=Paper=
{{Paper
|id=Vol-485/paper-11
|storemode=property
|title=Visualizing Reciprocal and Non-Reciprocal Relationships in an Online Community
|pdfUrl=https://ceur-ws.org/Vol-485/paper11-F.pdf
|volume=Vol-485
|dblpUrl=https://dblp.org/rec/conf/um/Sankaranarayanan09
}}
==Visualizing Reciprocal and Non-Reciprocal Relationships in an Online Community==
https://ceur-ws.org/Vol-485/paper11-F.pdf
Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
Visualizing Reciprocal and Non-Reciprocal
Relationships in an Online Community
Kadhambari Sankaranarayanan, Julita Vassileva
Computer Science Department, University of Saskatchewan
Saskatoon, SK, S7N 5C9 Canada
{kas411,jiv}@mail.usask.ca
Abstract. Online communities thrive on their members’ participation and
contributions. There are numerous ways to visually represent information,
current status, power, and acceptance of members in an online community. In
this paper we present a design of a visualization representing reciprocal and
non-reciprocal relationships among users, which emphasizes and hopefully
triggers common bond in the community. Our future goal is to see whether the
visualization triggers higher participation in an online community called
“WISEtales”, which currently is mostly based on common identity. If our
hypothesis is confirmed, it will present one of the few examples of successful
community whose members associate both by common identity and common
bond.
Keywords: Information visualization, social visualization, visual design.
1 Introduction
Designing a visualization tool for an online community is a great challenge in the
field of visualization research. During its existence an online community produces
huge amount of content and it becomes difficult for the user to navigate and find the
information that they are looking for. It also becomes complex to understand the
evolution and the type of relationships that exist among members. “Social
visualizations are one way to “describe” our online environments and make
interaction patterns and connections salient” [1]. Any visualization should evoke
meaning beyond direct mapping of data otherwise it is said to be misleading. Social
visualizations have some evocative quality [2].
WISETales is an online community for Women in Science and Engineering.
This community has been developed by a graduate student, Zina Sahib, as one of the
projects of the NSERC/Cameco Chair for Women in Science and Engineering for the
Prairies, Dr. Julita Vassileva. This community is specially designed to allow women
who are underrepresented in these areas to share their personal stories. This is a
virtual channel to share emotion, experience and provide support to other women. It
helps women to overcome the generation gap and isolation. Generally women in these
fields are very busy and achieving active participation is a great challenge. So to
motivate their participation is vital for the existence of the community. In order to
overcome this problem, we propose to use a visualization of user relationships that
can motivate users to contribute and reach a critical mass of active users.
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2 Literature Survey
Our research covers the area social comparison and motivational theories in
psychology, organizational theories: common identity and common bond theory, and
social visualization.
Theories of Motivation in Psychology
According to the cognitive evaluation theory there are two motivation systems -
intrinsic and extrinsic - that corresponds to two kinds of motivators. Intrinsic
motivators are: achievement, responsibility and competence, motivators that come
from the actual performance of the task or job, the intrinsic interest of the work.
Intrinsically motivated individuals perform for their own achievement and
satisfaction. Extrinsic motivators are: pay, promotion, status, power, better working
conditions, feedback that comes from a person’s environment, controlled by others
[3].
One of the theories from social psychology that is used to explain human
motivation is the social comparison theory [4]. Social comparison consists of
comparing oneself with others in order to evaluate or to enhance some aspects of the
self. Cognitive and emotional responses to comparison have been extensively studied,
but less is known about the effects of comparison on behavior. There is very little
guidance about how people compare themselves in an online community. Sun and
Vassileva [5] examined the effect of making individual reputation visible in an online
system for sharing research papers and found out that displaying reputation increased
contributions but some users contributed low quality content simply to achieve higher
reputation. A study on the MovieLens movie rating system was conducted [6] by
sending email newsletters to users indicating whether their contributions to the
community were above or below or about average when compared to others which
involved men and women. Women reported being motivated to contribute more
ratings when they were told they had rated approximately the same number of movies
as others and men were motivated to contribute more when they were told they had
rated fewer than others. Members who received a newsletter that encouraged social
comparison rated more movies than other members who received a newsletter which
didn’t encourage social comparison. Upward comparisons were most motivational in
this system. However, introducing social comparison into a community might be
risky. It could work and increase member participation or it might not work and
reduce member’s contributions. Competitive and gaming members like to be
compared with other members, but others may find it discouraging and de motivating.
