Design Patterns on Social Navigation

HCDE 505 | Final paper | Ru-ping, Kuo

Design Patterns on Social Navigation
Ru-Ping, Kuo | ruping@uw.edu

Introduction

The nature of hypertext is to make a website offering infinite ways of navigation to the visitors. True, the
goals among these visitors are diverse; however, no one wants to risk getting lost in an information space because
they come here either to get information or accomplish tasks. In addition, there are plenty of other web sites
available; if visitors get lost at a web site or encounter a difficulty on navigation, they leave (Nielsen, 2003). As
the result, web navigation design is continuously received significant attention over the past few decades. Many
design principles, guidelines, and patterns are proposed in order to make a website capable or successful
supporting these different information needs synchronously. While the ongoing growth of the World Wide Web
(WWW), at the beginning of 2000s the term “Web 2.0” was introduced to characterize some emerging trends in
internet use such as “user-generated content (UGC)”, “social networking sites(SNS)”, “folksonomy” and
“masups”. Although this change makes Tim Bemers-Lee’s vision of the web: “a collaborative medium, a place
where we [could] all meet and read and write”1 becomes more accomplishable to all internet users, creating a
good web navigation system also becomes a real problem to designers because of this change.

In general, a good navigation design means that “people will be able to find, access, and use the
information and services they need ‘efficiently’” (Kalbach, 2007). Today, more and more users come to a website
are not only for “accessing” information but also for “creating” content and “sharing” them to others; these social
interactions and two-way communications challenge the traditional navigation design strategies. As a website
must become a “place” where can support and potentially foster social interaction, many researchers (e.g.
Dieberger, 1997; Forsberg, Höök, and Svensson, 1998; Höök, Benyon, and Munro, 2003; Dourish, 2003;
Svensson, Höök, and Coster, 2005) believe that social navigation plays an essential role to fulfill this need. For
example, Svensson (2002) highlights two advantaged properties of social navigation: “dynamical” and
“personalized”. Both attributes are reflecting a “bottom-up” fashion of navigation design, which stress that users
get to the right information by using “community wisdom” (Brusilovsky, 2008) rather than following the
designer’s blueprint. According to Farzan (2009), the advantages of social navigation include: 1) help users filter
out relevant information and decide where to go next; 2) add social texture by presenting how other users react to

1
Tim Berners-Lee is the inventor of the World Wide Web. According to Wikipedia, this quotation is summarized from an interview (BBC News. 2005-08-09).
Information is retrieved from http://en.wikipedia.org/wiki/Web_2.0#cite_note-5,
1


the different content; 3) make information space more inviting by adding social affordance; 4) provide users more
control to reshape the information space.

Hence, in recent years, social navigation has been applied to solve a diverse set of problems from website
navigation to social software design (Wu and Bowles, 2010). However, a clear picture of social navigation
systems in practice on the web is still unavailable; as a result, web designers struggle to take advantage of social
navigation. Meanwhile, many designers make good use of “design patterns library” when they encounter some
similar problems because design patterns describe good solutions to recurring design problems in contexts
(Pauwels, Hübscher, Bargas-Avila, and Opwis, 2010). With the development of the WWW, more and more
successful examples supporting that the social navigation as design thinking can supplement the former strategies
of navigation design. Therefore, I believe now will be the best time to look for answers of these questions: “How
social navigation systems are used on the web? What kind of websites particularly need to adopt social navigation?
What kind of social navigation systems is used to solve what kind of problems? “ I also believe that the web
design community will be beneficial by giving this primary study of design patterns on social navigation.

Social Navigation

The idea of social navigation is defined by Dourish and Chalmers in 1994; they consider social navigation
as “navigation towards a cluster of people” or “navigation because other people have looked at something”
(Höök, Benyon, and Munro, 2003). Couple years later, Dieberger (1997) widened the applicative scope of social
navigation by introducing other web applications such as the earlier form of “social bookmarking” (sharing
favorite sites at personal webpages). The grounding of social navigation is based on that “information about
others and about others’ activities can be beneficial to an individual in the conduct of his or her activity. (Dourish,
2003)” In another words, social navigation helps users by visualizing footprints of other users and adding social
affordance about presence of others in an information space (Chalmers, Dieberger, Höök, and Rudström, 2004).

