COMMUNITY ASSOCIATION MAP

Processing Inter-community Relationships

arlinton B. Feres Carvalho, Hugo Fuks and Carlos Jos

e Pereira de Lucena

Department of Computer Science, Pontiﬁcal Catholic University of Rio de Janeiro (PUC-Rio)

Rua Marqu

es de S

ao Vicente 225, Rio de Janeiro, Brazil

Keywords:

User Interest Analysis, Online Community Analysis, Social Media Analysis, Social Media.

Abstract:

The use of modern communication technology like the Internet brings a new perspective for the study of

society. Following this paradigm shift, this paper presents a process to help in the analysis of social media

available on the Internet, specially the analysis focused in the online community’s content. The process goal

is to reveal the interests of users through a map of associated communities. The user membership is utilized to

establish the relationship among communities. This map shows the interests of members in other communities.

An example illustrates the application of the process. A discussion about the opportunities and drawbacks of

the process usage is also presented.

1 INTRODUCTION

With more than two billion users

, the Internet has

become the mainstream channel to study society. The

content available in social media is useful for many

different kinds of applications, from academia to busi-

ness purposes. For instance, the analysis of the dis-

cussion presented in online communities could be

a promising way to study healthcare topics (Paton

et al., 2011; Bender et al., 2011).

In the literature, publications can be found ana-

lyzing the content available on the Internet, devoting

special attention to social network analysis. How-

ever, recent studies are questioning the overrating

of current social network studies and looking for a

broader analysis of the content available in social me-

dia (Kwak et al., 2010; Cormode et al., 2010). This

work improves on the existing body of work devoted

to the content analysis of online communities (Preece

and Maloney-Krichmar, 2005), providing information

of users’ interests based on their associations within

their communities that helps to characterize the pop-

ulation involved in the community under analysis.

The process proposed in this paper focuses on user

membership within communities. Membership could

be seen as the user interest in the community topic.

The process relies on a model of related communities

based on users’ interest. A plotted graph reveals the

http://www.internetworldstats.com/stats.htm (Novem-

ber 2011)

strongest afﬁnities by weight of the connecting lines

linking communities. These graphs help experts in

discovering trends that will further their understand-

ing in speciﬁc social matters. Furthermore, experts

can apply ﬁlters on the data set to choose which users

and communities are interesting for the analysis. The

process application is limited to situations in which

users are also members of other communities.

The remainder of this paper is organized as fol-

lows. The methodology, with the epistemology con-

sidered on the process creation, is presented in Sec-

tion 2. Section 3 presents the proposed process. The

application of the process is illustrated by an exam-

ple in Section 4. A discussion about the opportunities

and drawbacks of the process application concludes

this paper in the Section 5.

2 METHODOLOGY

The study of social media has its roots in social sci-

ence. However, the use of modern communication

technology like the Internet brings a new perspective

for the study of society (Lazer et al., 2009). Follow-

ing this paradigm shift, this paper introduces a pro-

cess to exploit the content available on the sites that

support communities in order to reveal users’ prefer-

ences. This section presents the epistemology consid-

ered on the process development.

Social networking sites have spread over the Inter-

665

B. Feres Carvalho D., Fuks H. and José Pereira de Lucena C..

COMMUNITY ASSOCIATION MAP - Processing Inter-community Relationships.

DOI: 10.5220/0003913006650670

In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 665-670

ISBN: 978-989-8565-08-2

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

net, bringing a new perspective for communication.

Systems like Orkut and Facebook empower people

to manage their social networks and engage in online

community discussion on topics of their interest. The

data publicly available might be useful to allow dif-

ferent kinds of research and applications.

Research over content available on the Internet

used to give special attention to social network anal-

ysis. In the literature, there are studies that attempt to

characterize the users’ population of important sites,

such as Facebook (Nazir et al., 2008), Twitter (Hu-

berman et al., 2008) and YouTube (Cheng et al.,

2008). The mainstream theory used to analyze so-

cial networks is based on complex networks stud-

ies (da F. Costa et al., 2007). Other studies aim

to understand the users on social networks, so one

can identify special users on these networks and pre-

dict the network evolution and its implications (Ben-

evenuto et al., 2009; Wilson et al., 2009; Ahmed et al.,

2010; Bigonha et al., 2010).

