Experimental Studies on Web, Music and Blog Interfaces
Thesis submitted in partial fulfillment
of the requirements for the degree of
Masters of Sciences (By Research)
in
Computer Science
by
Anupama Gali
Cognitive Science
International Institute of Information Technology
Hyderabad - 500 032, India
April 2011
Copyright c© Anupama Gali, 2011
All Rights Reserved
International Institute of Information Technology
Hyderabad, India
CERTIFICATE
It is certified that the work contained in this thesis, titled“Experimental Studies on Web, Music and
Blog Interfaces”by Anupama Gali, has been carried out under my supervision and is not submitted
elsewhere for a degree.
Date Adviser: Prof. BIPIN INDURKHYA
To
Brother -Venkat Ramesh Gali,
and Parents -Rama Padma GaliandK V R Sarma Gali
Acknowledgments
Firstly, I would thank my parents without whose encouragement and support I would not have been
able to write this. I consider myself to be very lucky for having done by graduation and post-graduation
in a prestigious institution like IIIT, Hyderabad. While dual-degree is one safe option to obtain MS
from home land, it is also quite demanding because the whole work needs to becompleted in 1-1.5
years. Very unsure yet determined, I worked with Dr. Kamal in Data engineering lab to explore various
research problems and take up one of them as my masters thesis problem. I thank him for his patience
and agreeing to my request to move to cognitive science lab and work in the field of cognition.
Dr. Bipin gave a warm welcome and complete independence to choose among various research
topics. I was completely surprised and fascinated to learn about various kinds of research going on in
this field. While the class room lectures gave a subtle idea of how this field wouldbe, Amitash and
Saraschandra, two PhD candidates helped in every way to learn the topicsin detail.
Internship with Rediff.com is another milestone that actually gave shape to my research work. Smt.
Vaishnavi Narayanan helped me to learn designing usability tasks and preparing questionnaires. She
also supported me emotionally with her kind words and suggestions with the help of which I could
publish my first international research paper.
Again I thank Dr. Bipin for sending me to that conference to learn more aboutthe ongoing work
in this field. Attending that conference and presenting my work in front of so many learned people
has cultivated some motivation in me to work harder. My heartfelt gratitude to Prof. Catherine, the
conference chair whom I met there, for encouraging me a lot in the conference.
I must really thank Gopala Krishna, my friend and fellow student in lab who has proposed some
collaborative work on interfaces to visualize similarity in music data. This new idea inspired me and
helped me to learn about another new research problem- faceted navigation and its difficulties. We
worked together in designing the interface and I took that work a step further by implementing and
evaluating it. Gopal had supported me in every work I did, both emotionally andprofessionally. I,
being an ardent reader of blogs, have always wondered if there could be some help in knowing some
information about other readers’ responses on a post without completelygoing through them. This
motivated me to work on blogs and comments.
The literature study I have done has proved to be very helpful to learn about the varied research
going on and my experimental studies helped me to learn how to design, conduct, collect and analyze
various results. I duly thank Dr.Bipin for permitting me to explore whatever I was interested in. With the
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knowledge I have gained, I am sure I will be able to continue my research inthis field more comfortably
in a focused manner.
Finally, Thanks to my friends Gopal, Bharat, Divya and Harshita who had encouraged me and backed
me up when I was in need. I am very much thankful to Saraschandra Karanam for his valuable sugges-
tions and guidance. I would not forget the long discussion I, Amitash and Saraschandra had regarding
analysis of some results. It is probably one of the longest and best professional discussions I had with
them. Thank you everyone for everything!
Abstract
Human Computer Interaction and usability have become increasingly important with the advent of
the Internet and its emergence as means of communication,e-commerce and social networking. A user
friendly website will attract more readers thus increasing the number of loyal visitors. As a result, the
need for presenting information in an attractive and usable manner is significant. Web usability of a
website is a measure of how intuitive the presentation of its contents are. A variety of techniques like
heuristic evaluation, expert reviews, performance evaluation have been developed to measure usabil-
ity and correlate this to end-user results like increase/decrease in visitors,increase/decrease in errors
performed while doing some tasks on the webpage etc.
This thesis is a compendium of three different experiments each on webpage elements, navigation in
music data and polarity of comments in blog articles respectively. Present experimental studies mainly
focus on the semantic influence of text links and the impact of graphical conventions and layout lo-
cations. Comparatively, less work has been done on interaction in betweenweb page elements. An
experiment to test whether there exist any interdependencies in between location expectations of web
widgets is conducted. This experiment also includes an eye-tracking studywhose results can be used
to determine the blind spots of each widget in a webpage. Based on the resultsobtained from this
experiment, conclusions are made to establish the interdependencies. It is noticed that out of the 4 wid-
gets (Search, Navigation pane, Ads and Login) that we’ve considered, location expectations of two of
them (Navigation pane and Ads) are found to be independent of any other widget whereas the other
two (Search and Login) are found to be dependent on main content and navigation pane of the page
respectively. Such findings when followed as heuristics can be used in developing new websites to help
users search for dependent widgets when the location of a widget is known.
Another study includes the design,implementation and evaluation of a web model that helps in
searching and browsing through huge music data catalog. The fundamentals of faceted navigation are
used to build this model which groups the music data based on the similarity in their features rather
than metadata. We named this web model as REM which expands to Ray Exploration Model. As the
name suggests, it conceptualizes the depth of each attribute in the data and breadth across the attributes
in an easily navigable sun model with its rays. Together suns and rays forma network using relations
between the values of attributes. REM caters to search needs of a user without the limitations of the
popular keyword based search approaches. This model is implemented using Raphael JS graphics and
is evaluated using a usability evaluation method called ‘performance measurement’. Results from this
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evaluation are used to know the user satisfaction with the model and suggestions/improvements that can
be made. The usability evaluation of our model shows that users were majorly happy with its function-
ality and aesthetic appearance of the interface. Suggestions on navigatingback to a page were noted
and these changes are made to the model so that it complies with them.
