Extended Analysis of Multiplayer Platform Users Activity Based on the Virtual and Real Time...

Extended version of paper: Jankowski J., Analysis of Multiplayer Platform Users Activity Based on the

Virtual and Real Time Dimension Lecture Notes in Computer Science Volume 6984, 2011, pp 312-315

Analysis of Multiplayer Platform Users Activity Based

on the Virtual and Real Time Dimension

Jaroslaw Jankowski

Faculty of Computer Science and Information Technology

West Pomeranian University of Technology

ul. Zolnierska 49, 71-410 Szczecin, Poland

[email protected]

Abstract. The paper proposes an approach to modeling online multiplayer

systems users’ behavior and segmentation in terms of usage patterns. The

solutions presented, are based on analysis of time series in the real and virtual

time dimension. The proposed method can be applied in the field of community

platforms, virtual worlds and massively multiplayer online systems to capture

quantitative characteristics of online platforms usage.

Keywords: multiplayer platforms, time series analysis, web users’ behavior

1 Introduction

Together with the development of web systems, the need to conduct analyses that

focus on users’ behavior, increases. Research in the field of online communities,

virtual worlds, and massively multiplayer platforms and games, among other, relates

to users engagement [29] and social dynamics [7]. A big volume of the collected data

requires the usage of data mining methods, which are developed in the area of

personalization [22], semantic processing [9] and users’ segmentation [27]. Solutions

available in the cited papers are mainly focused on the analysis of behaviors in the

scope of websites in the connection with content, external links and navigation

patterns [23][12]. Less attention is paid to the segmentation of multiplayer platform

users and similarities based on time sequences related to their activity and platform

usage characteristics. In that context the goal of this article is to propose an approach

focused on the segmentation of users in the scope of trends and system usage

frequency. For the purposes of the considered behavior patterns, the dimension of real

and virtual time was introduced. The analysis of dependencies between the real and

virtual time activity and users segmentation based on usage patterns, delivers valuable

information about trends and can be used in planning platform development and

marketing actions. The structure of the article is as follows: paragraph two includes

the analysis of methods used for analysing website users activity, paragraph three

presents the assumptions of the proposed solution, paragraph four describes the

empirical research and analysis. User segmentation based on proposed approach is

presented in paragraph five. A summary and some final remarks are presented in

paragraph six.



2 Motivation and related work

Multiplayer online platforms can be thought off as multi-dimensional environments,

which connect the features of economic, technical and social systems. They work

usually in the form of virtual worlds or massive multiplayer games, where users

develop a parallel identity, perform different roles and participate in creating complex

social relations. They function somehow simultaneously with mainstream social

applications, such as Facebook or Twitter. They can also be integrated with them in

the form of social games and provide different forms of entertainment or

communication than web platforms. Depending on the used technology they acquire a

2D, 2.5D or 3D representation and integrate extensions improving the communication

with other users by audio or video sensations. Depending on the scope of usage and

functional specification, they are divided into several groups [32]. S. Kish [19]

proposes classification of systems in this area in five main categories including

relation to real world. These systems are divided into MMORPGs (Massive

Multiplayer Online Role Playing Games), more general MMO (Massive Multiplayer

Online), metaverses (social oriented), MMOLEs (Learning and Training

Environments), intraverses (internal corporate systems) and paraverses (mirror

worlds). In all of this groups, there is a growing demand for implementation of new

methods for data processing. The analyses aim to recognize different occurrences

and trends to acquire knowledge that have a significant role in the development of

online services. It creates interest in the area of methods of social network analysis

[24], diffusion of marketing messages [1], recommendations [13][11][17] or

dynamics of network structure development [21]. There are also developed solutions

deriving from methods of knowledge discovery in databases, which with reference to

online systems acquire a specialized form of web mining algorithms. They can be

applied, among others, at the basis of agent systems [10][5], electronic marketing

platforms [6], and in design of intelligent interfaces. In most kind of applications time

dimension is taken into consideration and it is a base for activity pattern analysis and

loyalty estimation of online service users. In this area it is important to determine a

quantitative representation of time–related behavior characteristics, and compare the

various patterns of behavior.

