=Paper=
{{Paper
|id=Vol-307/paper-7
|storemode=property
|title=Visualizing Social Bookmarks
|pdfUrl=https://ceur-ws.org/Vol-307/paper07.pdf
|volume=Vol-307
|authors=Joris Klerkx,Erik Duval
}}
==Visualizing Social Bookmarks==
https://ceur-ws.org/Vol-307/paper07.pdf
Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning & Exchange
Visualizing Social Bookmarks
Joris Klerkx1, Erik Duval1
1
Departement Computerwetenschappen, Katholieke Universiteit Leuven,
Celestijnenlaan 200A, B-3000 Leuven, Belgium
{joris.klerkx,erik.duval}@cs.kuleuven.be
Abstract. Social bookmarking tools are very popular nowadays. In most tools,
users tag the bookmarks to describe them. Therefore, it is often hard for users to
discover implicit structures between tags, users and bookmarks. We think that
this is essential for both end users to discover new bookmarks that could be of
interest to them, and for researchers who want to study how people use social
information retrieval tools. In this work, a cluster map visualization application
is customized to enable users to explore social bookmarks in the del.icio.us
system [5]. The design of the application aims to automatically identify tag and
community structures, and visualizes these structures in order to increase the
users’ awareness of them.
Keywords: Information visualization techniques, social bookmarking tools,
folksonomies, tags, cluster map.
1 Introduction
The ability to store or bookmark web page addresses has been one of the most
important features of browsers since the beginning of the Web. Social bookmarking
tools became possible when the process migrated from keeping the bookmarks on the
client, to keeping them online on the Web, described by tags or terms. The purpose of
social bookmarking tools is to tag the content of other users, mainly for the benefit of
the tagger, although the bookmarks and tags are generally public, and users can
establish networking opportunities [1]. The personal approach of tagging is an
unstructured bottom-up approach of classifying content, in contrast to a top-down
structured approach based on taxonomies, thesauri or ontologies. The semantic
structures that result from a tagging approach are often referred to as a folksonomy.
The problem with tags is that they generally produce a flat namespace, rather than
the hierarchical structures that taxonomies or other formal classification systems
provide [1]. However, there can be rich implicit structures between tags, bookmarks
and users. We designed a system that attempts to visualize these structures, so that
end users can explore the social bookmarks in a playful, efficient and flexible way.
There are a number of social bookmarking tools, like CiteULike [2], Furl [3],
BlogMarks [4], etc. Del.icio.us [5] is probably the most well-known social
bookmarking tool, designed to store and share bookmarks on the web instead of
saving in the browser. We chose del.icio.us as a source of data for our visualization
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Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning & Exchange
tool as it has many users, lots of data and a very easy API to access this data.
Visualizing data of other social bookmarking tools can be done easily in the future.
We start this paper with a detailed overview of the design of our visualization in
section 2. Section 3 describes the underlying technology. A short overview of related
work is given in section 4 and we conclude the paper in section 5.
Fig. 1. A visualization of the del.icio.us social bookmarking tool.
2 A Cluster Map of Del.icio.us
We designed a visualization application for the social bookmarks in del.icio.us.
First we discuss the design requirements in section 2.1. In section 2.2, we look at the
data we use for the application. The customized cluster map visualization is discussed
in section 2.3 and we conclude with a typical exploration use case in section 2.4.
2.1 Requirements
Our application for browsing social bookmarks was designed with a number of
requirements in mind. First of all, we wanted to use and evaluate a novel paradigm for
browsing social bookmarks, compared to the hypertext paradigm that is used on the
del.icio.us website. Secondly, we wanted the system to be able to automatically
identify tag and community structures. Such a structure is formed when two or more
tags or users respectively describe or share common bookmarks. After the
identification of these structures, they must be visualized for the users, so that they
can become aware of them. We think that this is essential for both end users to
discover new bookmarks that could be of interest to them, and for researchers who
want to study how people use social information retrieval tools. On top of this, users
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Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning & Exchange
should be able to explore the social bookmarks in a playful manner in a fun and
engaging space.
2.2 Data
In order to visualize the content of del.icio.us, we have to collect the data first. We
start from one or more del.icio.us user names and collect their bookmarks and the tags
that describe them. Del.icio.us enables users to add other users to their network, so
that they can access the bookmarks of these users. In the second step, we collect the
tags and bookmarks for all the users that are in the network of the users identified in
step 1. In a third step, the same data is collected for all the fans of the chosen user(s)
in step 1. User A ‘is a fan of’ user B if user A has added user B to his network. This
relation is not necessarily bi-directional.
During the exploration of this initial set of data, end users can ask the system to
expand the data with the tags, bookmarks and network of users of their choice.
2.3 Cluster Map Visualization
Fig. 1 shows the user interface of our del.icio.us visualization: it consists of three
panes – a tree structure that shows the different users and tags in the currently loaded
data, a cluster map visualization [7] and a search pane with integrated results list. All
three panes are synchronized with each other. We discuss these panes in detail in the
next paragraphs.
(a) (b)
Fig. 2(a) Tree view of the tags that describe the bookmarks.
