=Paper=
{{Paper
|id=Vol-2392/paper8
|storemode=property
|title=Modelling the Behavior Classification of Social News Aggregations Users
|pdfUrl=https://ceur-ws.org/Vol-2392/paper8.pdf
|volume=Vol-2392
|authors=Solomia Fedushko,Olha Trach,Zoryana Kunch,Yaryna Turchyn,Ulyana Yarka
|dblpUrl=https://dblp.org/rec/conf/coapsn/FedushkoTKTY19
}}
==Modelling the Behavior Classification of Social News Aggregations Users==
https://ceur-ws.org/Vol-2392/paper8.pdf
Modelling the Behavior Classification of Social News
Aggregations Users
*[0000-0001-7548-5856] [0000-0003-1461-791X]
Solomia Fedushko , Olha Trach ,
[0000-0002-9114-1911]
Yaryna Turchyn , Zoryana Kunch [0000-0002-8924-7274] ,
Ulyana Yarka [0000-0003-1920-1980]
Lviv Polytechnic National University, Lviv, Ukraine
solomiia.s.fedushko@lpnu.ua, olya@trach.com.ua,
turchyn.j@gmail.com, uliana.b.yarka@lpnu.ua,
zorjana.kunch@gmail.com
Abstract. This paper deals with actual fuzzy logic approach for modelling the
behavior classification of social news aggregations users. The peculiarities of
the structure of informational content of communities on the basis of social
news aggregations are explored. A formal model of social news aggregation
model has been developed, which includes user of the social news aggregation
on the basis of fuzzy measures of its characteristics. The method of behavioral
classification of users and methods for structuring sections and discussions of
social news aggregations are developed. The methods for determining the main
characteristics of the users of the social news aggregation: activeness, creative-
ness, attractiveness, reactiveness, loyalty, is developed. Method for defining
characteristics and classification of social news aggregations users is presented.
Keywords: classification, social network, modelling, social news aggregation,
fuzzy logic, behavior classification, web, user.
1 Introduction
Each user of social news aggregations makes a contribution to the development of a
social news aggregation. Contribution of the user can be determined objectively
(based on the study of its behavior, information content, which is created and classifi-
cation) and subjective (based on the assessments of other user of social news aggrega-
tions and expert evaluations). The indicator of user usefulness allows ranking the
users of social news aggregations, using received information for further administra-
tive measures. For example, the most useful users need to be involved in moderation
of the social news aggregation, to stimulate the material, while users with negative
usefulness need to remove from social news aggregations.
Ranking the users in the contribution and determining the core of social news aggre-
gation user allows the administrator at any time to establish the users, who bring the
social news aggregation the maximum benefit.
This information is necessary and critically useful for managing the social news ag-
gregation, since the social news aggregation is different from the usual site, which the
administrator must take into account the views and interests of the users. And in order
to make social news aggregation-based solutions is needed to listen to users, who
have a great authority and make the main contribution to its development.
The development of methods for behavioral classification of users of social news
aggregation based on the presentation of information content as a tree is an urgent
task.
2 The Method of User Behavioral Classification
The user of the social news aggregation a person who visits the social news aggrega-
tion site, reads or publishes its content in the form of discussions and messages on the
social news aggregation is considered.
The model of the user of the social news aggregation is assigned in the following
form:
Logini ,Passwordi ,Statusi ,Emaili ,
SNAU i
MemNamei ,LastVisiti ,PersonalDatai
where SNAUi is a user of social news aggregations; Logini is pseudo of user; Pass-
wordi is password of user; Statusi is the role of a user in the community; Emaili is e-
mail; Memnamei is the name of the user; LastVisiti is date of the last visit to social
news aggregation; PersonalDatai is personal data of the user.
2.1 Development of Methods for the Calculating Characteristics of Social
News Aggregation Users
In the course of the research it was established that the users of the social news aggre-
gations (1) to a greater or lesser extent have the following characteristics in Figure 1.
Fig. 1. Scheme of characteristics of social news aggregation users.
Based on these characteristics, we define the rules for classifying users in social news
aggregations.