New approaches and methods are developed that are focused on the analysis of data

that enable the acquisition of additional knowledge about online consumers’ behavior.

Temporal data mining is one of the developing fields of methods for knowledge

extraction from data bases focused on time characteristics. C.M. Antunes and L.O.

Arlindo presented a review of approaches and methods used in this field [2], and the

scope of appliance in different fields were identified. The survey of methods

presented in the study [28] relates to temporal data bases and indicates development

direction of methods focused on acquiring knowledge about hidden patterns and

analysis of changeability of sets with temporal characteristics. The study [16]

develops the aspect of web data specification and the need to search for dedicated

solutions that include local solutions which, when searching for generalized

dependencies for all data sets, are not detected. The presented methods are focused on

acquiring temporal, indirect, frequent patterns and their temporal, extended patterns in

the area of identifying Web users with distinct interests. Changeability of consumers’

behavior in time and evolution in time connected with different factors, as well as

developing new technologies in time, is emphasized in the works of V. Venkatesh and

M.G. Morris [31], E. Karahanna and others [18]. As B. Hernandez-Ortega, J. Jimenez

and others proved, there are significant differences in the behavior of potential, new

or permanent consumers [14]. The identification of their behavior enables adequate

segmentation and offers targeting or adaptation of functionality to a given group of

recipients. S.M. Beitzel and others presented an analysis focused on the temporal

aspects and arrangement in time of data from logs from servers connected with search

engines, and they proved that there is a possibility of temporal analyses in this

environment [3]. The concept of analysis of time series focused on analysis of Web

search system logs was presented by Y. Zhang, B.J. Jansen and others [36]. The

analysis of time series enabled the detection of dynamic occurrences and consumers’

behavior patterns. The temporal nature of data from transactional systems provided

the opportunity to isolate short-term patterns and to generalize multi-scope analyses.

Analysis in this area can be used to identify the consumers’ characteristics and also

increase their loyalty, which is a significant element of marketing actions [8].

The development of technologies based on communities, increases users’ interactions

and can be an element of strategies focused on loyalty building, e.g. in the form of

weblogs in the scope of corporative systems, which was indicated by S.C. Herring

and others [15]. Interpersonal engagement is an additional factor of loyalty building

also in the scope of technological platforms, which was proved by C. Wagner and N.

Bolloju [34]. What is also important in creating loyalty towards advanced online

services are the limitations of introduction threshold, where getting to know a new

system is time-consuming. F. Wangenheima and T. Bayona state that behavioral

aspects of loyalty translate into the purchase of a service and recognition of the

superiority of an individual service over other subjects on the market [35]. H.P. Lu

and S. Wang identify a dependency between users’ satisfaction and loyalty in

connection to Internet addiction [20]. The research also indicates a phenomenon

characteristic for online entertainment services, where loyalty can occur even with the

lack of satisfaction and according to H. Tsai and H. Huang [30] it can discourage

searching for alternatives. The presented studies focus on the aspect of representation

and temporal data analyses. Nevertheless, they do not include the specification of

multiplayer platforms and functioning of users in virtual reality, for which different

structuring of time lapse can be determined than in real systems, and with a use of

multidimensional analysis, the evaluation of loyalty level or characteristic of using

individual service or platform can be done. The approach presented in this paper

identifies two dimensions of temporal data analysis based on real and virtual time that

enable the segmentation of users, focusing on time series and trends identification.

The approach presented in the next part gives the opportunity to conduct analyses that

enable users to determine audiences’ parameters, identify their characteristics,

behavior trends, estimate loyalty and engagement levels of users, as well as detect

tendencies.