A final study includes an experiment to test the effect of polarity of socialinteraction history on
users reading blogs. Previous studies report that the number of commentson a blog article play a role
in attracting readers- users’ interest in an article increases with the numberof comments . The type
of comments that contribute to the total number is not considered. It is hypothesized that the type of
comments (polarity) on a blog post effect the user’s interest in a blog. In the main experiment, the
participants were asked to read a set of posts belonging to four different categories in two conditions-
presence and absence of polarity of comments. They were also asked to rate the posts on a scale of 5
given the polarity distribution of comments. This data from the experiment was statistically analyzed
using one-way ANOVA and paired values t-test measures respectively.The varied results show that the
polarity of comments affects readers interest for blogs categorized as ‘technology’ and did not play much
role for the other two categories (‘neuroscience’,‘polarity’ and ‘war’). Further, for the posts belonging
to ‘technology’ category ,it was also noticed that posts with major negative comments were liked by
more readers than posts with major positive comments. Thus,to conclude, polarity of comments was
found to effect reader’s interest in blog articles based on the type of posts. One immediate extension to
this study is to conduct it on a variety of participants to see if the results are governed by the readers’
background/knowledge.
Contents
Chapter Page
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Introduction to Usability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Web usability definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.2 Why is web usability important . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Outline of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Interdependencies in Location Expectations of Web Widgets. . . . . . . . . . . . . . . . . 6
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Hypothesis proposed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 8
2.4 Method of the experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 8
2.4.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4.2 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4.3 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4.3.1 Behavioral study . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4.3.2 Eye tracking study . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4.4 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.4.5 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.5 Procedure followed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12
2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.6.1 Results from behavioral study . . . . . . . . . . . . . . . . . . . . . . . . . .12
2.6.1.1 Location expectations of the search bar . . . . . . . . . . . . . . . 13
2.6.1.2 Location expectations of the navigation pane . . . . . . . . . . . . 14
2.6.1.3 Location expectations of the ads . . . . . . . . . . . . . . . . . . . 15
2.6.1.4 Location expectation of the login box/link . . . . . . . . . . . . . . 15
2.6.2 Comparison with eye tracking study . . . . . . . . . . . . . . . . . . . . . . 16
2.6.2.1 Interest regions for search widget . . . . . . . . . . . . . . . . . . 17
2.6.2.2 Interest regions for navigation pane . . . . . . . . . . . . . . . . . 18
2.6.2.3 Interest regions for ads . . . . . . . . . . . . . . . . . . . . . . . . 18
2.6.2.4 Interest regions for login box/link . . . . . . . . . . . . . . . . . . 19
2.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19
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x CONTENTS
3 REM-Ray Exploration Model that Caters to Exploratory Search. . . . . . . . . . . . . . . . 213.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .213.2 Faceted Navigation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22
3.2.1 Usability Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.3 Related work: A Brief Survey of Catalog Exploration and Visualization Models . . . . 22
3.3.1 mSpace model:slicing through n-dimensional space . . . . . . . . . . . . . . .223.3.2 Phlat:tagging UI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233.3.3 Mambo:Visual zooming approach . . . . . . . . . . . . . . . . . . . . . . . . 243.3.4 Audiomap:Graphs and nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . 253.3.5 Elastic Lists:faceted navigation . . . . . . . . . . . . . . . . . . . . . . . . . 263.3.6 Videosphere:spherical model . . . . . . . . . . . . . . . . . . . . . . . . . .. 27
3.4 REM-Ray Exploration Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 283.4.1 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.4.2 Exploratory Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31
4 Implementation and Evaluation of REM. . . . . . . . . . . . . . . . . . . . . . . . . . . . 334.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .334.2 Current Evaluation Practices . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 334.3 Our Evaluation Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 344.4 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344.5 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .344.6 Details Of Each Screen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 354.7 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 354.8 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.8.1 Error rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364.8.2 Satisfaction with the tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384.10 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
5 Effect of Polarity of the Traces of Interaction History in Reading Blog Posts . . . . . . . . . 405.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .405.2 Related work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.3 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.4 Online survey- Feasibility test . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 435.5 Method of the experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 43
5.5.1 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.5.2 Apparatus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435.5.3 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.5.4 Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 445.5.5 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5.6 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 455.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 45
5.7.1 Effect of polarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455.7.2 Effect of positive and negative polarity on user’s order of reading . . . . . . . 465.7.3 Effect of positive and negative posts on user’s likings . . . . . . . .. . . . . . 47
CONTENTS xi
5.8 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 48
6 Conclusions and Future Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496.1 Contributions and Final Word . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 49
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
List of Figures
Figure Page
2.1 Version1 of page used for ‘Search’ Widget . . . . . . . . . . . . . . . .. . . . . . . . 92.2 Version2 of page used for ‘Search’ Widget . . . . . . . . . . . . . . . .. . . . . . . . 92.3 Version3 of page used for ‘Search’ Widget . . . . . . . . . . . . . . . .. . . . . . . . 102.4 Results of behavioral study-‘Search’ widget . . . . . . . . . . . . . . .. . . . . . . . 132.5 Results of behavioral study-‘Navigation pane’ widget . . . . . . . . . .. . . . . . . . 142.6 Results of behavioral study-‘Ads’ widget . . . . . . . . . . . . . . . . . .. . . . . . . 152.7 Results of behavioral study-‘Login box/link’ widget . . . . . . . . . . . .. . . . . . . 162.8 Example of interest regions in a heatmap generated for one version of aweb page used
for search widget . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17
3.1 Screenshot of mspace model used for browsing through data of research papers . . . . 233.2 Phlat screenshot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . 243.3 Screenshot of mambo browser for browsing through songs . . . . . .. . . . . . . . . 253.4 Audiomap screenshot displaying search results for keyword ‘boney’ . . . . . . . . . . 263.5 Screenshot of elasticlists for browsing through data of noble prize winners . . . . . . . 273.6 Videosphere screenshot . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . 273.7 A prototype design for REM with terminology used in it . . . . . . . . . . . . . . .. 283.8 REM-different screens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . 29
4.1 REM-Screenshots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 35
5.1 Percentage of users reading posts based on polarity distribution of comments . . . . . 44
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List of Tables
Table Page
2.1 Result summary of behavioral study . . . . . . . . . . . . . . . . . . . . . . . .. . . 162.2 Result summary of blind spots noticed for ‘search’ widget across versions . . . . . . . 182.3 Result summary of blind spots noticed for ‘navigation’ widget across versions . . . . . 182.4 Result summary of blind spots noticed for ‘ads’ widget across versions . . . . . . . . . 192.5 Result summary of blind spots noticed for ‘login’ widget across versions . . . . . . . . 192.6 Result summary of eye tracking study . . . . . . . . . . . . . . . . . . . . . . . .. . 19
4.1 Summary of the results of the task-specific questionnaire . . . . . . . . . . .. . . . . 374.2 Summary of the results of the screen-specific questionnaire . . . . . . . .. . . . . . . 374.3 Summary of the results of the user-satisfaction questionnaire(scale of 5) . . . . . . . . 38
5.1 One way ANOVA results for effect of polarity . . . . . . . . . . . . . . . . .. . . . . 465.2 Paired T-Value tests of user given order for posts with positive and negative polarities . 475.3 Paired sample statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475.4 Paired T-Value tests of user given liking for posts with positive and negative comments 47
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Chapter 1
Introduction
1.1 Introduction to Usability
One of the most widely accepted definitions of usability, which is based on the ISO standard 9241-11
is “the extent to which a product can be used by specified users to achieve specified goals with effec-
tiveness, efficiency and satisfaction in a specified context of use”. The three core terms are defined as
follows: effectiveness is specified as the ‘accuracy and completenesswith which users achieve spec-
ified goals’ efficiency refers to the ‘resources expended in relation to the accuracy and completeness
with which users achieve goals’ and satisfaction is the ‘freedom from discomfort, and positive attitudes
towards the use of the product.’ For example, if the user’s goal is to complete purchase requisitions, ef-
fectiveness refers to the extent to which the finished purchase requisitions reflect the intended purchases
accurately and are complete; efficiency refers to the number of purchase requisitions completed within
a unit of time; and satisfaction refers to the extent to which the user is able to complete the task without
discomfort and with positive attitudes regarding the process of using the system.