(b) Result view showing detailed metadata about a bookmark.
Tree Structure. This structure presents an overview of the tags and the users that are
currently loaded in the system. Fig. 2a shows that each tag is presented as a node in
the tree and the number of bookmarks, that are described with the tag, is indicated. In
the design of this tool, we choose to follow the philosophy of “start with what you
know, then grow” [6]. This means that, by default, nothing is visualized in the cluster
map. After the checkbox in the tree structure is selected, the corresponding tag or user
is visualized together with its bookmarks. In this way, the initial visualization carries
less perceptual and computational burden to start with.
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Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning & Exchange
Fig. 3. Cluster Map showing 2 users with 100 bookmarks and 6 of them in common.
Cluster Map Visualization. For the visual presentation of the social bookmarks in
del.icio.us, we customized an existing visualization technique [7]. A bookmark is
represented as a small circle in the visualization. Each bookmark belongs to the
collection of one or more users. In Fig. 3, two users are shown, each having hundred
bookmarks in their collection. Those two users have six bookmarks in common. This
is represented in the visualization by the smaller common cluster of bookmarks in the
middle. By using the tree structure pane that was described in the previous paragraph,
users can select which users and tags are drawn on the cluster map.
Fig. 4. Cluster Map Visualization, showing 200 bookmarks with 2 users and 1 tag ‘email’.
Bookmarks can be clustered by the users that have them in their collection, or by
the tags that describe them. This can be seen in Fig. 4, where the tag ‘email’ is shown.
Users ‘lisamac’ and ‘jgarber’ have one bookmark in common which is tagged by
‘email’. The user ‘jgarber’ has 2 extra bookmarks tagged with ‘email’ that are not in
the collection of the other user. In Fig. 4., two colors are used to represent bookmarks
– yellow and blue. The blue color means that the bookmark belongs to a selected set
of bookmarks. Such a set can be created by clicking on a user or a tag in the tree pane,
or by performing a keyword query in the search box. In Fig. 4, the blue bookmarks
are also tagged with the tag ‘webdesign’ which is used to create a selection by
clicking on this tag in the tree pane (Fig. 2a). The blue bookmark between ‘lisamac’
and ‘email’ is therefore in the collection of the users ‘lisamac’ and ‘jgarber’ and is
described by the tags ‘email’ and ‘webdesign’.
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Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning & Exchange
Result list. This list, shown in Fig. 2b shows detailed metadata about the bookmarks
that are either selected in the cluster map, or bookmarks that match the search terms
when a query was performed. The metadata cover the title, the location, user(s) that
added the bookmark and tag(s) that describe the bookmark. A user can interact with
the detailed information by clicking on e.g. a tag that describes a bookmark. If this tag
is not already drawn on the cluster map, the visualization automatically updates itself
and the tag classification is shown.
2.4 Typical Use: Exploring Social Bookmarks
As an alternative to exploring social bookmarks on the del.icio.us webpage, we
offer a novel access paradigm that enables a user to explore social bookmarks in a
playful manner in a fun and engaging space. There are a number of ways that users
can start the exploration of the social bookmark space. First of all, a user can start
from an egocentric point-of-view, with a visualization of his bookmarks, much along
the lines of the philosophy of “start with what you know, then grow” [6]. The user can
select a number of bookmarks in the visualization or perform a keyword query after
which detailed metadata about the resulting bookmarks is shown in the result list (Fig.
2b). The metadata contain all the tags that describe the bookmarks, i.e. not only the
tags of the user itself. The user can click on those tags and by doing this add them to
the cluster map, where the layout of the bookmarks updates itself to represent the new
sub-clusters, like in the example of Fig 4.
A second way of exploring is browsing the tree structure to find interesting tags to
visualize. Users can order the tag tree alphabetically or by the number of bookmarks
they describe. By adding tags to the visualization, the corresponding bookmarks,
possibly tagged by different users, can be explored in the cluster map.
Users can also interact with the visualization itself by clicking on nodes and
expanding these nodes and the visualization with new information. Upon clicking a
bookmark, the user can choose to show all associated tags. Depending on the choice,
the network and the fan data of all the users that saved this bookmark are loaded into
the system so that they become available for exploring. The reason that we do not add
the data of all users that saved a bookmark automatically from the start is the usage
throttling and abuse monitoring software at the del.icio.us website [8].
A last way of exploring del.icio.us social bookmarks with our tool is to enter one
or more user names in a dialog box. All the data of those users is then loaded in the
system and ready for exploring.
3 Underlying Technology
The tool for visually searching and analyzing del.icio.us social bookmarks is created
with the open and extensible information visualization framework that we created as
part of our research on the use of information visualization techniques for flexible and
efficient access to learning repositories [9]. We want to make it easy to add new
visualization techniques as well as new data sets, possibly delivered in various
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Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning & Exchange
formats and structured according to various metadata schemes. This was a
requirement because we wanted to reuse this framework in different contexts.
Visualization techniques like tree-maps, hyperbolic trees, node-graphs, fisheye-views,
etc. are supported by the framework by plugging in existing visualization components
into our framework.