3 Conceptual framework for usage patterns analysis

The article herein proposes an approach towards data analysis and characteristics of

multiplayer platforms usage related to the players activity and engagement. Users

usually perform distinct roles in the virtual worlds or online games, building their

position by achieving levels of initiation and expertise. Their representation is

functioning in cyber space in a sense of place and environment. It can be assumed that

here exists also another dimension of time, more related to the virtual life and activity

within the online system than to real life. Time series analysis in such case will

usually show some periods of activity and inactive real days when the user is not

logged into the system, and seasonality can be observed with low activity during late

hours or offline periods. Analyzing together the time dimension related to the period

spent in the system and real life time, is mixing both realities. In our approach we

propose to separate them into two time dimensions. The main focus is to analyze the



relation between them and measure the distance and dynamics of their changes

together with comparison to the other users, and the possibility to create user

segments. Theoretical background related to a different time interpretation for

different periods is based on the approach proposed by S. Radas and S.M. Shugan

who de-seasonalised processes by changing the speed at which time flows [26].

Research showed that during low seasons time can be slowed down and during high

seasons can be speeded up. In our research we don’t change the speed of time flow.

Instead, we considered time flow in the online system, and time spent offline without

contacts with multiplayer environments as separate dimensions.

Lapse of time in the online system (herein referred to as ‘virtual time’) can be

considered separately from real time. For mapping and parameters’ determination

purposes, the two-dimensional bi-temporal model of time representation was adopted,

in which occurrences are registered in terms of real and virtual time. There can be

introduced different time separations that will lead to an increase of analysis accuracy

by decomposition of the time period into sub periods and determination of time

granulation. In order to describe such a dependency, the virtual time factor was

defined, which gives the relation of virtual and real time, according to the formula

F=v*n/d, where d represents the number of real time days, v the number of virtual

time days and n is the number of time intervals taken into consideration for

monitoring the users’ activity. Such an approach enables monitoring the lapse of both

types of time for each user and determining a level of activeness. For the purpose of

data analysis consistency with assumed time granulation at the t moment for every i-

th user, the St snapshot of parameters that are monitored in an individual time period

is generated. Parameters can include social characteristics, user’s activeness, number

of loggings and use of particular system functions according to the formula:

1

,1,1

1

,1t

00

,1

1,= S

tk

t

i

k

i

t

i

t

t

tk

i

k

i PT

PDPPt

(1)

where Ps,t parameters were assumed for every i-th user, with i = 1,…,n for every type

of s parameter where s= 1,…,m. For every t time moment the aggregated parameter

values are determined in relation to previous periods, and so are the dynamics of

change Dt in relation to the previous period and the total number of periods T. Time

dependency between real and virtual time is a measure of the user’s engagement and

the dynamics of service usage. A set of snapshot parameters depends on the

parameters and abilities of a system and aims to present the dynamics of system usage

and changeability of the user’s determined features. For the purpose of sequence

analysis, the methods focused on similarity between sequence A and B can be

introduced in the form of similarity d(A,B) and the correlation factor can be used to

measure it. Similarity can be defined as a distance among the Minkowski series,

determined consistently with the following formula:

PPn

i

ii yxBAd

/1

1

),(

(2)

In the case P=2, the distance among series is a Euclidean distance. Determination of

distance in such a way causes limitations in case of a series with different length and a

series with time-lag with changeable amplitude. When comparing two series in the

scope of real and virtual time, there can be a time-lag that should not influence the

total similarity evaluation. To evaluate similarity, the time warping distance method

was applied. For two series X(x1,x2,…,xn) and Y(y1,y2,…,ym), with lengths n and m

respectively, the M matrix is defined as an interpoint relation between series X and Y

where element Mij indicates the distance d(xi,yj) between xi and yj. Dependency among

a series is determined by the time warping path. The algorithm determines the

warping path with the lowest cost between two series according to the formula [25]:

kkW

wwwwdYXD ,...,,,min, 21 (3)