Usability as coined by Jakob Nielson1 composes of:
• Learnability: How easy is it for users to accomplish basic tasks the first time they encounter the
design?
• Efficiency/Effectiveness: Once users have learned the design, howquickly can they perform
tasks?
• Memorability: When users return to the design after a period of not using it, how easily can they
attain proficiency?
• Errors: How many errors do users make, how severe are these errors, and how easily can they
recover from the errors?
• Satisfaction: How pleasant is it to use the design?
1http://www.useit.com/alertbox/20030825.html
1
Bevan & Macleod [7] suggest that effectiveness can be measured byaccuracy, efficiency by time and
satisfaction by subjective workload measures.
Ease of use and effectiveness should ideally be defined in quantifiable metrics such as [10]:
• Ease of start up: time taken to open/install a program and start using it.
• Ease of learning: time taken to learn how to perform a set of tasks.
• Error scores: Number of errors committed/ time taken to correct errors that occurred.
• Functionality: Number of different things the program can do?
• Users’ rating of ease of use: Ratings can be used to measure users’ perceptions.
According to the discussion in this section so far, usability relates to having a system which is easy and
safe to use, and satisfies user requirements in a particular environment.
For example, Gas cylinders are generally painted red in color because red represents danger and
is vaguely visible in dim light. PDF files while browsing web is an example of bad usability design
because it breaks users’ flow.
1.1.1 Web usability definition
Web usability can be considered as the ability of Web applications to support such tasks with effec-
tiveness, efficiency and satisfaction. Also, the above mentioned Nielsen’s usability principles can be
interpreted as follows:
• Web application learnability must be interpreted as the ease for Web users to understand from
Home page the contents and services made available through the application, and how to look
for specific information using the available links for hypertext browsing. Learnability also means
that each page in the hypertext front-end should be composed in a way soas contents are easy to
understand and navigational mechanisms are easy to identify.
• Web applications’ efficiency means that users that want to find some contents can reach them
quickly through the available links. Also, when users get to a page, they must be able to orient
themselves and understand the meaning of the page with respect to their navigation starting point.
• Memorability implies that, after a period of non-use, users are still able to get oriented within the
hypertext, for example by means of navigation bars pointing to landmark pages.
• Few errors mean that in case users have erroneously followed a link, they should be able to return
to their previous location.
• Users satisfaction finally refers to the situation in which users feel that theyare in control with
respect to the hypertext, thanks to the comprehension of available contentsand navigational com-
mands.
2
1.1.2 Why is web usability important
Usability of the website is important for the users because they will enjoy usingit and achieve their
goals more efficiently. This will help them cultivate confidence and trust in thewebsite. Usability is
also important for the providers because it reduces development time and support costs. It also reduces
user errors and decreases training time and errors.
Assessing the usability of information available on the WWW is becoming more important as the
Internet is slowly becoming first resort for information for many people [22]. With the web information
space getting bigger day by day, it is quite difficult for a user to choose thecorrect web page that satisfies
his/her requisites in terms of the content, organization (non-linear structureof the web) and presentation
of the content. There is also a possibility of user getting lost in the hyperspace. According to Conkli
n [11], two classes of problems are associated with hypertext: problems withcurrent implementations,
which include delays in the display of referenced materials, deficiencies in browsers, etc; and secondly,
problems that seem native to hypertext such as cognitive overload and disorientation. Cognitive over-
load is the additional effort and concentration necessary to maintain several tasks or trails at one time.
Disorientation is the tendency of users to lose their way in non-linear information. This is commonly
referred to as the “lost in hyperspace” (LIH) problem. The LIH phenomenon according to us, however,
can refer to any of the following conditions: users cannot identify wherethey are;users cannot return to
previously visited information; users cannot go to information believed to exist; users cannot remember
what they have covered; and users cannot remember the key points covered ( [11] [34]etc). Many navi-
gational aids have been proposed to users to assist in overcoming this problem such as breadcrumb trails
[23], site maps [41],etc. Nonetheless, users continue to get lost and feel disoriented, particularly on the
Web. Therefore, it is quite important to understand how people find and comprehend the information in
a web page so that web pages that can assist the users in this process can be developed.
In this thesis, we worked on three different web usability issues. One of them is about the locations
of web widgets in a webpage which relates to usability of web pages. Locationof web widgets in a web
page plays a very important role because violation of these locations will result in decreased usability of
the page [22]. The second study is about design, implementation and evaluation of a usable web model
to browse through huge music catalogs. Last study is about the effect ofpolarity of user responses on
reader’s interest in a blog. This is related to usability of blogging websites.
The next section briefly outlines the contents of this thesis. The related workand motivation for each
study are described in every chapter in detail.
1.2 Outline of this thesis
Chapter 2 of this thesis includes an experiment that investigates if location expectations of web
widgets are interdependent on each other. Several studies have beendone on the location expectations
of commonly used widgets like search,navigation,login,ads,footer and logo [5][37] [22]. With these
initial studies as basis, a new hypothesis is proposed that these expectations are interdependent on each
3
other. ie. Location of a widget in one page is expected to be dependent onthe location of another widget.