People who are by nature more competitive (stereotypically, men are believed to be
more competitive than women) are more likely to be motivated by the upward social
comparison condition. It is arguable if women are less competitive, and especially if
women in the science and engineering field are less competitive. They may respond
very well to social comparison. However, in this research we would like to
experiment with creating a visualization that emphasizes relationships, based on the
common bond theory. It is generally considered a bad idea to mix motivations (e.g.
extrinsic and intrinsic motivation) in the same system. Similarly, we fear that mixing
social comparison with common bond may negate each other and it may be
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impossible to observe any change in user participation, or it will be hard to attribute
the change, if there is any.
Common Identity and Common Bond:
Community design affects how people can interact, the information they receive
about one another and the community, and how they can participate in community
activities. There are two theories of group attachments that have been linked to design
decisions on online communities [7]. The common identity theory makes predictions
about the causes and consequences of people’s attachment to the group as a whole
and the common bond theory makes predictions about the causes and consequences of
people’s attachments to individual’s group members.
The causes of common identity are social categorization, interdependence and
intergroup comparison.
Social categorization: it happens when one creates a group identity by defining a
collection of people as members of the same social category [8][9]. Interdependence:
Groups whose members are cooperatively interdependent tend to become committed
to group [7]. Intergroup comparison: People who define and categorize themselves as
members of a group compare themselves with other groups [10] and raising the
salience of out-groups intensifies people’s commitment to their in-groups. The causes
of common bond are social interaction, personal information, and personal attraction
through similarity.
Social interaction: Social interaction provides opportunities for people to get
acquainted, to become familiar with one another, and to build trust. As the frequency
of interaction increases, their liking for one another also increases [11]. Personal
Information: Opportunities for self-disclosure when members exchange personal
revealing information about the self becomes a cause or consequence of interpersonal
bonds [12]. Personal attraction through similarity: People like others who are similar
to them in preferences, attitudes and values, and they are likely to work or interact
with similar others. Similarity can create common identity as well as interpersonal
bonds [7].
Comparison of Common identity and Common bond:
Some identity-based communities shift eventually toward supporting and promoting
interpersonal connections among members. For example, Flickr.com was established
as an online application for photo management and sharing but it later evolved into a
community where people not only share, tag, and comment on photos, but also join
groups and interact in its public and private forums[7].
Bond based communities help newcomers to connect with existing members,
to join group interactions, and to form lasting relationships with a subset of
community members. Bond-based communities care more about people-finding than
information finding, making it easy to find and meet specific members through
directory or personal profile search page [7]. These communities encourage personal
relationships, and their introductory material often encourages participants to post on
a wider range of topics [7]. As compared to common identity, in common bond based
communities newcomers feel isolated and become confused to see off topic
discussions among members. But in our research since all discussions would be based
on members stories, newcomers would be able to understand every part of the
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discussion once the corresponding story is read and it would not be off putting for
them.
Reciprocation
In Common Bond based community people develop relationships with other members
and that is what ties them to the community which cannot be expected from Common
Identity based community. People often help others with the expectation that their
help would be compensated or reciprocated, either by those they have helped or by
the group as a whole [13] [14]. Thus reciprocation can happen at a dyadic or at
community level. In case of common bond there is direct reciprocity and in case of
common identity there is general reciprocity. Social psychologists have found that the
urge to reciprocate is deeply ingrained [15]. Sellers and buyers on eBay usually
reciprocate in their ratings of each other [16] Voting on web sites is sometimes done
in the context of reciprocity [17]: if you rate my story highly I will rate yours highly.