Social navigation is a navigational activity and it is composed of at least five elements: a starting-point, a
destination, the navigating agent, the route, and other agents with whom the navigating agent interacts. The
interaction between the navigating agent and other agents is the key to make social navigation “social” (Forsberg,
1998) and differ from the other types of navigation. Social navigation doesn’t have a narrow focus; Höök, Benyon,
and Munro (2003) suggest that social navigation can be seen from several different perspectives and in several
different domains. Many works and studies are covered five major areas: Human-computer interaction (HCI),
Computer-Supported Cooperative Work (CSCW), Collaborative virtual environments (CVEs), Intelligent User
Interfaces (IUI), and Information Retrieval (IR) (Höök, Benyon, and Munro, 2003; Xu, Kreijns, and Hu, 2006).
Farzan (2009) propsed the taxonomy of social navigation (see Figure 1) to illustrate the broad application of
2


social navigation. The most popular way to classify social navigation is based on the types of communication :
direct and indirect and it is proposed by Diberger Figure 1 Taxonomy of Social Navigation in Information Space

(2000). The direct social navigation is often
considered as synchronous and direct interaction
between information provider and receiver
(Farzan, 2009), but there are cases when it is
based on asynchronous communication (Svensson,
2002). On the contrary, indirect social navigation
focuses on aggregated history informaiton (Farzan,
2009). According to Svensson (2002), two
common techniques for indirect social navigation
are “collaborative filtering” and “history-enriched environments”.

Social Navigation on the Web

The advent of UGC and SNS makes today’s web space becomes more and more crowded with
information and users. This type of modem websites demonstrate the “power of the collective” by allowing
people to share experience and tacit knowledge, to make recommendations; it also enable people to get to know
other people and seek out affiliation, companionship and support from others (Girgensohn and Lee, 2002).
Furthermore, this overwhelming power not only expands the production of Web content, but also ameliorates the
ways of information access because designers have taken the advantages of social navigation systems. As
discussion above, social navigation can help users get to the right information using “community wisdom” which
is distilled from tracked actions of those who interact with this information earlier. Although both direct and
indirect social navigation systems can be applied in a web environment, the indirect and asynchronous approach is
more common in web applications. For example, one of the most well-known social navigation applications is the
book recommending mechanism at Amazon.com (Chang and Wang, 2011). More recently, the other types of
social navigation systems like social tagging and bookmarking system are also become familiar to internet users
because of some representative and successful websites such as Flickr.com and Dellicious.com (Brusilovsky,
2008).

Since this study intends to review and evaluate how social navigation are used on the web by examining
popular design patterns of social navigation closely and carefully, giving an overview of these applications is
necessary. Thus, several common and web-based social navigation systems are discussed as follows.

3


Recommendation System
“Collaborative filtering” is the foundation stone of recommendation system. It also is one of earlier forms of
social navigation systems. Collaborative filtering systems assume that people who agree on some things will
likely agree on others, the more two people have in common, the more likely that one will like what the
another one likes (Farkas, 2007). In this process, early users leave information clues that help later users make
sense of the wealth of alternatives available to them (Konstan and Riedl, 2003). According to Konstan and
Riedl (2012), today the mainstream of recommendation model is called “the personalized collaborative
recommender”. This type of recommender is adopted by many popular websites: Amazon, Netflix, Last.fm,
and Facebook’s friend suggestions. Take Amazon.com as an example, the store radically changes based on
customer interests. Moreover, rather than matching the user to similar customers, the “item-to-item”
collaborative filtering matches each of the user’s purchased and rated items to similar items, then combines
those similar items into a recommendation list (Linden, Smith, and York, 2003). However, it should be noted
that current studies of recommender systems mostly focused on the techniques and computer algorithms to
produce the right content of the recommendations, rather than the usability and user-related issues (Ozok, Fan
and Norcio, 2010). Farkas (2007) points out that the systems based on the mathematical algorithms giving
recommendation could be questionable, for example, just because one buys a book does not mean that he or
she is actually happy with it; users might buy things for others; and the suggestions can also be misled when
one account is used to choose items for more than one person.