To test this hypothesis, a behavioral study and an eye tracking study areconducted on eight different
websites. The results collected from the study have been analyzed and it isfound that the hypothesis
is proven true. If these interdependencies are followed as heuristics when designing a web page, it can
help people in locating the dependent widget (s) when the location of a widgetis known. This will thus
decrease users’ time in searching for a widget thereby aiding him/her and increasing the usability of the
web page.
Chapter 3 relates to user interface design of a web model. It includes a literature survey on the
state of the art exploratory models for browsing through data and the description of the model that we
propose to serve this purpose using faceted navigation. Faceted navigation is a technique for accessing a
collection of information represented using a faceted classification, allowingusers to explore by filtering
available information. Each facet corresponds to the possible values of aproperty common to a set of
objects.To date, it is not clear if faceted navigation enhances usability or increases number of categories
to be substantially searched [20]. However, several models have emerged that enable faceted browsing
through data, especially music data. Some of them arrange music according tothe tags, user’s choice,
etc. REM (Ray Exploration Model) is a sun-ray model that uses faceted browsing and groups music
data according to similarity in features like scale,pitch,emotion (raaga) etc. This model represents the
current data item as a sun and all its related attributes as rays. Users explore by filtering information
based on the attributes.
In Chapter 4, the implementation and evaluation of REM is explained in detail. Raphael JS is
used to implement REM as a web model. Raphael’s JS framework is a powerfultool used to draw
vector graphics on web.A database of 330songs is collected and are grouped based on similarity using
melody features extracted using inhouse raaga identification system.Evaluation of exploration visual-
ization models is a challenge because the exploration differs from user to user [25]. Hence, the user
satisfaction in visualization design and utility of the model is tested. User evaluation in terms of overall
satisfaction,browsing,learning and efficiency of the tool is tested by conducting an experimental study
on 13 participants. Our model successfully overcomes some limitations of the faceted navigation like
handling of complex queries etc. Web exploratory models that provide faceted navigation are limited to
our knowledge (More details in chapter3). REM is a decent addition to suchmodels and is thus helpful
to increase usability of web models with faceted navigation.
In Chapter 5, we hypothesize that the type of social interaction history (here polarity) influences
a reader’s interest in reading a blog article. Previous studies suggest that the number of comments
on a blog article influence a reader’s interest in choosing that post to read [25]. However, the type of
comments that contribute to this number is not considered. We propose that it isnot just the count but the
type of the comments also has an influence. This is reasonable because there might be user responses
that are irrelevant/that are not in accordance in the post. Hence to know iftype of comments has an
influence, we begin with polarity of comments with respect to the post as the first measure. It is found
that polarity of comments has an influence depending on the category of posts. Further studies have to
4
be conducted to test the hypothesis on a different set of participants before we generalize the results.
If the hypothesis is true then it can be used for a new type of navigation in blogging sites based on the
type of comments each post received. This will help users who wish to browse through the blog posts
according to this criteria and hence increase usability of blogging websites.
Conclusionsincludes the observations and interesting results that we have found in ourstudies. It
also has a future work section which shortly describes how this researchcan be carried further.
5
Chapter 2
Interdependencies in Location Expectations of Web Widgets
2.1 Introduction
Our research strategy has been to discover if location expectations of one web widget are dependent
on the expectations of another. We test this by placing some web widgets in different locations on a
web page and see where the participants expect the missing/target widget toappear. We first discuss the
related work section, followed by the hypothesis and procedure we followed to do this experiment. We
then analyze the data and conclude with our observations and future work.
2.2 Related work
The time taken to find web widgets is affected by a) familiarity with the widget and b)location of
the widget, apart from the graphics of the web page [22]. Familiarity with a web widget speeds up the
process of recognizing it. It has been shown that users quickly find thefamiliar widget compared to the
unfamiliar widget [8]. In surveying the frequency of widget use, Hinesley [22] has found that search
widget is the most used and newsletter is the least used web widget. In this survey, it was also found that
frequently used widgets were generally expected to appear in the top of thepage whereas infrequently
used widgets were generally expected to appear in the bottom of the page.
Several web design experts [47] and usability researchers have stressed the importance of establish-
ing and maintaining web page layout conventions [2]. Users develop a setof expectations about most
commonly used web page widgets as they keep browsing the web. For example, one usability study
says that logo is consistently placed on the top left of the page and clicking onit takes to the home page
of the site most of the times [36]. One recent study by Sandra [43] also reports that internet users have
distinct mental models for different web page types (online shop, news portal, and company web page).
Users generally agree about the locations of many, but not all, web widgets. These mental models are
robust to demographic factors like gender and web expertise. This knowledge could be used to improve
the perception and usability of websites.
6
Petrie’s [40] studies investigate the effects of navigational inconsistencies of the website on users’
perceptions and performance. It was found that when the position of navigational bar was altered from
left to right, the time spent on a page was more than doubled than time usually spent on pages with its
position intact. This effect due to inconsistency also persisted over subsequent pages.
Van Shaik [38] found that there was an effect of frame layout both on accuracy and speed measures,
with frames located at the top or left of the screen leading to better performance. Pearson and Van
Shaik [39] also conducted experimental studies which reported that positioning of navigational menus
was mixed-both left and right.
Also, Recent studies using eye-tracking technology report that eye movements are directly driven
by location expectations: people look on the left side first when searchingfor navigation links even
when the link have been placed in unconventional locations [37]. Anothereye-tracking study provides
evidence that people start their search by looking at the upper left of a display and then proceed in a
clockwise direction [15].
Users who use a web page have some expectations about where to find thecommon web widgets.
Constructing a web site that reflects these expectations will help in increasingthe site’s accessibility in
terms of producing more accurate and faster information retrieval, as well as greater satisfaction with the
site [1] [2] [6]. Markum’s study [33] on e-commerce websites also reports that user location schemas
are largely consistent for e-commerce web object locations, and these expectations are also consistent
with previous research.
Memory also affects users in identifying location of web widgets. Studies by Oulasvirta [37] tested
user memory for links. Results indicated that only the location for task-relevant widget could be re-
trieved. According to Hinesley [22]:
“This limitation of memory to only the attended objects fits well with research from thevisual search
literature. This selective memory raises the question of whether only task related elements develop user
expectations and whether these expectations increase with experience. It seems important, therefore, to
gain some understanding of users’ experiences on the internet.”