where dk=d(xi,yj) indicates distance represented by wk=(i,j) on w path. A series with a

higher level of similarity can be better compared because of alignment and

dependencies resulting from dynamic time distance. In the proposed procedure, the

users’ sequences can be compared to distinguish similar behavior and compared to the

pattern of ideal time lapse similar to real time. On the basis of time series and

behavior patterns it is possible to create a learning system focused on detection of

users with big potential, for which the engagement in system usage and becoming a

heavy user has a high probability. For the purpose of further analysis, the usage of

classification and regression trees was adopted [4]. The selection of subsections was

applied on the basis of predictor variables, which are used to forecast the belonging of

cases and objects to classes determined by the dependent variables. In the applied

method the first phase of division consists of determining the division knots and a

predictor variable. The classification is made on the basis of the value representing

the distance from the ideal vector for individual units of time. Similarity in the frames

of separate groups relates to the patterns of behavior. The forecasts of behaviors in

later periods can be generated based on sequences with determined lengths. In the

next part of this article this approach is verified on the basis of empirical data and a

particular area of applications was identified.

4 Empirical data analysis based on real and virtual time

In the next stage an analysis based on empirical data was conducted. Real and virtual

time was identified for the multiplayer platform Timik.pl (a virtual world platform),

targeting web users in Poland. The system in question is presented in the register of

virtual worlds published by Virtual Worlds Management, and has more than 850k

registered users [33]. The main function relates to communication, social area,

multiplayer games and chats. Data received for research purpose remained

anonymous and were delivered in encoded form. We took into consideration 4410

users who created accounts during three months prior to the research and their

activeness was registered at least for 10 real days in that period. We analysed the

period of one hundred days and the number of real days was equal to virtual days. For

particular users, changeable dependencies of two dimensions of time can be observed.

Fig. 1 shows the time dependency for real time Rd and virtual time Vd. On axes x and

y, real and virtual days were marked. For user u1 there is a consistency of measures,

and a high frequency of system usage is observed. User u6 initially showed an

increase in the distance between real and virtual time, with Vd =12 and Rd=20 at point

A. In the following days the distance is slightly changed to Vd=19, and there is a

stability up to point B where the distance rapidly increases and eventually equals

Vd=49 and Rd=100. For user u5, stability occurs from Vd=7 up to point C after which

the distance rapidly increases between dimensions. Dependencies among users can be

a basis for segmentation depending on behavior similarities. The analysis was made

including similarities and a distance to an ideal vector, which represents a state of

maximum engagement. In such a situation Vd is consistent with Rd.



Fig. 1. Relative users’ behavior characteristics u1, u5

and u6.

Fig. 2. Average values of real day in relation to

virtual day.

Fig. 2 presents average values of real days for corresponding virtual days for the

initial set of users. For example, on the 11th virtual day the average value of the real

day was 25. In the initial period of service usage, the average value increases, and in

the following period it decreases. In the following step the dynamic time warping

algorithm was used to determine the similarity of the time series with the ideal vector.

Fig. 3 presents the changeable dependency for particular users with a change of

sequence length from virtual dimension. Together with the change of sequence length,

the minimum and maximum distance to the ideal vector increases, which is illustrated

by a regression line.

Fig. 3. Distance from ideal vector calculated with dynamic time warping.

Fig. 4. Detailed distance characteristics for users u1-u1000.

A similarity to the ideal vector results not only from the sequence length, but also

from the character of changes and lag of virtual time vector. Such characteristics are

included by dynamic time wrap method that enables, among others, a presentation

with comparison to the sequence time-lag. Fig. 4 illustrates in detail the distance for

sequences from 1-1000 (window W1 from Fig. 3) and distances from the ideal vector.

