., Andreev, A.: Matematycheskye modely kynetyky mykrobyolohycheskoho
    synteza: vozmozhnosty yspolzovanyia y novye podkhody k razrabotke. Vestnyk Tam-
    bovskoho unyversyteta, (in Russian). Seryia: Estestvennye y tekhnycheskye nauky 5(1),
    pp. 111–123 (2000)
34. Hanneman, R., Riddle, M.: Introduction to social network methods. University of California,
    Riverside (2005)
35. Molodetska, K.: Sposib pidtrymannia zadanoho rivnia popytu aktoriv sotsialnykh internet-
    servisiv na content, (in Ukrainian). Radioelektronika, informatyka, upravlinnia 4(35),
    pp.113–117 (2015)
36. Kalytyn, B.: Kachestvennaia teoryia ustoichyvosty dvyzhenyia dynamycheskykh system, (in
    Russian). Belorusskiy gosudarstvennuy universitet, Minsk (2002).