Comments (Customer Reviews) and Rating Systems
Comments and ratings are the other well-known types of social navigation systems in present websites.
Unlike the recommendation system collects data implicitly and without interrupt users, both rating and review
systems are examples of explicit feedbacks which are provided by voluntary users. Accompanying the user
rating and review system is widely adopt in many E-commerce (EC) sites, many researches has begun to
investigate the influence of user-generated reviews on other consumers’ purchase decisions. Walther et al.,
(2012) summarized some important findings by carefully reviewing prior related studies, for instance,
consumers apparently regard user-generated reviews as more trustworthy than traditional advertising
information (Huang, Chou, and Lan, 2007); source characteristics associated with the author of a product
review shape consumers’ perceptions of products (Forman, Ghose, and Wiesenfeld, 2006); and a variety of
message features also affect responses (Dellarocas et al., 2005), such as valence, argument density, argument
diversity, and the writer’s expertise claims. Moreover, compare to giving personal comments, the rating
system is brief and it only requires minimal effort to users. A study shows that a rating without including a

4


comment may not be of great value for an end user, however, ratings provide the most reliable information for
collaborative filtering and social navigation (Brusilovsky et al., 2010)

Tagging Systems and Social Bookmarking
The other notable mechanism of social navigation is the tag cloud. A tag cloud is composed of many
keywords or terms (text links). The font-size of keywords is weighted by frequency (popularity). Kalbach
(2007) claims that tag clouds are good for presenting content dynamically. Moreover, because tagging
systems allow users to collectively classify information with their own words, they are also known as
“folksonomy”. Although the social tagging has some drawbacks such as people can tag an item in many
different ways (Farkas, 2007), it is applied at many different type of websites and content such as Del.icio.us
for bookmarks, Flickr for images, Technorati for blog posts, CiteULike for academic papers, or LibraryThing
for books (Chang and Wang, 2011). In addition, the tag cloud is not the only form of tagging systems. Some
other forms like “Navigating tags” or “Geotagging (a type of machine tag)2” are also used on the Web 2.0
sites frequently. According to Smith (2008), there are seven potential benefits for websites and organizations
that adopt tagging systems: 1) facilitating collaboration; 2) obtaining descriptive metadata; 3) enhancing
fundability; 4) increasing participation; 5) identifying web usage patterns; 6) augmenting existing
classification efforts; 7) sparking innovation.

Summary: Design Challenges of Social Navigation

Although social navigation provides a promising opportunity to turn information space into a more
sociable place, social navigation is not a concept that can be un-problematically translated into ready-made tools
and apply to an existing system or a web space directly (Konstan and Riedl, 2003). Social navigation requires
designers to think differently about the nature of people and their activities with communication technologies and
environment (Höök, Benyon, and Munro, 2003). Svensson (2002) suggests several design questions that need to
be investigated by designers in order to successfully implement social navigation: “How actions should be
communicated between advice providers and advice seekers? Is there any privacy and trust issues concerning
providers? ”; “How will presence of others and their actions be mediated to advice seekers?”; “How social
navigation fits into the overall system?”

Since the concept of social navigation is new and widely applied in different type of computer and
information environments, the universal evaluation criteria of social navigation still fall short of consensus.

2
Unlike other tags, Geotags required a particular kind of structure metadata – geographic coordinates – to place resources on a map (Smith, 2008). According to Wikipedia,
machine tag uses a special syntax to define extra semantic information about the tag, making it easier or more meaningful for interpretation by a computer program.
5


However, many challenges and influential factors regarding to this matter are introduced by some researchers (e.g.
Svensson, 2002; Svensson and Höök, 2003; Farzan, 2009), they are summarized as follows.

1. Social affordance (also known as the awareness of others and their actions): Users can learn what
appropriate behaviors are if they can see the behavior of others. As the result, this awareness make
users feel the web space is alive and more inviting.
2. Bootstrapping: Tthis issue also is named as “cold start” problem (Farzan, 2009). Social navigation
systems often rely on the accumulated user behavior and feedback; as the result, early users will not
have many social navigational aids. Hence, a very important challenge in developing social
navigation systems is how to get the system started.
3. Drifts of interest: Over time, the interest of people and the importance of information are changed.
Therefore, taking into consideration that different types of information have different expiration dates
is important. Farzan (2009) proposes several solutions address this problem in her research paper, for
instance, weighing more recent visits, and providing social navigation support based on the data from
a specific period of time.
4. Snowball effects: Social navigation takes advantage of “collective wisdom”. However, a side effect
occurs when more and more people walk down the “wrong” path because it will be indicated as a
“preferred” path in a typical social navigation system. The snowball effect especially harms systems
that rely mostly on implicit feedback from users. Farzan (2009) suggest that combining several types
of implicit feedback can partially deal with this problem.
5. Privacy: This issue is raised because social navigation systems strongly rely on the visibility of
people and their activities. Different people have different concerns about their privacy. Meanwhile,
interpersonal trust also plays a significant role on social behavior of the system. Therefore, how the
trust-privacy tradeoff can be optimized is the other needed consideration.