Several studies have been done to test the effect of violating the common location conventions. Effect
of violating the location expectation of navigation frame on mock pages was examined. They found that
this led to poorer search performance [39] [38] [45]. Also in a surveyof commercial websites, it has
been found that consistency of location conventions among websites is notmaintained [2].
From the literature, we believe that users have some expectations on the location of web widgets
when browsing web. The aforementioned studies lack to investigate if these expectations are interde-
pendent. If the location expectations of two or more widgets are dependenton each other, then it is easy
to find the dependent widget when the location of a widget is known. Hence, our research goal is to
discover if the location expectations of one web widget are dependent onthe other and also to identify
them. The four commonly used web widgets on which this study is done are search, navigation pane
(Links that take a user from one section of the website to the other), Ads and Login [22].
7
2.3 Hypothesis proposed
The hypothesis of our study is that there exist interdependencies between the location expectations
of some web widgets.This means that if widget A and widget B are expected to be location-dependent
on each other, then widget B is expected to appear according to that dependency violating which will
increase time taken to find it. So if the interdependency is true, it makes it easierto find one widget
when the other widget on which it is dependent is located. Hence, if these are taken into account while
designing, time taken by a user to get familiar with a new web page can be decreased, thereby increasing
the usability of the web page.
2.4 Method of the experiment
2.4.1 Participants
Twenty people (17 men and 3 women) of average age 24 participated in the experiment. All of them
are software engineers employed with an internet-services and web-portal company, and are advanced
or intermediate internet users in terms of the quality time spent on browsing the web. The partici-
pants reported that they have been using internet for 4 years or longer. They primarily use the web for
educational and entertainment purposes.
2.4.2 Apparatus
We used a 2 x 3 (versions) x 4 (widgets-search, login, navigation, ads)repeated measure design.
Each participant had to look at six web pages for each widget. In total, each participant had to do
twenty-four tasks. Respective versions of two pages used for the samewidget were made to look same
in terms of widget locations to avoid web complexity. For example, the version 1 of page 1 for a widget
and the version 1 of page 2 for the same widget were identical in the location ofvisible widgets and so
on for all the web pages.
The following figures are examples of the three versions of web page1 used for the ‘search’ widget.
Note that none of the versions has the search widget and the remaining three widgets (navigation pane,
ads and login) are placed in each of them at different locations.
8
Figure 2.1Version1 of page used for ‘Search’ Widget
Figure 2.2Version2 of page used for ‘Search’ Widget
9
Figure 2.3Version3 of page used for ‘Search’ Widget
Version1 has navigation links on the left, ads on the right and login box on thebottom right below
the ads. Version2 has navigation links on the right, ads on the left and login box on the top left below
the logo. Version3 has navigation links on the top, ads on the right and login box below the main
content of the page. Note that no two versions are identical in terms of the location of visible widgets
we are considering for the experiment. (Here- navigation links, login and ads because search is the
target/missing widget).
Screen shots of some web pages were taken and necessary changes were made to them. Care had
been taken to see that same widgets appear in each web page but in different order and each web page
occupied the same amount of screen space. None of the pages requiredany scrolling. Making a widget
invisible was typically done by removing it from the web page. Each version of the page was made to
look different from the other versions by rearranging the functional units.
All the web pages were edited using GIMP, an open-source software popularly used for image ma-
nipulations. After removing a widget from a web page, the space it previously occupied was filled with
background color/graphics of the web page and the remaining widgets were adjusted accordingly. Nec-
essary care was taken to make the web page look natural except that it was missing the widget. The
entire web page was made clickable to allow the user to click on any region he/she finds apt for that
particular widget. To make sure visual parameters do not dominantly effectusers’ attention, two web
pages used for the same widget were made to have same background color.
10
2.4.3 Design
The main experiment consists of two studies.
2.4.3.1 Behavioral study
Each participant was presented with a screen which showed the task followed by one version of a
web page for every task. The task given to the user was to look at the page for some time and click on
the region where he/she expected to find the missing widget. The mouse movements of the participant
were recorded separately using a video recorder. The participants were also asked to explicitly state the
reason why they expected a particular widget in a particular region. These results were analyzed to find
out whether the location expectations of widgets were interdependent.
2.4.3.2 Eye tracking study
The purpose of integrating the eye tracking study with the normal experimentwas to get additional
information about where the participants are looking as they search for themissing widget [44]. The
eye-tracking software can represent the areas of the screen receiving more fixations or receiving the
longest dwell times in a color-coded “hotspot” image of the interface which is popularly known as
the “heatmap”. The regions are closer to red if more fixations occur in an area of the interface. The
results were examined to note the regions that were attended to and the blind spots in the page that went
completely unnoticed. We analyzed the density of fixations in different areas. This data will reveal
whether eye- movement patterns differ for different versions and what is the blind spot for each widget.
2.4.4 Tasks
Each participant had to first look at the question screen for each widgetwhich has the name of the
missing widget. Then he/she was shown the experimental page on which the place where the participant
expected that widget to appear had to be clicked. Then he/she had to state why that particular region was
chosen. This task had to performed 24 times for 4 widgets. All the experimentand the eye movements
of each participant were recorded separately for analysis.
2.4.5 Variables
The dependent variable for the behavioral study is the region on the webpage that is clicked as an
expected region. It has to be noted that the division of the web page (screen) estate and the name for
each division varies from participant to participant. For example, the bottomof the page might contain
the copyright information. Some participants may call it as “bottom of the page”. Some others might
call the same region as “location of copyright info”. The exact terminologyused by the participants has
been maintained all through the analysis. One direct advantage of using exact terminology is that we are
11
clearly able to maintain the difference in between user expectations supporting interdependencies and
not supporting interdependencies.
The dependent variable for the eye tracking study is the density of fixationon each page for each
widget. It has to be cautioned that the presence of high density of fixation ina particular region of a web
page does not imply that user expects the missing widget to appear in that place. The reason for more
fixation could be due to several other reasons like visual balance etc. Eye tracking can only be used to
determine blind spots of each widget in a web page.
2.5 Procedure followed
The same task order was used for all twenty participants to minimize the complexitiesin analysis
part. Each task order had four sets of six pages with jumbled versions. Because each version of a
web page was different from others in the layout of widgets and these versions were jumbled while
presenting, the chances that a user can learn about the web page in a particular version were considerably
minimized. It means no two versions of different web pages were presented consecutively to the user.