Networks of reciprocity are highly motivating, and encourage participants to maintain
an awareness of the community that surrounds them [18].A community designed on
the basis of common identity is said to be more stable when compared to community
designed on the basis of common bond [7]. This is because, in common bond based
community, if a member leaves the group, the friends associated with that member
would also likely leave the group or become passive. This does not occur in
community designed on the basis of common identity. WISEtales is designed on the
basis of common identity theory, so we can expect that it would be more stable.
Representing relationships in a common identity based community encourages
common bond. As very little research has been done on the coexistence of identity-
based and bond-based attachment, this encourages us explore combining cues that
stimulate both kinds of attachment. According to Milgrams [19] and Zajonc[20],
visually representing people in an online group formed personal attachment to them
even without communicating with each other. Visualization of actual communication
flow among community members can create bond between friends of friends by
helping people fill in gaps [7]. Making contributions visible in a community as a
whole leads to some extent of recognition of the member’s contributions. The nature
of online interaction means that helpful acts are more likely seen by the group as a
whole. The following features encourage reciprocity: ongoing interaction, identity
persistence, and knowledge of previous interactions, since they promote the creation
and importance of reputation within a community. So visualizing reciprocal and non
reciprocal relationships might help members to recognize their current position in the
community.
Social Visualization
Visually representing information enables users to see data in context, observe
patterns and make comparisons [21]. Visualization techniques are important aids in
helping users and researchers understand social and conversation patterns in online
interactions [22]. A data portrait of an online community can give overall information
about each other and the overall social environment [23]. “Social visualization is
defined as the visualization of social data for social purposes” [24]. Social
visualization is a sub category of information visualization. It focuses on people,
groups, conversational patterns, interactions with each other and relationships with
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each other and with their community. Social networks are said to be a form of social
visualization because they have two types of organization patterns namely social
groups and social positions [25]. There are various techniques to represent a group of
people in an online community. Most approaches use nodes to represent individuals
and arcs between the nodes to represent connections between them. Real social
networks have dense interconnections between people.
Vizster is a visualization system for playful end-user exploration, navigation of large-
scale online social networks to increase awareness of the community. Heer et al. [21]
found out by observing through Vizster visualization that groups of users, spurred by
stotytelling of shared memory spent more time in exploring stories and asked deeper
analysis questions than other members. Further Vizster’s visual community analysis
provided help to users who could construct and explore higher-level structures of their
online communities. Visualizations provide not just an analysis tool for social science
researchers. Heer et al. [21], through the “sense.us” visualization for group
exploration of demographic data found out that combining conversation and visual
data analysis helps people to explore data broadly and deeply. When visualizing
conversations, it should evoke an appropriate intuitive response to represent the feel
of the conversation as well as depict its dynamics [26]. Coterie, a visualization tool
for Internet Relay Chat (IRC) shows the activity of the participants and also the
structure of conversation. It highlights active participants and conveys the vitality of
discussion [26]. PeopleGarden is a visualization tool for representing member’s
participation on a message board. It uses flower and garden metaphor. From this
anyone can easily perceive an individual’s active role or long-time lurker [26]. The
Loom Project is an evocative semantic visualization for Usenet newsgroups. It is used
to depict the leaders and provocateurs. There are people who post frequently and are
often replied to in a positive way. This visualization distinguishes them from other
frequent posters such as trolls (deliberate troublemakers), automatic newsfeeds, and
the excessively verbose [26]. IBlogVis [27] is a visualization tool for browsing blog
archives. It provides an overview of posted blog articles over time with their length
and number of comments received to help users to find the interesting articles in the
blog at a glance and to ease exploration and navigation. Social network visualization
for blogspace revealed that topic-oriented blogs had more interconnections and
reciprocation than most popular blogs [28]. Webster and Vassileva [29] explored in
the context of a discussion forum, if a visualization of the reciprocity of a user’s
relationships with other users would motivate the user to engage in more reciprocal
relationships and showed that it indeed does so for active members, though it doesn’t
increase the level of participation in general. Chin and Chingel’s [30] visualization for
blogspace show links for suggesting a social relationship among the bloggers. Social
visualization is expected to activate social norms of behavior, encourage social
comparison and reciprocity. According to Vassileva and Sun [5] motivational
visualization effectively increased awareness of community and encouraged social
comparison and as a result contribution to the community increased. We propose to
incorporate a motivational visualization to increase participation by stimulating social
bond among members and evoking reciprocity among between pairs of users, as well
as a gentle social comparison in terms of number of reciprocated relationships.