Point A represents user u92 with dtw=68 and sequence length 74. Point B shows user

u183 with dtw=574 and sequence length 64. Point C shows user u332 with dtw=2875

and sequence length 52. Point D denotes user u719 and dtw=3294 and sequence

length= 36. Sequence similarity and segmentation can be conducted by searching for

optimal alignment, which enables similarity analysis in case of time-lag. During the

analysis of behavior pattern characteristics, the similarity and time-lag of determined

behaviors can be identified. The punctual sequence comparison does not provide such

possibility. Fig. 5 shows alignment for sequences A and B previously analysed, where

one of the sequences is treated as a reference point and constitutes a reference pattern

for comparison of other sequences. Matching pairs of points determine areas of

similarity in time and can be visualised as cost density for sequences A and B, as

shown on Fig. 6.

B C

Fig. 5. Aligning time series A and B. Fig. 6. Cost density for sequences A and B.

Dependencies between sequences can be analysed with asymmetric presentation,

where the lag is determined in relation to a reference sequence. Such a presentation

for sequence A and B, and A and C can be modelled as well. Using this method, the

warping curve can show the direction of changes and lag in relation to the reference

points. Analyses of dependencies of virtual and real time can be used for early

detection of active users, and for estimation of the possibility of conversion on

permanent users who often participate in the service. Depending on the audiences’

specification, the set of pattern sequences can be determined, as well as a set of

informative supports, for which estimation is realized with adequate accuracy.

5 User segmentation based on usage patterns

In the next step we analysed patterns of behavior-analysed users, whose activity was

registered at least during fifty days in relation to the ideal vector. The main goal was

to verify an algorithm based on classification trees in order to forecast users activity

based on distance between virtual time and real time. We performed a division of

users into four classes depending on their activity during the period of our research.

The first class included users, which did not use the platform for maximally ten days

during the analysed period. There were 68 users in this group. The second group

included 71 users with a lack of activity for up to twenty days. The third group

consisted of 115 users with lack of activity for up to 30 days. The fourth group

gathered 144 users who did not use the system during the period in question for up to

40 days. For every user ui, there were current values determined, connected with a

real time R(ui), virtual time V(ui) and the distance D(ui) from the ideal vector. Table 1

presents the fragment of input data for a sample of chosen users

U={u1,u19,u39,u55,u203} with different patterns of system usage during one hundred

virtual days vd1 - vd100. The real time dimension Rd(ui) corresponding to each virtual

day was determined for each user. For example, for user u19 on the second day of

virtual time vd2 the corresponding value of real time was Rd(u19,vd2)=7. Table 1

shows the break in system usage and lack of user’s activity on the {2,3,4,5,6} day of

real time. Distance for this user amounts to 5 because in reference to the ideal vector

the corresponding amount of a real time should amount to 2, and is 7. In the next

period the distance for user u19 does not increase which shows the stable usage of a

system in the following days . Another pattern occurs for user u55 for whom in the

following days the distance gradually increases, which indicates the decrease of

activity in the following days.

Table 1. Selected users from different classes.

Uid Usage Input vd1 vd2 vd3 vd4 vd5 vd6 vd7 vd8 vd9 vd10 C

u1 100% Rd(u1) 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

1.00 D(u1) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

u19 91% Rd(u19) 1.00 7.00 8.00 9.00 10.00 11.00 12.00 13.00 14.00 15.00 1.00



D(u19) 0.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00 5.00

u39 85% Rd(u39) 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

1.00 D(u39) 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00

u55 82% Rd(u55) 5.00 7.00 16.00 19.00 20.00 21.00 22.00 23.00 24.00 25.00

2.00 D(u55) 4.00 5.00 13.00 15.00 15.00 15.00 15.00 15.00 15.00 15.00

u203 62% Rd(u1) 13.00 14.00 15.00 19.00 27.00 28.00 29.00 38.00 47.00 48.00

4.00 D(u1) 12.00 12.00 12.00 15.00 22.00 22.00 22.00 30.00 38.00 38.00

Ideal vector 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00

The aim of our research and analysis presented in this paper, was to determine

whether it is possible to forecast behaviors of users, classify them and allocate them in