The users were tested using a desktop running Windows XP OS, with HP 1366 x 768 flat panel and eye
tracker SR Research eye link 2000. For each task, the participants wereshown a set of task instructions
on the screen (such as each of the web pages has the ‘search box’ missing. If we introduce a search box
to this page, which area of the web page will you expect it to be?). They were asked to click the mouse
button when ready to proceed. Then they were told to put the cursor overthe area of the web page where
they expected the widget to be and click on that region using the mouse. All theclicks made by each
participant were recorded in separate videos. There was no time limit on anytask and the participants
were asked to verbally state the reason why they expected the widget to be inthat region. Because there
is no right or wrong region, all the expected regions by all the participantswere noted. The participants
were asked to find a place for the missing widget to note if they expected that particular widget to appear
with some other widget (s). They were presented the new page as soon asthey finished clicking on the
current page. A total of 480 responses were collected from all the participants.
2.6 Results
2.6.1 Results from behavioral study
The data collected from both think-aloud study and recorded videos was analyzed. All the re-
gions/widgets in the page associated with the missing widget were tabulated and the value of the corre-
sponding cell was incremented by one every time a participant chose it. One such table was obtained for
each of the four widgets (search engine, navigation pane, ads and loginbox) resulting in four tables that
are graphically shown in the below figures. Each graph was plotted against the areas that participants
expected the widget to be and the number of times that area was chosen.
12
As stated earlier, there is no correct/incorrect region and the participantswere not presented any
choices of regions to choose them. They were asked to explicitly state the expected region of the
missing widget and the reason why they expected it to appear in that location.These expected regions
were analyzed without any changes to the exact terms used by the participants. Hence, it is possible that
the regions might be overlapping. However, the percentage counts of thepeople corresponding to each
location expectation are mutually exclusive.
For each widget, expected regions in all the 6 versions were obtained. The data was analyzed sepa-
rately for each version to see if the location of missing widget was expected tobe dependent on location
of some other widget or not and grouped accordingly. Each of the graph in the results section below is
plotted using this grouped data. The results from this study are explained in detail in each section below.
Figure 2.4Results of behavioral study-‘Search’ widget
2.6.1.1 Location expectations of the search bar
In each of the 6 images of web pages (2 web pages x 3 versions) used, the search bar was mostly
expected to appear with the main content of the page. Most of the participantsexpected the search to
appear at a fixed place-top right of the main content of the page for all theversions used. For 20 partici-
pants, 61 out of 120 responses suggested that the search is dependent on the location of the main content.
Figure 2.4 shows the graph plotted against expected regions and corresponding percentage counts. Most
of the participants preferred to have the search widget on the top of the content because it is the main
content of the page that changes when search results are displayed. The next widget on which the search
box is mostly dependent is the navigation bar (both top and side). From the 120 responses, 99 suggested
that the search bar is dependent on other widgets (the main content, navigation pane, login link/box and
ads in that order) and 21 suggested that search bar is independent ofany other widgets on the web page.
It is also interesting to note that the search bar is least expected to be seen inthe bottom of the page and
13
left of the page. The most probable region where it can be seen, according to this study, is on the top
right of the main content of the page.
A participant’s thought on the location of search widget was the following:“Search should appear
with the main content because it is the main content that changes when a search engine is queried using
the search widget.”
2.6.1.2 Location expectations of the navigation pane
Navigation pane of a page is that region which contains all the links that take areader from one page
to another page. Irrespective of the location of other widgets, the expected region of navigation pane
was the left of the web page in all the 6 web page images (3 versions each for 2 web pages) used. Dawn
and Lenz’s [1] results suggest that participants mostly expect the internal links to appear on the left side
of the page. The current results also support this.
From Figure 2.5, the widget for navigation was expected to be independent of any other widget in
the web page and most of the users (36.58% of the responses) expectedit to appear on the left of the
page. This suggests that people generally prefer the book like viewing ofinternet. Closely following
(28.33% of the responses) was the top of the page irrespective of otherwidgets. Few users also expected
the navigation pane to appear with the main content (<5%) and logo of the page (<2%). Another
interesting observation is that navigation pane was also expected (9.8 %) to appear on the right of the
page.This can be attributed to the present growing trend of blogging websites.
“I read a lot of books.So I’m habituated to look from left to right”
“ I like the way the blogs have navigation to their right. Having navigation on the right seems trendy.”
Figure 2.5Results of behavioral study-‘Navigation pane’ widget
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2.6.1.3 Location expectations of the ads
The expected region of ads was also found to be consistently same for all the web page images used
irrespective of the location of other widgets- to the right of the page.
From Figure 2.6, we can see that the advertisements in a page are expectedto be independent of any other
widget.74.17% of the responses suggest that the ads should appear on the right of the page irrespective
of the other widgets. They were least expected to appear with the navigationpane of the page (3.33%).It
is also interesting to note that most of the participants expected to find the ads onthe right of the page
because of the design trends that are dominant today.
“Ads should come on the right because that is the popular way of having them.”
Figure 2.6Results of behavioral study-‘Ads’ widget
2.6.1.4 Location expectation of the login box/link
In all the web page images (2 web pages X 3 versions) used, irrespective of the position of other
widgets, login was mostly expected to appear with the navigation pane. The location of navigation pane
was different in each of the versions used (version1-top, version2-left and version3-right of the page).
The expected location of the login was also found to vary accordingly.
From Figure 2.7, most of the responses (35.83%) suggested that the location expectations of the login
are dependent on the location of navigation pane. The second most expected location is the extreme right
of the page. It is evident from the graph that login is most expected to be present with the navigation
pane and least expected to be with the header image. Another interesting observation that was made
is that 97% of the users, who had voted for this dependency, expected the login to appear with top
navigation rather than side navigation.The most stated reason is that logging inprovides a personalized
15
menu which appears in the navigation menu.
“We want to see a personalized menu when we log in. So login should come with the navigation bar.”