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3 Proposed approach
To achieve the goal of increasing active participation, we propose designing a system
which incorporates visualization techniques to motivate user participation by evolving
their relationships with other members in the online community.
Motivation
Our hypothesis is that an appropriately designed visualization can stimulate
motivational and organizational mechanisms that lead to more active contributions by
users to their community. Our approach is to encourage intrinsic motivation
(according to the cognitive evaluation theory from psychology) and the common bond
theory (from organizational studies). The objective of our research would be to model
the evolution of relationships based on data from user interactions, for example
reading and writing stories or giving comments and to design a visualization of these
relationships which will serve as a tool to motivate users to contribute more towards
their group. Our visualization would display these relationships between users so that
it would be easy for the user to understand his/her current position in the community.
We have chosen the WISEtales community as a test bed for our approach. In
bond-based community people engage in direct reciprocity. So the visualization will
reveal which are reciprocal and non reciprocal relationships. Reciprocity increases
when members interact repeatedly. People help others with the expectation of having
their help returned by that individual or the group as a whole [13] [14]. Returning
favors is are acts of reciprocation. Yet it is not clear if being aware of the reciprocity
of their relationship, and the direction of non-reciprocal relationships (who “owes”
favors to whom) will motivate users to reciprocate more frequently and thus
contribute more. This is what we would like to test. In this community, reciprocation
happens when a member reads a story or post comments to a story submitted by
someone else. Other actions, such as posting a story to one’s Facebook profile,
forwarding it to a friend or checking the story, author’s profile may also be considered
as acts of reciprocation.
Visualization Design
To make the visualization more likeable for women, a flower garden metaphor is used
(see Figure 1). Each user is represented in circular node with his/her name written in
it. The node is surrounded by arcs (visualized as leafs) corresponding to relationships
with other users. Each arc (leaf) has the corresponding user names and different color
to indicate reciprocal and non reciprocal relationships. The stronger and thicker the
color then the reciprocation is said to have happened between the users. This helps the
users to understand how many reciprocal and non reciprocal relationships they and
the other users are involved in. The node of the viewer will be highlighted among the
other circular nodes, so that he / she can compare his/her relationships with those of
the other users. If a user has received lot of comments from a particular user and has
not been aware of that before, the visualization will make him/her realize that he/she
“owes” that user some attention, and that he/she needs to contribute something to the
other user. Also the realization that other users are viewing the same visualization and
will be aware of the lack of reciprocation from the user to others will add social
pressure to behave according to community norms (a form of social comparison).
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Thus a social visualization showing the users’ relationships with other users
could be motivational, if users become aware about the number and balance of their
reciprocal and non reciprocal relationships with other users expressed through the
visual effects. They would get an overall idea about the other members’ contributions,
would be interested to read stories contributed by active members, post comments and
also spread the word about interesting stories. The visualization will be dynamic – it
will change when new members sign in and when new comments are given and
reciprocal actions performed. The visualization is intuitive but not interactive since
previous research by [5] showed that interactive features were rarely used. It is not
customizable by the users.