Last, among these challengers, “social affordance” is the most common and essential factor that
influences social navigation design (Svensson, 2002). Many successful social navigation systems aimed at making
users aware the social presence by providing “representations” of fellow participants and their activities (Cosley,
Ludford, and Terveen, 2003). Erickson and Kellogg (2000 & 2003) developed a concept of “social translucent” to
explain why and how a CSCW system will be benefited by revealing and visualizing the presence and activity of
users in digital environments. Moreover, there are three properties make a system socially translucent: 1)
visibility: a system should allow users to see the presence and activity of others; 2) awareness: users should aware
that others can also see what they are doing when they can see others are doing; 3) accountability: shared
awareness leads to user accountability. It encourages users to follow social rules and norms since they know what

6


others can notice their actions (Vassileva, 2009). Hence, in order to fulfill the need of social affordance, these
properties of social translucent should also be took into account when design a social navigation system.

Design Patterns: Design Solution in a Context

Design is a problem solving process. Ideally, design solutions should originate from an explicit
understanding of users, tasks and environments. However, in reality, designers may be forced to make decision
based on their experience, knowledge and best-guess sometime. Under this kind of circumstance, design patterns
as well as design guidelines are widely used to help designers communicating and evaluating design solutions
according to the relevant problem in its context. Thus, in recent years, the notion of design patterns has received
considerable attention in web design. Van Duyne, Landay, and Hong (2007) claim that Design patterns solve
recurring design problems, so designers can use pattern solutions to design their websites without reinventing the
wheel.

A typical design pattern describes the problem, the chosen solution, the rationale behind that solution, and
related patterns that the designer should be aware of (Spool, 2003; Van Duyne, Landay, and Hong, 2007).
Therefore, design patterns help a design team or an organization formalizing design knowledge and record best
practices in the context. The other benefits of design patterns also includes: 1) reduce design time and effort on
new projects; 2) improve the quality of design osolutions; 3) Facilitate communication between designers and
programmers; 4) Educate designers (Cooper, Reimann, and Cronin, 2007) because accordign to Alexander (1979)
who is the concept of design patterns developer, design patterns were desinged to give non-professionals the
power to create good design as well (Pauwels, Hübscher, Bargas-Avila, and Opwis, 2010).

My study plans to make good use of design patterns and adopt “pattern language” as the evaluate
framework to examine how social navigation are used on the web today. On the other hand, integrating multiple
related patterns into a pattern library brings higher value on design process (Pauwels, Hübscher, Bargas-Avila,
and Opwis, 2010). Many web pattern libraries have been published either as books or online (e.g. Van Duyne,
Landay, and Hong, 2007; Yahoo! Inc. 20063) but social navigation patterns are still lacking in. The finding of this
study might complete the absence of these patterns to the pre-exist pattern libraries in someway.

3
Yahoo! Design Pattern Library http://developer.yahoo.com/ypatterns/
7


Methods: Collecting and Evaluating Design Patterns of Social Navigation

As discussed above, the essential value of design patterns Table 1 summary of study websites

is that patterns give proven and workable solutions. In another
words, design patterns are not derived from theory but identified
as invariant aspects of solutions that emerge as best practices
(Pauwels, Hübscher, Bargas-Avila, and Opwis, 2010). However,
to identify these invariant design patterns from millions websites
for evaluating with limited resource is the mission impossible to
my study. In order to ensure the quality of my study subjects
(websites), I choose to generate a list of Top 50 sites in U.S from
Alexa.com4 and then screen out websites that do not meet the
criterion (subjects must use at least one type of social navigation
systems: recommendation, tagging, rating and review). As the
result, total 14 websites are selected (see Table 1 for summary).
Among them, 31% is EC sites, 38% is UGC sites, 19% is SNS
(n =14)
sites, and 13% is others. It’s clear that these site genres confirm
to prior researchers’ suggestions which were discussed in the earlier sections. In sum, certain types of websites
such as Web 2.0 sites and EC sites are more depend on social navigation systems than other types of websites.