a certain group at the initial stage of multiplayer platform usage. For this reason,

classification trees were built based on input data. The analysis was conducted for a

different sequence length relatively to the initial values for ten and twenty days of

service usage. Table 2 presents the classification matrix for the acquired results. In the

case of a short time of analysis concerning the first ten days of activity in relation to

the forecast horizon of the whole analysed period, the analysis is burdened with a big

deviation and most of the users were included in the fourth class. When the analysed

period is extended to 20 days, the accuracy of prediction increased for classes 1, 3,

and 4. The results for the second class of users were burdened with a bigger deviation.

Table 2. Classification matrix for 10 and 20 days sequences.

Period Class Forecast class 1

Forecast class 2 Forecast class 3 Forecast class 4

10 days

1 4.41% 13.24% 11.76% 70.59%

2 0.00% 25.00% 26.39% 48.61%

3 0.00% 8.62% 33.62% 57.76%

4 0.69% 2.78% 14.58% 81.94%

Elements 4 41 87 268

Total % 1.00% 10.25% 21.75% 67.00%

20 days

1 64.71% 5.88% 23.53% 5.88%

2 29.17% 26.39% 23.61% 20.83% 3 27.59% 5.17% 57.76% 9.48%

4 15.97% 7.64% 25.69% 50.69%

Elements 120 40 137 103 Total % 30.00% 10.00% 34.25% 25.75%

Fig. 7 and Fig. 8 illustrate the results of classification in reference to real data for the

initial ten and twenty values of service entries. There is a risk of system usage

decrease among the users from the fourth group. Intervention actions can be

conducted towards them, aiming at the recognition of their needs and determination of

the reason of such behavior. The users from the third group are characterised by a

decreased activity and this group can be subject to more detailed analyses. The users

from the first group are most active and the detailed analyses of their profiles, social

activeness or the realized transactions can constitute the basis for determination of

behaviors and features of a system, which contributes to big activeness.

Fig. 7. Classification accuracy for 10-day

usage patterns.

Fig. 8. Classification accuracy for 20-day

usage patterns.

The proposed solution enables to determine the scope of analysis, which should be

conducted for the individual group of audiences, and to reduce the number of profiles,

which should be a subject to a more accurate analysis. The conducted analysis

indicates the possibility of using the presented approach based on virtual and real time

during the research concerning behavior patterns related to the usage of multiplayer

platforms. The combination of dynamic time warping and classification methods in a

virtual and real time enables the introduction of quantitative measures of similarity in

relation to the ideal vector. The research conducted based on empirical data showed

that in the initial phase of platform usage it is possible to identify behavior patterns,

which indicate the usage of application in the future. The presented approach

supplement the current proposed methods focused on users analysis. It allows the

comparison of sequences in relation to the ideal vector or reference to the behavior of

other users with the system usage patterns. Using this method, the identification of

users that are at risk of stopping service usage is possible. It can help to introduce

appropriate intervention actions to increase activeness and frequency of usage.

6 Summary

In parallel to the increase of Internet systems’ complexness and the increasing

competition in this sector, there is a bigger demand for introduction of new data

analysis methods, and for examining the behavior of users participating in Internet

services. The proper recognition of needs and behavior tendencies provides the basis

for making rational decisions and better adaptation of online services to users’ needs.

High dynamics and changeability of users’ behavior show the need for

implementation of solutions that take time characteristics into account. The solutions

presented so far in the literature did not include two-dimensional approach towards

data character and time series. The approach presented in this paper enables

quantitative estimation of users’ behavior with the use of reference sequences and

users’ segmentation based on time conditioning. The applied methods of dynamic

time warping enable quantitative presentation of similarity including usage patterns.

The presented research proves the possibility of using this approach in different fields

and initiating new research areas focused on recognition of audiences characteristics

with bi-temporal representation.



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