For presentation the characteristics of the users in the social news aggregation we will
use a set of unclear plurals of the listed characteristics of the community users in rela-
tion to the entire social news aggregation are determined based on the analysis of the
behavior of users within the community:
─ activeness is determined by the amount of information content they create;
─ creativeness is determined the quality of information content and how other users
of community react to it;
─ attractiveness is determined the quantity of users who react to the created content;
─ reactiveness is a way of participating in discussions;
─ loyalty is a reaction to the information content of other users.
2.2 Development of Methods for Calculating the Values of Linguistic
Variables and Measures of Belonging
For each of the proposed characteristics of the users, we introduce the corresponding
linguistic variables: Activeness, Creativeness, Attractiveness, Reactiveness, and Loy-
alty.
The linguistic variable is given by the quartet
< 𝛽, 𝑇, 𝑋, 𝑀 >
where 𝛽 is the name of the linguistic variable;
T is a plural of values of a linguistic variable, which is the names of fuzzy variables;
T " low"," medium"," high"
X is area of definition of fuzzy variables describing the linguistic variable
𝑋 = [0; 𝑐𝑎𝑟𝑑(𝑃𝑜𝑠𝑡)];
M is a set of measures of fuzzy variables, which are values of a linguistic variable.
A2
1
A1 A3
0,5
x
1 1 2 2 3 1 4 2
Pнизька Pсередня Pнизька Pсередня Pсередня Pвисока Pсередня Pвисока card(Post)
Fig. 2. The function of belonging to fuzzy plurals: A1 –"low", A2 – "medium", A3 –"high".
(1) 2 1 2 3 4 1 2
Plow , Plow , Pmedium , Pmedium , Pmedium , Pmedium , Phigh , Phigh are the param-
eters that are proportional to card(Post) and given by an expert or administrator of the
forum, and moreover
Pmedium Plow Pmedium Pmedium Phigh Pmedium Phigh 1.
1 1 2 2 3 1 4 2
Plow
All the P parameters that are used later in the membership functions are determined
by experts for each feature of the social news aggregation.
We will write down the functions of membership for all characteristics (1) – (30) of
the users of the social news aggregation.
The activeness of creating discussions:
1,if 0 ActivenessThread SNAU i Patlow
1
2
Patlow ActivenessThread SNAU i
low Th 2 1
, (1)
Pat low Pat low
if Patlow ActivenessThread SNAU i Patlow
1 2
ActivenessThread SNAU i Patmedium
1
2 1
,
Patmedium Patmedium
if Patmedium ActivenessThread SNAU i Patmedium
1 2
2
medium Th 1,if Patmedium ActivenessThread SNAU i Patmedium
3 (2)
Pat
medium ActivenessThread SNAU i
3 3
,
Patmedium Patmedium
3
if Patmedium ActivenessThread SNAU i Patmedium
4
ActivenessThread SNAU i Pathigh 1
2 1
,
Pat high Pat high
high Th if Pathigh
1
ActivenessThread SNAU i Pathigh 2
(3)
1, if Pat 2 Activeness
high Thread SNAU i 1
where ActivenessThread SNAUi is activeness of creating posts j-th user.
The activeness of creating polls:
1, if 0 ActivenessPoll SNAU i Papllow
1
2
Papllow ActivenessPoll SNAU i
low Pl 2 1
,
Papl low Papllow
(4)
if Papllow ActivenessPost SNAU i Papllow
1 2
ActivenessPoll SNAU i Paplmedium 1
2 1
,
Paplmedium Paplmedium
if Paplmedium ActivenessPoll SNAU i Paplmedium
1 2
2
medium Pl 1, if Paplmedium ActivenessPoll SNAU i Paplmedium
3 (5)
Paplmedium ActivenessPoll SNAU i ,
4
3
Paplmedium 3
Paplmedium
3
if Paplmedium ActivenessPoll SNAU i Paplmedium
4
ActivenessPoll SNAU i Paplhigh
1
2 1
,
Paplhigh Paplhigh
high Pl if Paplhigh
1
ActivenessPoll SNAU i Paplhigh
2
(6)
1, if Papl 2 Activeness
high Poll SNAU i 1
where ActivenessPoll SNAUi is the activity of creating polls i-th user.