One can see in Figure 1 that there are three distinct colors used to represent reciprocal
and non reciprocal relationships. The more petals a flower has, the more the active the
member is. The dark green color leaf is used to represent reciprocation among users;
the medium green color leaf is used to represent comments received from other users
and the light green color leaf is used to represent comments given to other users.
Viewers perceive colors differently but experimental evidence shows that
relationships between colors are universal and are free from individual and cultural
differences [31]. According to [31], “People can make consistent evaluation of the
magnitude of any given experience of colors based on the type of interaction among
colors. People respond to the relationship among colors”. The colors chosen for this
visualization are of analogous ordering. Such kind of ordering is more lively than
monochrome and is stable in arrangement than non analogous ordering or
complementary parings. Each member is represented as circular node in brown color.
The person who is engaged in most reciprocal relationships is placed in the center and
other members are placed surrounding it. According to [32] “Varying shapes of nodes
is used to denote different characteristics of members in the graph; the location of the
node is used to denote the valuable marker for understanding the structure in the
network. Centrality in a group is a useful indicator that the participant plays a key role
in the group [33]. Each leaf has a rounded and a very sharp edge. The sharp edge is
placed outside and is rotated to point to the direction of the corresponding individual’s
node whose name is mentioned on the respective leaf (along the arc connecting the
nodes representing the users). The reason is to give an easy navigation and sense of
direction for the user to find their relationship partners in the visualization.
Reciprocation between two members is currently calculated by the number
of views and comments to each other’s story. For example, in Figure 1 it can be seen
that Karthik’s node is placed in the center as it has a higher number of reciprocated
relationships when compared to other nodes. The members with fewer reciprocated
relationships are placed surrounding the central person. The other members with very
few relationships are placed in the outer circle. All nodes in the graph are created
using concentric circle algorithm. Placing the leaves in the corresponding direction of
the node is not a trivial task. It is done by using some rotation measures and graphics
algorithm to generate the graph.
This visualization does not include any connection lines between nodes.
“The fewer the number of lines crossing, the better the sociogram” [32]. This is
because lines between nodes increases complexity and decreases the beauty of the
visualization. The visualization comes with a key to help users indentify which colors
represent which type of relationship.
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Figure 1: Visualization of reciprocal and non-reciprocal relationships for logged in
members of WISETales.
Implementation
The technology used to design the visualization is Flash and Flare. Flare is mainly
used for web content visualization and is highly scalable. WISETales is built on
Drupal, PHP and MySql technologies. Flash can easily integrate with PHP and
MySql. A link to the visualization will be implemented in WISETales website. As
soon as the member of WISETales website logs in he/she would be able to click on
the link to visualization to see it. In the visualization, the area of the corresponding
member who is currently viewing the visualization would be highlighted in pink to
show his/her current position in the group. Also when they click on their node all the
nodes and leaves that are related to them representing reciprocal and non-reciprocal
relationships would also be highlighted in the visualization. User of the visualization
can also click on the particular flower to scale to get the information of a particular
person clearly.
Prototype Evaluation
A medium fidelity prototype of the visualization using Flash was developed and
tested to assess whether the visualization of reciprocal and non-reciprocal relationship
conveys the correct information to the user, whether they were able to understand the
visualization clearly. The evaluation tool used for the medium fidelity prototype was a
questionnaire. The question type used were Scalar-Likert scale because it measures
opinions, attitudes and beliefs. Each question asks the user to judge a specific
statement on a numeric scale with extremes 4 –indicating agreement and 1 –
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indicating disagreement with a statement. I also used open questions to get specific
answers and to give room for user suggestions. The questionnaire was implemented
using the SurveyMonkey.com tool.
The representative users for the evaluation of the medium fidelity prototype
were 12 graduate students from our MADMUC lab at the University of
Saskatchewan. The link to the prototype as it ran on a server and a link to the
questionnaire were sent to each participant in an email. The most serious concerns
users had were related to the scaling of the visualization, as can be seen in Figure 2.