Discussion: Social Navigation Patterns

Site Genre Patterns
Each “site genre” has similar content, goals, and users; therefore, Van Duyne, Landay, and Hong (2007)
suggest the need to identifying the “site genre patterns” at first because this type of patterns is high-level and
describing general properties and characteristics of certain type of sites.” Accordingly, I found that different
type of websites adopts design patterns of social navigation systems differently. The difference between EC
sites and UGC sites is distinct; meanwhile, SNS sites genre patterns share some common traits with UGC sits.

1. EC sites (& Netflix.com)

4
Alexa.com provides traffic data, global rankings and other information on thousands of websites, and claims that 6 million people visit its website monthly.
http://en.wikipedia.org/wiki/Alexa_Internet
8


EC sites promise to make online shopping experience is easier and more enjoyable (Van Duyne, Landay,
and Hong, 2007). Moreover, the business goals of an EC site are encouraging visitors to stay longer and
browse more items. As the result, the recommendation systems become a magical solution fulfill the
needs. The recommender plays as a salesclerk or a shopping consultant in a virtual store. More
importantly, this sales assistant has amazing memory. She is not only able to remember the specific
location of every item in the store but also know everything about customers and their shopping
preference and history. She is an expert on almost everything, too. She could suggest things that
customers might like or even not aware they might need from many different aspects. Hence, EC sites
provide various recommendations at the same time, for instance, “customers who viewed/bought this also
viewed/bought…”; “frequently bought together”; “what other items do customers buy after viewing this
item”. Moreover, compare to UGC and SNS sites, EC sites’ rating systems are more polished and join
with review systems together since trustworthiness plays an important influence on online purchase
decisions. In some cases, users even can give their rating and comments to sellers as well as products.
There is no any EC site adopts tagging system in my study.

2. UGC sites
UGC sites use social navigation systems differently because they face different challenges. First, almost
every UGC site takes advantage of tagging systems because the tagging enhances findability and makes
the information structure more flexible, extensible, and aggregatable (Smith, 2008). However, in order to
reduce the bootstrapping effect, the “multiple ways to navigate pattern5” is adopted, too. For example,
Flickr.com uses Geotagging system adding location tag upon users’ photo and allows users exploring
photos by place, date, and even camera brand (these are all “machine tags’). Moreover, rating system is
more easy to use. Besides the form of rating starts is replaced by one “favorite” button (or “like” and
“dislike” buttons), it also works independently (rating and comment are two individual systems in
general). Last, compare to EC sites, UGC sites’ recommendation systems are usually implicit, simple
(some are shown as text links) and single.
3. SNS sites
Unlike the other two types of websites’ social navigation systems are more focus on things (products,
photos, articles etc.), SNS sites concentrate on human and how to promote social awareness through
social navigation systems. Many forms of social navigation systems are altered in order to fulfill the
needs, for instance rating, re-post/re-pin (adapt from the comment systems), and follow. In short, the
design patterns of SNS sites’ rating and review systems are close to UGC sites than EC sites. Moreover,

5
See Van Duyne, Landay, and Hong’s (2007) book for detail. pp. 216-220
9


the role and patterns of recommendation system in SNS sites are diverse. For example, the
recommendation pattern in linkedin.com is similar to other genres but pininterst.com push content to
users’ personalized home page according to a group of interests. Moreover, unlike UGC sites and EC sites,
the recommender is designed as a component of web pages, pininterest.com put recommendations design
patterns on the whole start page in order to create “dynamical” and “personalized” user experience to the
members.

Recommendation Patterns
The keystone of recommendation pattern is using “collaborative filtering” approaches to build an algorithmic
model based on users’ past behavior. However, from a user point of view, no matter how the mathematical
algorithms giving recommendation in behind, the whole point is that weather these recommendatory items are
related to his or her interest? Therefore, the goal of this pattern is to predict and satisfy every user’s unique
needs based on collective wisdom.
[Problems] The recommendation pattern is usually used to solve these problems or fulfill these needs: 1) When visitors
have no clear or specific ideas about what they need, or what other things they can find from this website; 2) To
encourage visitors stay longer, see more content (items), or buy more items; 3) Provide alternative ways to navigate the
website; 4) Enhance visibility and fundability of content (and/or users); 5) Provide alternative ways to compare items; 6)
Save visitors’ time and effort on browsing or navigating; 7) Provide personalized service to visitors. 8) Want to make
good use of community wisdom. 9) When a website has a huge collection of content and the content is continuously
growing. In addition, a website can constantly refine and improve the information structure and user experience design
by aggregating and analyzing these use data.
[Solutions] Use multiple sources to generate the recommendatory list (e.g. Amazon’s item-to-item” collaborative
filtering model) instead of using single and purely inferred data to make recommendations. The form and placement of
recommendation patterns can be various, but each recommendatory item should include its photo and some important
information (depends on the purpose of the recommendation). If possible, use clear label address how is the
recommendation generated from (e.g. personal browsing history, other users’ activities, similar topics, popularity etc.).
SNS or UGC sites should consider including provider’s (creator’s) handle name for enhancing social awareness.
Moreover, provides other useful information such as date, rating, and popularity (e.g. total views) for helping visitors
filter out most attractive item. By doing so, “the snowball effects” might be reduced, too. Finally, if the placement of
advertising is apposition to recommendation systems, make sure users are able to recognize advertisements different
from recommendations.