The activeness of creating posts:
1, if 0 ActivenessPost SNAU i Papslow
1
2
Papslow ActivenessPost SNAU i
low Ps 2 1
, (7)
Paps low Papslow
if Papslow ActivenessPost SNAU i Papslow
1 2
ActivenessPost SNAU i Papsmedium 1
2 1
,
Papsmedium Papsmedium
if Papsmedium ActivenessPost SNAU i Papsmedium
1 2
(8)
2
medium Ps 1, if Papsmedium ActivenessPost SNAU i Papsmedium
3
Papsmedium ActivenessPost SNAU i ,
4
Papsmedium3 3
Papsmedium
3
if Papsmedium ActivenessPost SNAU i Papsmedium
4
ActivenessPost SNAU i Papshigh
1
2 1
,
Papshigh Papshigh
high Ps if Papshigh
1
ActivenessPost SNAU i Papshigh 2 (9)
1, if Paps 2 Activeness
high Post SNAU i 1
where ActivenessPost SNAUi is the activity of the i-th user in the creation of
posts.
Activeness of participation in polls:
1, if 0 ActivenessVote SNAU i Pvtlow
1
Pvt ActivenessVote SNAU i
2
low Vt low 2 1
, (10)
Pvt low Plow
if Pvtlow ActivenessVote SNAU i Pvtlow
1 2
ActivenessVote SNAU i Pvtmedium 1
2 1
,
Pvtmedium Pvtmedium
if Pvtmedium ActivenessVote SNAU i Pvtmedium
1 2
2
medium Vt 1, if Pvtmedium ActivenessVote SNAU i Pvtmedium
3 (11)
Pvtmedium ActivenessVote SNAU i ,
4
3
Pvtmedium 3
Pvtmedium
3
if Pvtmedium ActivenessVote SNAU i Pvtmedium
4
ActivenessVote SNAU i Pvthigh
1
2 1
,
Pvthigh Pvthigh
high Vt if Pvthigh
1
ActivenessVote SNAU i Pvthigh
2 (12)
1, if Pvt 2 Activeness
high Vote SNAU i 1
where ActivenessVote SNAUi is active participation in the voting.
The activeness of evaluating the actions of other users:
1, if 0 ActivityFeedback SNAU i Pfblow
1
2
Pfblow ActivityFeedback SNAU i
low Fb 2 1
, (13)
Pfblow Pfblow
if Pfblow ActivityFeedback SNAU i Pfblow
1 2
ActivenessFeedback SNAU i Pfbmedium
1
,
Pfbmedium
2 1
Pfbmedium
if Pfbmedium ActivenessFeedback SNAU i Pfbmedium
1 2
(14)
2
medium Fb 1, if Pfbmedium ActivenessFeedback SNAU i Pfbmedium
3
Pfbmedium ActivenessFeedback SNAU i ,
4
Pfbm edium
3 3
Pfbmedium
3
if Pfbmedium ActivenessFeedback SNAU i Pfbmedium
4
ActivenessFeedback SNAU i Pfbhigh
1
2 1
,
Pfbhigh Pfbhigh
high Fb if Phigh
1
ActivenessFeedback meSNAU i mberi Pfbhigh
2 (15)
1, if Pfbhigh 2
ActivenessFeedback SNAU i 1
where ActivenessFeedback SNAUi is the activeness of evaluating actions by the i-
th user.