Recent studies are questioning the overrating of

current social network studies and looking for a

broader analysis of the content available in social me-

dia (Kwak et al., 2010; Cormode et al., 2010). An

extensive discussion on modeling and measurement

of social media is presented in the paper by Cormode

et al. (Cormode et al., 2010). This paper presents a

process to help in the analysis of social media pre-

sented in sites that support communities. The focus

is to reveal users’ interests by exploring their com-

munity associations. A community association map

is built from the communities’ membership informa-

tion, and allows the visualization of the most related

communities within a deﬁned boundary. Due to the

kind of processed data, advanced statistics of this data

can lead to a misleading analysis, therefore it is not

covered in this work (Krishnamurthy and Willinger,

2008). Although similar approaches are found in lit-

erature, such as the feature presented on Orkut system

to provide up to nine related communities at a com-

munity page (Chen et al., 2009), not all data required

by other methods are available to be used. Moreover,

experts require more suitable tools for helping them in

the analysis of social media, such as the one presented

in this paper.

Models and measurements help experts to analyze

social media, which also considers other elements as-

sociated with their study. A key point for measure-

ment considered in the process development is the use

of appealing visualization for its outcome. To achieve

this goal, the process relies on a software package for

visualizing data and information. The study case that

motivated the process development is available in the

technical report (Carvalho and Lucena, 2010).

3 THE PROCESS CAM

The process has three steps: data gathering, model

and measurement, and visualization. The ﬁrst step,

data gathering, obtains the social media data from on-

line community sites. Next is the model and measure-

ment step, as discussed in the previous section. The

collected data is processed and organized in an ap-

propriated format to plot the data visualization. The

visualization step is responsible for displaying data in

a more appealing way, easing the work of the experts.

Details of the steps to build the Community Associa-

tion Map (CAM) are presented as follows.

1 ) Data Gathering

Collect data from an online community site (social

media data source). The collection step requires ac-

quiring all user information from an isolated com-

munity of interest. Then, for each user, membership

information for other communities is obtained. The

gathered data set is a collection of users, communi-

ties, and the relationships among them.

2 ) Model and Measurement

Create the model of communities’ relationships. In

a formal notation, if user

is a member of commu-

nity cmm

and cmm

then cmm

has a relationship

with cmm

. If user

is also a member of cmm

and

cmm

, then the relationship is reinforced (+1). There-

fore, applying this rule over a given list of users and

their membership information builds the model with

the weighted relationship among the communities.

The most important measurement obtained from

this model is a list of the top relationships within the

communities (strongest ties), which means the com-

munities with more users in common. This measure

is exposed through the community association map,

which is displayed in the next step, plot.

Depending on the data available, the model size

can cause problems in the process application. To

overcome eventual processing limitations, there are

ﬁlters for the communities considered in the model.

For instance, one can choose to model only relation-

ships among communities with more than 10 mem-

bers. Applying this ﬁlter, the model will keep its

features, because the modeling goal is to identify the

most related communities.

Filters can also be applied to change the focus of

the measurement, for instance, taking into account

only communities of some speciﬁc category. An ex-

ample of ﬁltering is in Section 4.

3 ) Plot the Visualization

The ﬁnal step is to plot the community association

map. Given that the map is used to reveal the most in-

teresting associations among communities, it is useful

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Figure 1: The process that generates the Community Association Map.

to limit the number of communities presented in the

ﬁnal graph.

The outcome of this process is to support experts

in the study of social media. For this purpose, one can

use Circos, a software package for visualizing data

and information. Circos plots graphs that visualize

data in a circular layout – making it ideal for explor-

ing relationships between objects or positions (Krzy-

winsk et al., 2009). Other visualization tools can also

be used.