Users Comments/Reviews and Rating Patterns
Researches show that people read “user reviews” in order to weigh their decision when shopping online (e.g.
Huang, Chou, & Lan, 2007; Mudambi and Schuff, 2010). On the other hand, many SNS and UGC sites
provide comments system in order to support social interaction and encourage users participated in
community. These patterns might play different roles on different types of websites but their goal is alike.

10


These patterns are used to support two-way communication. In the end, the website become more alive and
appeal to visitors by providing social affordance.
[Problems] The user comments/reviews and rating patterns are usually used to solve these problems or fulfill these
needs: 1) To encourage participations and social interaction; 2) To encourage visitors stay longer, see more content
(items), or buy more items; 3) Create an environment where cultivate rich and various voices (opinions); 4) Want to
make good use of community wisdom; 5) Provide alternative ways to prioritize content (items); 6) Enhance credibility; 7)
Provide convenient ways for users to share the opinions; 8) Help users make better decision; 9) Enhance social
awareness.
[Solutions] Provide a ship to multiple addresses action button and make they are obvious and easy to use. For example,
Amazon.com actively invites and reminds customers provide feedback from many ways. Although people usually enjoy
sharing their ideas, they might also unwilling to share if it required too much efforts. Therefore, the feedback systems
should be easy to learn, easy to use, and give clear instruction. Websites also should provide different tools (required
different levels of effort) for users to share their opinions. Take Amazon.com as an example, users can write a customer
review and give the rating (require login), press “like” button (no login required), or comment other users’ comment. As
soon as users provide their opinion, the system should give clear feedbacks to users. The systems also should provide
ways for users tracking, monitoring, or even revoking their sharing (both comments and ratings). Create an enjoyable
and interesting sharing experience for users in order to encourage their sharing behavior. More importantly, try to make
these patterns as social translucent as possible if the design does not against users’ privacy concerns.

Tagging Patterns
Although the usefulness of some tagging patterns such as tag clouds is still under debated, the concept of
“folksonomy” makes tagging system stand out from the other types of navigation in website design.
Smith (2008) describes tagging system as “people-powered metadata for the social web”. The unique
advantage of tagging patterns is it allows users to organize and labeling information with their own way
although tags also can be created by machine automatically (by defining semantic information).
[Problems] The tagging patterns are usually used to solve these problems or fulfill these needs: 1) When visitors have
no clear or specific ideas about what they need, or what other things they can find from this website; 2) To encourage
visitors stay longer, see more content; 3) Enhance social awareness; 4) Provide alternative ways for users to organize and
navigate content (bottom-up); 5) To obtain and aggregate metadata from users point of views; 6) Made good use of
community wisdom; 7) Provide alternative ways to prioritize content; 8) Help users to find and interact others users who
have similar interests; 9) When a website has a huge collection of content and the content is continuously growing; 10)
Enhance visibility and fundability of content (and/or users); 11) When content are generated by users; 12) To create a
semantic web.
[Solutions] Tag clouds are a powerful solution to present all tags associate with a subject or with the whole website.
Usually a tag cloud presents tags alphabetically and then enlarges the tags proportionally based on popularity. It
provides more information cues than the general hierarchical structure of information. It also is a good way to show users
a visual representation about a virtual community’s most general activities. However, there are many forms of tagging
systems (tag clouds, navigation tags, Geotagging, etc.) and it is important to provide multiple navigating ways to users in
order to reduce some negative effects of social navigation such as “drifts of interest” or “bootstrapping”. Flikr.com

11


presenting many great and successful tagging patterns that other UGC and SNS sites can imitate. In addition, Smith
(2008) introduce several ways to motive user tagging, for instance, help users use tags expressing themselves or
managing personal information and social information.