We need to work on the scalability, perhaps through creating fish-eye views or a
magnifying glass effect.
Figure 2: Results of the evaluation of the visualization prototype.
Future evaluation of the visualization
Our hypothesis is that visualizing reciprocal relationships would increase the users
understanding of their community, will encourage common bond and will ultimately
increase participation. We chose to evaluate the effect of proposed visualization in
WISEtales by using three different versions (two control versions and experimental
version) of the community with two different groups of users. Fifteen members would
participate in each version. The experimental version would have the proposed
visualization. The first control version will have no visualization and the second
control version will have a different visualization (one developed by Zina Sahib) and
based on common identity theory, showing only the type of contributions, not the
users. All members would be given a period of one month to use the community with
their respective version. In the next two months, the groups will rotate their versions,
so that each group gets exposure with each version. The contributions from members
in experimental version and members in control version and their reciprocal
relationship with other members would be collected and analyzed. A questionnaire
will also be used to collect qualitative data about the users understanding of the
structure of the community, the importance of individuals in it; as well as their
feelings of attachment to particular individual or the community as a whole.
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4 Conclusions
We propose to use a motivational visualization aimed at encouraging common bond
in a common identity based community and see the effects on user contributions. We
want to test if particular visualization design, showing how users are engaged in
reciprocal and non-reciprocal relationships with each other could stimulate
reciprocation and motivate higher user participation. If our hypothesis turns to be true
this may provide empirical evidence about the possibility of successful and stable co-
existence of common identity based community and common bond based community
within one group.
References
1. Karahalios, K.G 2006 ‘Social Visualization: Exploring Text, Audio, and Video Interaction’.
CHI April 22-27 ACM.
2. Boyd, D., Lee, H-Y, Ramage,D. and Donath, J. Developing legible visualizations for online
social spaces. In Proceedings of the Hawaii International Conference on System Sciences.
(Jan. 7-10, 2002, Big Island, Hawaii).
3. Theories of Motivation. http://www.analytictech.com/mb021/motivation.htm
4. Festinger, L. A Theory of Social Comparison. Human Relations, 7, 1954.
5. Vassileva,J and Sun,L.(2007) Using Community Visualization to Stimulate Participation in
Online Communities. E-Service Journal, 6 (1), 3-40. (special Issue on Groupware).
6. Harper, M.F., Frankowski, D., Drenner, S., Ren, Y., Kiesler, S., Terveen, L., Kraut, R.,
Riedl, J Talk Amongst Yourselves: Inviting Users To Participate In Online Conversations,
2007 IUI, Jan 28-31, ACM.
7. Ren ,Y., Kraut, R., and Kiesler, S. 2007. Applying Common Identity and Bond Theory to
Design of Online Communities. ACM. Syst. 15, 5 (Nov. 1993), 795-825.
DOI:10.1177/0170840607076007, http://oss.sagepub.com/cgi/content/abstract/28/3/377
8. Turner, J.C. 1985 ‘Social categorization and the self-concept: A social cognitive theory of
group behavior’ in Advances in group processes: Theory and research, Vol. 2. E. J. Lawler
(ed), 77-122. Greenwich, CT:JAI Press.
9. Turner, J. C., M.A.Hogg, P.J. Oakes, S.D.Reicher, and M.S. Wetherell 1987 Rediscovering
the social group: A self-categorization theory. Oxford; Blackwell.
10. Tu, M. A., and D. J. Terry 2000 ‘Social identity and selfcategorization processes in
organizational context’. Academy of Management Review 25/1: 121–140.
11. Cartwright, D., and A. Zander 1953 ‘Group cohesiveness: Introduction’ in Group
dynamics: Research and theory. D. Cartwright and A. Zander (eds). Evanston, IL: Row
Peterson.
12. Collins, N. L., and L. C. Miller 1994 ‘Self-disclosure and liking: A metaanalytic review’.
Psychological Bulletin 116/3: 457–475.