Figure 1 gives a glance at the whole process ap-

plication. At the top of the image is the three steps

of the process in a sequence. Bellow the steps, cor-

responding with each one, there is a diagram with the

data ﬂow from the social media source to the ﬁnal plot

of the community association map. In between, there

are agents representing the software that manipulates

the data, transforming it into suitable formats. An ex-

ample of the process application is presented in the

next section.

4 EXAMPLE OF APPLICATION

The process application is illustrated by an example

used in a real life study about Hepatitis C, a medical

condition deﬁned by the inﬂammation of the liver and

characterized by the presence of inﬂammatory cells in

the tissue of the organ

. This study analyzed the dis-

course presented in an online community discussion

that has Hepatitis C as its main theme and involves

other subjects related to the disease. The Commu-

nity Association Map was part of the study unveling

the interest of the users that posted messages in the

community forum. As a result, the Community Asso-

ciation Map shows the interests of active users in the

community topic reinforcing the discourse analysis.

The process implementation is split over many in-

http://en.wikipedia.org/wiki/Hepatitis

teractions, executed by different programs. Scripts

implemented in Lua process the data, while the

iMacros script executor on Internet Explorer Browser

automates Orkut’s system access. The ﬁnal step of vi-

sualization plotting is done by a program specialized

for this purpose (i.e. Circos).

The studied community was the Hepatite

C (Portuguese for Hepatitis C), which had

around 1.2 thousand members in March 2010

and more than 2.100 days of existence. The

community URL is http://www.orkut.com.br/

Main#Community?cmm=216788. The process is

applied as described in the previous section consid-

ering active members of the Hepatitis C community.

Figure 2 illustrates the whole process application.

The full process execution took two weeks, mainly

because of the data gathering step.

1 ) Data Gathering

The application considered Orkut communities,

which were chosen mainly because of the experts’ in-

terests. The data acquisition is scraping-based due

to Orkut system limitation (no API for this purpose

is available). The data acquisition was an exhaustive

crawling within a deﬁned boundary, having a starting

point in a community of interest.

Instead of taking all members, the process con-

sidered as input only users that posted messages on

the community forum (467 active users). From these

users, the crawler was able to retrieve 48,218 associ-

ations with 35,954 communities.

2 ) Model and Measurement

Most of the 35,954 communities have few users as-

sociated with them. The use of these communities

in the model only adds an extra effort in the model

processing. A ﬁlter to remove the communities with

less than 20 users is applied to build a more concise

model with 30 communities. The communities’ re-

lationships model is built based on a combination of

these communities.

COMMUNITYASSOCIATIONMAP-ProcessingInter-communityRelationships

667

Figure 2: Details of the process application example.

3 ) Plot the Visualization

In the ﬁnal step of the process, graph plotting, the last

ﬁlter is important to provide a smooth visualization.

A plot with the 15 communities related to the ana-

lyzed community has 120 weighted relationships. It

is the combination of 16 communities (15 + analyzed

community).

Figure 3 shows the plot for the model considering

the top 15 communities related to the Hepatite C com-

munity. The image is built using a special component

of Circos, the Table Viewer

. Table Viewer has many

conﬁguration variables, and it must be customized for

better visualization according to each expert’s prefer-

ences. The last step is completed in two phases, ﬁrst

generating the input ﬁle for Table Viewer and then ex-

ecuting it.

The communities are displayed in the graph fol-

lowing clockwise orientation in descending order of

the relationship weight with the analyzed commu-

nity (Hepatite C). The connection lines among com-

munities show the relationship weight by thickness

http://mkweb.bcgsc.ca/circos/presentations/articles/

vis tables2/

and transparency. Broad and opaque relationships are

stronger than narrow and translucent ones.

The plot showed in Figure 3 reveals the interests

of active users in other communities related to hepati-

tis, meaning that they are users looking for informa-

tion and support in other communities related to the

disease as well. The names of related communities

are in Portuguese because this is the language used in

the Hepatite C community.