Total activeness:
Calculated based on these types of activeness:
1, якщо 0 ActivenessTotal SNAU i Ptlow 1
2
Ptlow ActivenessTotal SNAU i
low Total 2 1
, (16)
Ptlow Ptlow
if Ptlow ActivenessTotal SNAU i Ptlow
1 2
ActivenessTotal SNAU i Ptmedium 1
2 1
,
Ptmedium Ptmedium
if Ptmedium ActivenessTotal SNAU i Ptmedium
1 2
(17)
2
medium Total 1, if Patmedium ActivenessTotal SNAU i Patmedium
3
Patmedium ActivenessThread SNAU i ,
4
3
Patmedium 3
Patmed
ium
if Patmedium ActivenessTotal SNAU i Patmedium
3 4
ActivenessTotal SNAU i Pthigh
1
2 1
,
Pthigh Pthigh
high Total if Pthigh
1
ActivenessTotal SNAU i Pthigh
2
(18)
1, if Pt 2 Activeness
high Total SNAU i 1
where ActivenessTotal SNAUi is total activeness of the i-th user.
Creativeness of the user:
1, if 0 Creativeness SNAU i Pcrlow 1
2
Pcrlow Creativeness SNAU i
low Cr 2 1
, (19)
Pcrlow Pcrlow
if Pcrlow Creativeness SNAU i Pcrlow
1 2
Creativeness SNAU i Pcrmedium 1
2 1
,
Pcrmedium Pcrmedium
if Pcrmedium Creativeness SNAU i Pcrmedium
1 2
medium Cr 1, 2
Creativeness SNAU i Pcrmedium
3 (20)
if Pcrmedium
4
Pcrmedium Creativeness SNAU i
,
3
Pcrmedium 3
Pcrmedium
ium Creativeness SNAU i Pcrmedium
3 4
Pcrmed
Creativeness SNAU i Pcrhigh
1
2 1
,
Pcrhigh Pcrhigh
high Cr if Pcrhigh
1
Creativeness SNAU i Pcrhigh
2 (21)
1, if Pcr 2 Creativeness SNAU 1
high i
where Creativeness SNAU i is creativeness of the user.
Аtractiveness of the user:
1, if 0 Attractiveness SNAU i Pattrlow 1
2
Pattrlow Attractiveness SNAU i
low Attr 1 2 1
, (22)
Pattrlow Pattrlow
if Pattrlow Attractiveness SNAU i Pattrlow
1 2
Attractiveness SNAU i Pattrmedium 1
2 1
,
Pattrmedium Pattrmedium
if Pattrmedium Attractiveness SNAU i Pattrmedium
1 2
(23)
2
medium Attr 11, if Pattrmedium Attractiveness SNAU i Pattrmedium
3
Pattrmedium Attractiveness SNAU i ,
4
Pattrmedium 3 3
Pattrmedium
3
if Pattrmedium Attractiveness SNAU i Pattrmedium
4
Attractiveness SNAU i Pattrhigh
1
2 1
,
Pattrhigh Pattrhigh
high Attr 1 if Pattrhigh
1
Attractiveness SNAU i Pattrhigh
2 (24)
1, if Pattr 2 Attractiveness SNAU 1
high i
where Attractiveness SNAU i is attractiveness of the user.
Reactiveness of the user.:
1, if 0 Reactiveness SNAU i Prlow
1
2
Prlow Reactiveness SNAU i
low R 1 2 1
, (25)
Prlow Prlow
if Prlow Reactiveness SNAU i Prlow
1 2
Reactiveness SNAU i Prmedium 1
2 1
,
Prmedium Prmedium
if Prmedium Reactiveness SNAU i Prmedium
1 2
(26)
2
medium R 1, if Prmedium Reactiveness SNAU i Prmedium
3
Prmedium Reactiveness SNAU i ,
4
3
Prmedium 3
Prmedium
3
if Prmedium Reactiveness SNAU i Prmedium
4
Reactiveness SNAU i Prhigh
1
2 1
,
Prhigh Prhigh
high R if Prhigh
1
Reactiveness SNAU i Prhigh
2 (27)
1, if Pr 2 Reactiveness SNAU 1
high i
where R e activeness SNAU i is reactiveness of the user.