Conclusion
Although social navigation has been proposed as a means to make information space becomes more
social and easier to navigate (e.g. Dieberger, 1997; Höök, Benyon, and Munro, 2003; Dourish, 2003), a lack of
common and interpretive frameworks makes it difficult to unpack and adopt to design process effectively. But at
the same time, there are plentiful well-known and successful examples on the web demonstrating the capability of
social navigation systems. Thus, this study first carefully investigates and discusses related concepts of social
navigation from the literatures review, and then identified three popular social navigation systems:
recommendation, ratting and reviews, and tagging which are widely applied on the web. Next, I take “design
patterns” as the study approach; carefully examined many popular websites in order to reveal the current status of
social navigation in practice.

The results of my empirical study confirm scholars’ expectation of social navigation. It’s clear that certain
websites like SNS, UGC and EC sites rely on social navigation systems than other types of websites. This
tendency provides evidences supporting that social navigation can supplement the emerging need of social
interaction on the social software. It also suggests that the social navigation provides the solutions for designer to
handle the rapid growing and unpredictable content which are generated by users. Unlike traditional navigation
tend to focus on “efficiency of information retrieval”, social navigation is flexible to use on different information
navigate needs. For instance, recommendation systems help users either explore more items or find the item they
need quickly because this system can predate users’ information needs. Furthermore, my finding also discoveres
that the unique character of social navigation allows designers use it in other application such as “re-pin” and
“follow”. Both “re-pin” and “follow” are familiar function to most SNS users although these two functions are
usually not considers as kind of social navigation patterns in general.

Finally, there are several limitations of my study should be mentioned. First, my study only examined 14
websites because of the limitation of resource. I expect that more different patterns of social navigation can be
discovered if I reviewed more websites. Moreover, in general, a design pattern should suggest other relative
patterns in order to make it more useful. However this part does not include because my study intent to examine
best practice of social navigation systems on the web and depict them with pattern language framework rather
than develop a complete set of design patterns of social navigation.

12


References
A. Ant Ozok, Quyin Fan, and Anthony F. Norcio. (2010). Design Guidelines for Effective Recommender System Interfaces
Based on a Usability Criteria Conceptual Model: Results from a College Student Population. Behaviour &
Information Technology, 57–83.

Alan Cooper, Robert Reimann, and David Cronin . (2007). About Face 3: The Essentials of Interaction Design. Wiley.

Andreas Dieberger. (1997). Supporting social navigation on the World Wide Web. Int. J. Human Computer Studies, 805-825.

Andreas Girgensohn and Alison Lee. (2002). Making Web Sites Be Places for Social Interaction. CSCW '02. New Orleans,
Louisiana, USA.

Dan Cosley, Pamela Ludford, and Loren Terveen. (2003). Studying the Effect of Similarity in Online Task-Focused
Interactions. Group '03. Sanibel Island, Florida, USA.

Douglas K. Van Duyne, James A. Landay, and Jason I. Hong. (2007). The Design of Sites: Patterns for Creating Winning
Web sites. Pearson Education Inc.

Greg Linden, Brent Smith, and Jeremy York. (2003). Amazon.com Recommendations: Item-to-item Collaborative Filtering.
Internet Computing, IEEE, 76-80.

Hsia-Ching Chang, and Chen-Ya Wang. (2011). No Cue, No Clue? Understanding Information Interaction in Social
Bookmarking Services. 2011 Eighth International Conference on Information Technology: New Generations. Las
Vegas, USA.

Jakob Nielsen. (2003). Usability 101: Introduction to Usability. Retrieved from useit.com:
http://www.useit.com/alertbox/20030825.html

James Kalbach. (2007). Designing Web Navigation. O’Reilly Media, Inc.

Jared M. Spool. (2003). Design Patterns: An Evolutionary Step to Managing Complex Sites. Retrieved 12 10, 2012, from
UIE.com: http://www.uie.com/articles/design_patterns/

Joseph A. Konstan and John Riedl. (2003). Collaborative Filtering: Supporting Social Navigation in Large, Crowded
Infospaces. In Munro, A., Höök K. & Benyon D. (Eds), Social Navigation of Information Space, pp. 43-82.

Joseph A. Konstan and John Riedl. (2012). Recommended for You. Spectrum, IEEE, 54 - 61.