It is important to remember that the Community

Association Map (CAM) is more interesting when

compared to other research artifacts, as part of the

whole content analysis (Kozinets, 2009). However, it

is interesting to note that most of the associated com-

munities are related to Hepatitis C, showing the strong

interest of the active users in discussing the disease.

The Orkut environment has all kinds of commu-

nities and, even without weighting the relationships

among the communities considering their size, the

graph shows that the users are engaged with others

communities regarding the topic of interest. The trend

showed in the graph is the user interest in other com-

munities related to the disease. In this case, no special

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Figure 3: Community Association Map for Hepatite C (ID 216788) community.

interest stand out of the associated communities re-

lated with brands. Two other hot topics are Religion,

also identiﬁed in the discourse analysis as an impor-

tant matter of the treatment, and Games, considered

by far the most popular application in social network-

ing sites. Therefore, it reinforces the validity of the

discourse analysis of this community content in the

sense of getting a broader perception of what its user

population says and seeks.

5 DISCUSSION

A promising way to study social media is to focus

on the analysis of the online communities’ discus-

sions (Kozinets, 2009). Identifying the users’ inter-

ests in these online communities is an important part

of this study. Specialists can apply the process pre-

sented in this paper to reveal these interests and search

for textual clues in the discussions to corroborate their

ﬁndings, or vice-versa. The process generates an el-

ement of analysis that shows the interests of users

through a map of associated communities as part of

a study of social media.

The process application is limited to situations in

which users are also members of other communities.

Consequently, there must exist many other communi-

ties being hosted by the same social media site of the

community under analysis, or a way to identify links

between users from the communities under analysis to

other communities in different social media sites. For

instance, the SIOC (Semantically-Interlinked Online

Communities), an initiative that aims to create and

leverage a layer of semantic data in online communi-

ties (Breslin et al., 2006), could be used to map these

communities over different sites.

If the number of community members is avail-

able, one can modify the model to consider a nor-

malized relationship weighting. The relationship with

very large communities would show a weaker re-

lationship than with smaller communities, because

they have lesser proportional membership overlap.

This approach is not considered in this work because

the community size information is not a reliable in

the Orkut’s system, as detailed in the technical re-

port (Carvalho and Lucena, 2010). Nevertheless, the

use of ﬁlters can alleviate misrepresentations.

The advantage of the process is to provide extra

information about interests of users of an online com-

munity. Although it is up to the experts to choose

which users and communities are interesting for the

analysis, there are several ﬁlters like those shown in

the previous section. The aforementioned example

gives clues of how one can place ﬁlters on the data set

to reveal speciﬁc interests, such as considering only

active users that are more relevant to the study.

According to Cormode et al. (Cormode et al.,

2010), the process is characterized as follows. The

data collection can be retrieved by connecting to the

social media site API, or scraping-based when the

API is not available, which was the case in the exam-

COMMUNITYASSOCIATIONMAP-ProcessingInter-communityRelationships

669

ples described in this paper. The sampling methodol-

ogy is based on an exhaustive crawling within a de-

ﬁned boundary, with starting point in a community

of interest. The measurement efforts focus on show-

ing the weighted relationships among the communi-

ties through a map. An evaluation of process results

is achieved by comparing them to other elements of

analysis (e.g. discourse analysis).

A considerable step for advancing this work is

to minimize process application restrictions. Instead

of relying on explicit user membership information,

other ways of community detection like ﬁnding inter-

ests on exchanged messages could be applied to deter-

mine community association data. In the same line of

reasoning adding text-mining capabilities could fur-

ther the extraction of more information from social

media. Finally, this process could be added as a com-

ponent on a social media analysis platform.

ACKNOWLEDGEMENTS

The authors would like to thank Rodrigo Pazzini

for his expert social media skills. This work has

been sponsored by CNPq (Brazilian Council for

Research and Development) – Bolsa de Doutorado

CNPq 142620/2009-2.

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