Loyalty of the user:
1, if 0 Loyalty SNAU i Pllow
1
2
Pl Loyalty SNAU i
low L low 2 1
, (28)
Pllow Pllow
if Pllow Loyalty SNAU i Pllow
1 2
Loyalty SNAU i Plmedium 1
2 1
,
Plmedium Plmedium
if Plmedium Loyalty SNAU i Plmedium
1 2
(29)
2
medium L 1, if Plmedium Loyalty SNAU i Plmedium
3
Plmedium Loyalty SNAU i ,
4
3
Plmedium 3
Plmedium
3
if Plmedium Loyalty SNAU i Plmedium
4
Loyalty SNAU i Plhigh
1
2 1
,
Plhigh Plhigh
high L if Plhigh
1
Loyalty SNAU i Plhigh
2 (30)
1, if Pl 2 Loyalty SNAU 1
high i
where Loyalty SNAU i is loyalty of the user.
3 Building Rules for Classifying Users of Social News
Aggregation
The classification rules for each class of users of the social news aggregation are for-
mulated based on the developed methods for calculating the characteristics of users
and certain classes of social news aggregation users. The classes of the social news
aggregation are proposed:
Activist
Moderator
Flamer
Author
Critic
Reader.
The membership of the users in one of the classes based on its characteristics (Ac-
tivity, Creativity, Attraction, Reactivity, Loyalty) is represented by production rules
and Table 1.
Table 1. Classes users of the social news aggregation.
Moderator Flamer Author Critic Reader Activist
Activeness medium, medium, medium,
low low
high high high
Creativeness medium, medium,
low low low
high high
Attractiveness low, low,
high
medium medium
Reactiveness low, medium,
high low low
medium high
Loyalty medium,
low
high
1. If Асtiveness(SNAU) "medium","high" and
Creativeness(SNAU) "medium","high" and
Reactivenes(SNAU) "low","medium"
then Member - Аctivist;
2. If Аctiveness(SNAU) "medium","high" and
Reactivenes(SNAU)="high" and
Loyatly(SNAU) "medium","high" , then Member - Мoderator;
3. If Аctiveness(SNAU) "medium","high" and
Creativeness(SNAU)="low" and
Loyatly(SNAU)="low", then Member - Babbler;
4. If Аctiveness SNAU ="low" and
Creativeness(SNAU) "medium","high"
and Аtractiveness(SNAU) "medium","high"
and Reactiveness SNAU ="low",then Member - Аuthor;
5. If Creativeness SNAU ="low" and
Rеаctiveness(SNAU) "low","high"
and Аtractiveness(SNAU)="high" and
Loyatly SNAU ="low",then Member - Critic;
6. If Аtractiveness SNAU ="low" and
Creativeness( SNAU )="low"
and Atractiveness( SNAU ) "low","average" and
Reactiveness SNAU ="low", then Member - Reader;
4 Determine the User's Usefulness for the Community
The usefulness of a user for the social news aggregation is a complex indicator calcu-
lated on the basis of its characteristics: activeness, attractiveness, creativeness, reac-
tiveness and loyalty. The usefulness of user ME is calculated by the equation:
ME C1 Activeness C2 Attractiveness C3 Creativeness
(31)
C4 Reactiveness C5 Loyalty
where C1 ,C2 , ,C5 is weight coefficients of each user's characteristics, which are
determined based on the development scenario of the social news aggregation, more-
over C 1 , Ci 0 .
i
i
Consequently, 𝑀𝐸 ∈ [0, 1].
The user's usefulness allows the administrator to evaluate the importance of the user
for the community and to take this value into account when applying sanctions.
5 Conclusion
In this work the models have been developed that are the basis for further research on
the construction of effective site positioning methods. Formalized structure of the
social news aggregation, which includes two components (information content, users)
is suggested.
The peculiarities of the structure of informational content of communities on the basis
of social news aggregations are explored. A formal social news aggregation model
has been developed, which includes the model of user of the social news aggregation
on the basis of fuzzy measures of its characteristics, the model of the structure of
information content and the model of the content of information content, on the basis
of which developed the method of behavioral classification of users and methods for
structuring sections and discussions of social news aggregations.
The methods for determining the main characteristics of the users of the web commu-
nity: activeness, creativeness, attractiveness, reactiveness, loyalty are developed.
The classes of users of social news aggregations on the basis of social news aggrega-
tions are allocated and the rules of classification of users are formulated.
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