Joseph B. Walther, Yuhua (Jake) Liang, Tina Ganster, Donghee Yvette Wohn, and Josh Emington . (2012). Online Reviews,
Helpfulness Ratings, and Consumer Attitudes: An Extension of Congruity Theory to Multiple Sources in Web 2.0.
Journal of Computer-Mediated Communication, 97-112.

Julita Vassileva (2009). Social Interaction History: A Framework for Supporting Exploration of Social Information Spaces.
International Conference on Computational Science and Engineering. Vancouver, Canada.

Kristina Höök, David Benyon, and Alan J. Munro. (2003). Footprints in the Snow. In Munro, A., Höök K. & Benyon D.
(Eds), Social Navigation of Information Space. pp. 1-11.

Martin Svensson. (2002). Defining, Designing and Evaluating Social Navigation. ph.D. thesis, Stockholm University.

13


Martin Svensson and Kristina Höök. (2003). Social Navigation of Food Recipes: Designing Kalas. In Munro, A., Höök K. &
Benyon D. (Eds), Social Navigation of Information Space. pp. 201-222.

Martin Svensson, Kristina Höök, and Rickard Coster. (2005). Designing and Evaluating Kalas: A Social Navigation System
for Food Recip. ACM Transactions on Computer-Human Interaction, 374–400.

Matthew Chalmers, Andreas Dieberger, Kristina Höök, and Åsa Rudström. (2004). Social Navigation and Seamful Design.
Cognitive Studies, 1-11.

Mattias Forsberg, Kristina Höök, and Martin Svensson. (1998). Design Principles for Social Navigation. 4th ERCIM
Workshop on 'User Interfaces for All', Special Theme "Towards an Accessible Web". Stockholm, Sweden.

Meredith G. Farkas. (2007). Social Software in Libraries: Building Collaboration, Communication, and Community Online.
Information Today, Inc.

Min Wu and C. Travis Bowles. (2010). Principles for Applying Social Navigatin to Collaborative Systems. CHIMIT’10. San
Jose, USA.

Paul Dourish. (2003). Where the Footprints Lead: Tracking down Other Roles for Social Nvigation. In Munro, A., Höök K.
& Benyon D. (Eds), Social Navigation of Information Space, pp. 15-34.

Peter Brusilovsky. (2008). Social Information Access: The Other Side of the Social Web. SOFSEM 2008. Novy Smokovec,
High Tatras, Slovakia.

Peter Brusilovsky, Lillian Cassel, Lois Delcambre, Edward Fox, Richard Furuta, Daniel D. Garcia, Frank M. Shipman III,
Paul Bogen, and Michael Yudelson. (2010). Enhancing Digital Libraries with Social Navigation: The Case of
Ensemble. ECDL 2010. Glasgow, UK.

Rosta Farzan. (2009). Study of Social Navigation Support under Different Situational and Personal Factors. ph.D. thesis,
University of Pittsburgh.

Stefan L. Pauwels, Christian Hübscher, Javier A. Bargas-Avila, Klaus Opwis. (2010). Building an Interaction Design Pattern
Language: A Case Study. Computers in Human Behavior, 452-463.

Susan M. Mudambi and David Schuff. (2010). What Makes a Helpful Online Review? A Study of Customer Reviews on
Amazon.com. MIS Quarterly, 185-200.

Thomas Erickson and Wendy A. Kellogg. (2000). Social Translucence: an Approach to Designing Systems that Support
Social Processes. ACM Transactions on Computer-Human Interaction, 59–83.

Thomas Erickson and Wendy A. Kellogg. (2003). Social Translucence: Using Minimalist Visualisations of Social Activity to
Support Collective Interaction. In Munro, A., Höök K. & Benyon D. (Eds), Social Navigation of Information Space,
pp. 17-42.

Wen Xu, Karel Kreijns2, and Jun Hu. (2006). Designing Social Navigation for a Virtual Community of Practice.
Edutainment 2006. Hangzhou, China.

14

Design Patterns on Social Navigation

Recomendados

Recomendados

Mais conteúdo relacionado

Mais procurados

Mais procurados (20)

Destaque

Destaque (20)

Semelhante a Design Patterns on Social Navigation

Semelhante a Design Patterns on Social Navigation (20)

Mais de Ruby Kuo

Mais de Ruby Kuo (20)

Último

Último (20)

Design Patterns on Social Navigation