Figure 2.7Results of behavioral study-‘Login box/link’ widget
These results are summarized as follows:
Widget Expected location
Search top of main content of the pageNavigation pane Independent of other widgets-on the left side of the pageAds Independent of other widgets-on the right side of the pageLogin with the navigation pane
Table 2.1Result summary of behavioral study
2.6.2 Comparison with eye tracking study
Eye-tracking results cannot guarantee that the participants looked at a particular region just because
they expected the widget to appear there. To our knowledge, the interdependencies between location
expectations cannot be determined from this data. However these results can be used to find the blind
spots for a widget in a particular web page. Higher fixation rates or dwell times in an area can be
determined by the hotness of the region. It is indicative of users looking atthat region for a long period.
The lack of visual attention in non hot regions is indicative of users not fixating upon it. These regions
can be termed as blind spots. To analyze the eye tracking study results, the 480 responses (.edf files)
collected from the participant were averaged to 24 responses using the data viewer of eye link. All
the responses were averaged for each version by setting the variable (across which average is taken) to
“image” in the data viewer. This will regroup the data based on the images of theweb pages. Because
16
the number of images of web pages is 24, a total of 24 averaged responses were generated. From each
of these 24 responses, heat maps were generated using the data viewer. In this way, we obtain heat maps
for each version of each web page.
Each heat map was divided into 6 interest regions: top-left, top-center, top-right, bottom-left, bottom-
center and bottom-right like shown in the figure below. Note that the name of each interest region
denotes the location of that region on the page. For example, top-left denotes the first interest region
that is on the extreme left of the page in its upper half. The above figure shows the six interest regions
Figure 2.8Example of interest regions in a heatmap generated for one version of a web page used forsearch widget
in an example heat map of one version of a web page used for the search widget. The top left corner
has an intense red color which means it is a hot region. To quantize the results from heat maps, all the
observations made from the images were tabulated. If the interest region ofa heat map contained a heat
spot, the corresponding cell was given a value 1. If the interest regionwas unnoticed, it is considered
a blind spot, and the corresponding cell was given a value -1. In all other cases, the cell was given a
value 0. For example, in the example heatmap, the values of interest regions were given these values
as follows: top-left:1, top-center:0, top-right:0, bottom-left:0, bottom-center:0, bottom-right:0. After
obtaining such tables for six web pages related to a widget, these values were analyzed separately for
each version to note the blind spots.
2.6.2.1 Interest regions for search widget
The following table shows the blind spots for each version used for the search widget. It is noticed
that corresponding versions (Example: version1 of web page1 and version1 of web page2,so on) have
same blind spots which is desirable. From the table,it can be said that the bottom-left and bottom-center
of the page were not seen by participants at all. So these regions can be considered as the blind spots
17
Version Description Blind spot (s)
Version1 Navigation on the left, Ads on the right andLogin on the right
bottom left,bottom center and bottom right ofthe page
Version2 Navigation on the right, Ads on the left andLogin on the left
bottom center of the page
Version3 Navigation on the top, Ads on the left and Lo-gin on the top left below the logo
bottom left and bottom center of the page
Table 2.2Result summary of blind spots noticed for ‘search’ widget across versions
for search widget. From this, it seems imperative to say that placing a search bar in the bottom-left and
bottom-center of the page will increase the time taken to locate this widget and hence hinder the usability
of the site. Studies by Dawn and Lenz [1] suggest that participants expect to find the search engine on
the top-right of the page followed by its top-left. Our results from the eye-tracking data support this.
2.6.2.2 Interest regions for navigation pane
The following table shows the blind spots for each version used for the navigation widget. It is again
noticed that corresponding versions (Example: version1 of web page1and version1 of web page2,so
on) have same blind spots. The participants did not look at the bottom-right ofthe page, so this can be
Version Description Blind spot (s)
Version1 Search on the top right, Ads on the bottomand Login on the top of the page
bottom right of the page
Version2 Search on the right, Ads on the left and Loginon the top with the logo of the page
bottom right and bottom left of the page.
Version3 Search on the top, Ads on the right and Loginon the right of the page
bottom right of the page
Table 2.3Result summary of blind spots noticed for ‘navigation’ widget across versions
called as a blind spot for navigation links.
2.6.2.3 Interest regions for ads
Interestingly in all the heatmaps of the versions, it was noticed that participants mostly looked at
the top-right of the page followed by the top-center of the page because those regions were hot. The
participants did not look at the right bottom of the page. So this seemed to be theblind spot for ads.
The results from behavioral study suggest that participants expected the ads to appear on the right side
of the page irrespective of other widgets. This is supported by the resultsfrom eye tracker. Following
table summarizes the results for blind spots.
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Version Description Blind spot (s)
Version1 Search on the top with logo, Navigation onthe top and Login on the left of the page
bottom right of the page
Version2 Search on the right, Navigation on the left andLogin on the top
bottom right of the page.
Version3 Search on the left, Navigation on the right andLogin on the right of the page
bottom right of the page
Table 2.4Result summary of blind spots noticed for ‘ads’ widget across versions
2.6.2.4 Interest regions for login box/link
From the following table, It can be noted that the bottom-center of the page was never looked at,
which is thus the blind spot for the login box/link. The top right of the pages was found to contain the
hot regions. The reason for this dominance of the top right of the page can be attributed to the present
trend of websites. Most of the websites today that require a user login have the login box in the right of
the page (e.g. Gmail, Yahoo, Orkut) or a login link in the extreme top right of the page (Twitter, Rediff).
Version Description Blind spot (s)
Version1 Search on the top right with logo, Navigationon the top and ads on the right of the page
bottom left, bottom center and bottom right ofthe page
Version2 Search on the left, Navigation on the left andads on the bottom of the page
bottom left,bottom center and bottom right ofthe page.
Version3 Search on the right, Navigation on the rightand ads on the left of the page
bottom center of the page
Table 2.5Result summary of blind spots noticed for ‘login’ widget across versions
These results can be summarized as follows.
Widget Hot Interest Region Blind spots
Search Top left and top right corners Bottom left and Bottom centerNavigation pane Top left corner and top center Bottom rightAds Top center and top right corner Bottom rightLogin Top right and top left corners Bottom center
Table 2.6Result summary of eye tracking study
2.7 Conclusions
Our experimental results suggest that there are some interdependenciesbetween location expecta-
tions of some widgets. Search is expected to appear with the main content of thepage and a login button
19
is expected to appear with the navigation links of the page. Location expectations of ads and navigation
links are independent of any other web widget and they are expected to appear on the right and left side
of the page respectively. Each web widget has a blind spot in the web page. Placing a widget in its
blind spot will take more time in locating it, thereby decreasing the usability of the web page. If these
dependencies are followed as heuristics in designing a web page, a userwill save time in searching for
a widget and thus usability of the page can be improved.