13. Blau, P.M. 1964 Exchange and power in social life. New York: John Wiley.
14. Emerson, R.M. 1972 ‘Exchange theory: A psychological basis for social exchange’ in
Sociological theories in progress (Vol. 2,pp. 38-87). J. Berger, M. Zelditch and B. Anderson
(eds). Boston: Houghton Mifflin.
15. Cialdini, R. B. Influence: Science and practice (4th ed.). Boston: Allyn & Bacon. 2001.
16. Resnick,P and Zeckhauser,R., Trust Among Strangers in Internet Transactions: Empirical
Analysis of eBay’s Reputation System (2002). The Economics of the Internet and
Amsterdam, Elsevier Science. Pp. 127-157.
108
Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009
17. Dellarocas, C. N., Fan, M. N. and Wood, C. A., Self-Interest, Reciprocity, and Participation
in Online Reputation Systems (February 2004). MIT Sloan Working Papers No. 4500-04.
18. Sadlon, E., Sakamoto, Y., Dever, H. J., Nickerson, J. V. (2008). The Karma of Digg:
Reciprocity in Online Social Networks In Proceedings of the 18th Annual Workshop on
Information Technologies and Systems.
19. Milgram,S. 1997. ‘The familiar stranger: An aspect of urban anonymity’ in The individual
in a social world: Essays and experiments. S. Milgram (ed.), 51-53 Reading, MA: Addison-
Wesley.
20. Zajonc, R. B. !986 ‘ Attitudinal effects of mere exposure’. Journal of Personality and Social
Psychology 9/2, PT. 2: 1-2
21. Heer,J., Viegas, F.B and Wattenberg, M. 2009 ‘Voyagers and Voyeurs: Supporting
Asynchronous Collaborative Visualization’. Proceeding of the SIGCHI Conference on
Human Factors in Computing Systems, April 2007. VOL. 52, Communications of the ACM.
22. Viegas,F.B., danah boyd, Nguyen, D.H., Potter, J., and Donath,J. Digital artifacts for
remembering and storytelling:PostHistory and social network fragments. In Proceedings of
Hawaii International Conference on System Sciences (HICCSS) (2004), 105-111.
23. Xiong, R and Donath,J (1999). “PeopleGarden: Creating Data Portraits for Users” MIT
Media Laboratory, Cambridge ACM.
24. Karahalios K.G and Viegas F.B. “Social Visualization: Exploring Text, Audio, and Video
Interaction” CHI 2006, ACM.
25. Freeman, L.C., Visualizing Social Networks. Journal of Social Structure, 2000.
26. Donath,J.2002 ’A Semantic Approach to Visualizing Online Conversations’ (Vol. 45).
Communications of the ACM.
27. Indratmo, Vassileva J and Gutwin C, 2008 ‘Exploring Blog Archives with Interactive
Visualization’, ACM (May 28-30).
28. Herring, S.C., Kouper, I., Paolillo, J.C., Scheidt, L.A., Tyworth, M., Welsch, P., Wright, E.,
and Yu, N. Conversations in the blogosphere: an analysis “from the bottom up.” In Proc.
HICSS-38, 2005
29. Webster,A and Vassileva,J ., Visible Relations in Online Communities, in Adaptive
Hypermedia and Adaptive Web-Based Systems, Dublin, Ireland, Springer LNCS 4018, 223-
233.
30. Chin, A. and Chignell, M. A social hypertext model for finding community in blogs. In
Proc. HYPERTEXT, 11-22, 2006.
31. Jacobson, N and Bender, W. “Color as a determined communication”, IBM Systems
Journal, VOL 35, NOS 3&4, 1996.
32. Moreno, J.L. Who Shall Survive? Beacon, NY: Beacon House, Inc. 1953.
33. S.Wasserman and K.Faust. Social Network Analysis. Cambridge University Press, 1994.
109