For the visualization of the del.icio.us bookmarks, we plugged the Aduna Cluster
Map software [10] into our framework. The Aduna Cluster Map software is a library
that contains functionality for creating visualizations of collections of hierarchically
classified objects. By integrating this library in our framework, the Aduna
visualization technique is available for the application, described in this paper, but
also for other case studies developed with our framework. Moreover, this integration
demonstrates the open and extensible nature of our framework.
With our framework, we can now also visualize the bookmarks as e.g. a tree-map
where the bookmarks are classified per user (Fig. 5), in a flexible and efficient
manner, or with any of the other techniques that our framework provides.
Fig. 5. Tree Map Visualization: blue rectangles represent the users, yellow rectangles
represent the bookmarks that match the selection made by the keyword ‘web’ in the search
box and gray rectangles are bookmarks that are not selected.
4 Related Work
There a quite a few social bookmarking initiatives, like CiteULike [2], Connotea
[11], del.icio.us [5], Furl [3], BlogMarks [4], etc. A thorough general review on a
number of these initiatives can be found in [1]. Tag Clouds are normally used to
visualize the tag structures of one or more users. Many visualizations of tag clouds
were created for del.icio.us. Notable such visualizations include HubLog [12] that
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Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning & Exchange
enables graphical browsing of del.icio.us tags in a mind mapping way. Extisp.icio.us
Text [13] provides a random textual scattering of user tags, sized according to the
number of times that they've been used. Revealicious [14] is a set of 2D graphic
visualizations that enables the user to browse, search and select tags and bookmarks.
Vizster [6] is a tool that is designed for visualizing the online social network
Friendster, as a browseable network of social relations. Vizster is very useful for
sociological research but does not take tag structures into account.
All these initiatives are based on either visualizing the tag structures or the
community structures where in our design both structures are taken into account for
the visualization, so as to make more apparent implicit community and tag structures.
5 Conclusion
In this paper, we presented our design of a visualization application for social
bookmarks. Our design tries to visualize implicit structures between tags, users and
bookmarks. We customized a cluster map visualization technique for this purpose.
The social bookmark tool del.icio.us was chosen as the source for the data to be
visualized.
In the future, we will extend the system with data of other social bookmarking
tools. In this way, users can explore social bookmarks from multiple tools in our
application. Other possible data sources are folksonomies like Flickr [15] where
photographs are described by tags. Validating the usefulness and the effectiveness of
our design for visualizing del.icio.us is very important. Therefore, we will proceed by
gathering user data of the application. We will gather this data by integrating the
Contextualized Attention Metadata (CAM) framework [16] into our information
visualization framework. CAM can be used to capture the attention a user spends on
content in an application. We will analyze this data, as well as the data of usability
tests we plan to do, to validate the effectiveness and usefulness of our design.
Acknowledgments. We gratefully acknowledge the financial support of the
K.U.Leuven research council through the BALO project, the Interdisciplinary
Institute for Broadband Technology (IBBT) through the Acknowledge project, and
the European Commission through the ProLearn Network of Excellence on
Professional Learning.
References
1. Hammond, T., Hannay, T., Lund, B, Scott, J. Social Bookmarking Tools (I): A General
Review. D-Lib Magazine, 11(4), April 2005, http://dx.doi.org/10.1045/april2005-
hammond
2. CiteULike. http://www.citeulike.org
3. Furl, http://www.furl.net
4. BlogMarks, http://www.blogmarks.net
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Proceedings of the 1st Workshop on Social Information Retrieval for Technology-Enhanced Learning & Exchange
5. Del.icio.us, http://del.icio.us
6. Heer, J. and Boyd, D. 2005. Vizster: Visualizing Online Social Networks. In Proceedings
of the Proceedings of the 2005 IEEE Symposium on information Visualization (October
23 - 25, 2005). INFOVIS. IEEE Computer Society, Washington, DC, 5.
7. Fluit, C., van Harmelen, F., Sabou, M., Ontology-based Information Visualization:
Towards Semantic Web Applications, in Visualising the Semantic Web (2nd edition),
2005, Springer Verlag.
8. Del.icio.us abuse monitor: http://del.icio.us/help/json/url
9. Klerkx, J., Meire, M., Ternier, S., Verbert, K., Duval, E., “Information Visualization:
Towards an Extensible Framework for Accessing Learning Object Repositories”, Proc.
ED-Media’05, AACE, Montreal, Canada (2005), pp. 4281-4287
10. Aduna ClusterMap Library:
http://www.aduna-software.org/projects/display/CLUSTERMAP/
11. Connotea: http://www.connotea.org/
12. HubLog: http://hublog.hubmed.org/archives/001049.html
13. Extisp.icio.us: http://kevan.org/extispicious
14. Revealicious: http://www.ivy.fr/revealicious/
15. Flickr: http://www.flickr.com
16. Wolpers, M., Martin, G., Najjar, J. and Duval, E., Attention Metadata in Knowledge and
Learning Management, Proc. IKNOW‘06, Graz, Austria.
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