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Chapter 3
REM-Ray Exploration Model that Caters to Exploratory Search
3.1 Introduction
In general, the users of any information system can be divided into two distinct groups - those who
query the system and those who explore the catalog [32]. Though the keyword-based search approaches
have catered to the needs of the former group, very few models exist [17, 46, 29, 52] which enable a lay
user to browse through the complete catalog. The reason for this can be attributed to huge databases with
a multitude of attributes, which present tough challenges for researchersin designing usable interfaces
that facilitate easy catalog exploration. Exploration of an information system has to be useful for a
layman to discover relevant information.
Search models have some limitations over catalog exploration models such as: theuser has to have
some search item in his/her mind, the user has to know the exact or nearest keyword of this search item,
the user has to go through the filtered results before finding the desired result and the search providers
have to use several optimizations to provide an efficient search in less time. For instance, consider a
user who needs to buy an electric oven. In this case, he/she does not necessarily have a search keyword
to start with. A user, in this case, will just have few specifications about thesearch object from which
the necessary keywords have to be formed. Canonical search interfaces which function with a list of
keywords do not seem to fit in this scenario. Same is the case with browsing huge catalogs such as music,
e-commerce and digital libraries etc. The key to this problem is exploratory search [54]. When catalog
exploration models are modeled with usable interfaces, they overcome these limitations. One probable
shortcoming for such interfaces is the time spent by the user on a query. Inthe research presented here,
we strive to provide a model that cuts down on this and enables user to pleasantly and effectively go
through the catalog before he/she zeroes in on the desired result.We use the principles of faceted
navigation to achieve this.
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3.2 Faceted Navigation
Faceted navigation is a proven technique for supporting exploration and discovery and has become
enormously popular for integrating navigation and search on vertical websites. Its popularity is attested
to in part by the fact that content management architectures, such as Solrand Drupal, contain support
for faceted navigation. Despite its widespread use, there are design challenges inherent in build- ing
the interface for faceted navigation. The two biggest challenges are: (i)poor choices in the design
can lead to decreased usability of the interface, and (ii) large category systems, especially subject-
oriented category systems, are still not well-supported in the interface. Facets refer to categories used
to characterize information items in a collection. A facet can be flat or hierarchical; in either case, a
set of labels is associated with each facet. In an information collection that supports faceted search,
multiple labels are assigned to each item, unlike a strictly hierarchical system in which items are placed
into single categories or folders. In other words, these bear some relationship to social annotations or
tagging[20].
3.2.1 Usability Issues
The biggest usability issue with faceted navigation is how to show the facets ofheirarchical metadata
without crowding the display or confusing the user. Though there is an advantage that it allows user to
see all options at a glance, if the number of options are more it becomes a bane to the system.
3.3 Related work: A Brief Survey of Catalog Exploration and Visualiza-
tion Models
There are two broad kinds of catalog exploration models. The first category of models is visualization
models that are concerned with the display of complete catalog. These are not primarily intended
as interactive exploratory models. The second category of models aim at selectively recommending
the information based on what user has searched for [49, 28]. Thesemodels provide better browsing
interfaces since there is no data cluttering. Here, we provide a brief survey of these approaches and web
models that fall into the second category. They employ different approaches in retrieving data [51] for
catalog exploration such as faceted browsing and collaborative filtering.
3.3.1 mSpace model:slicing through n-dimensional space
While keyword searches rely on user’s domain expertise to retrieve appropriate results, category
searches provide the user an overview of the range of the data in a domain. Some popular category
searches have a limitation that they rely on the fixed hierarchical structure of the categories. mSpace
model [16] effectively covers this slack. It allows the users to arrangethe n-dimensional space in such a
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way that they can slice through it and this slice can be altered in its scope, orientation and arrangement.
In other words, a hierarchical representation of the dependence of attributes in that hierarchy is shown in
that slice. The main advantages of this model for domain interaction are that users can easily perceive the
scopes and relations within the domain from the relative attributes and they canexplore the information
based on their interests by changing the orientation of the design. mSpace’sinterface consists of columns
that show the user a set of options to choose from. A user’s interaction in this model is from left to right.
The selection made in one column effects the options that are shown in the subsequent columns. Each of
the columns represents a facet and a selection within a column is like specifyingone dimension among
the multi dimensions of the data. The interactions between columns are termed as constraints and they
are expressed as class expressions in the description logic language. There are two types of constraints-
type constraints that govern the type of options listed in a column and selection constraints that represent
the relation between a particular column and other columns in the layout. One of the disadvantages of
mSpace is that the construction and composition of query patterns is very clumsy when more than one
selection is made in a column.
Figure 3.1Screenshot of mspace model used for browsing through data of research papers
3.3.2 Phlat:tagging UI
PHLAT system [12] was designed to interact with personal data. All the data is indexed by the
Windows Desktop Search (DS) indexing. Phlat system allows users to hierarchically add metadata
and tags to the data. These tags are directly added to the data. The advantages of doing this rather
than maintaining a new tag database are: 1. They are indexed by Windows DSlike other metadata 2.
Tags and data can be easily ported together 3. Single tagging system can beused for files of different
extensions. The interface of Phlat consists of 3 main areas: query area, filters area and results area.
Query area contains the text box in which keywords have to be entered orcan be chosen from the words
23
that are auto suggested based on the key stroke. Filter area contains the key words on which a filter
can be applied before querying the system. Phlat includes the filter in the query instead of in the results
to get rid of the stuck-filter problem. The results area contains the results that are generated based on
the structured query with properly reinforced filters. The advantages of this system are that it supports
tagging UI, which allows the users to generate their own metadata. The disadvantages of this system
are: 1. Because it runs on desktop search engine that is independentof file and email system, changes
made to the objects take time to get reflected causing obstructions to search 2. Moving files to a file
system that is not supported by Phlat will cause in the loss of tags. 3. Tagsare available only through
Phlat i.e., only in the retrieval time.
Figure 3.2Phlat screenshot
3.3.3 Mambo:Visual zooming approach
Mobile fAcet-based music BrOwser (Mambo) [13] uses visual zooming approach to arrange and
browse music data. Facet zoom subdivides a set of data according to hierarchically organized facets.
It is actually a tree visualization that displays each hierarchy in a horizontalbar divided into a number
of cells. Each cell in a horizontal bar represents a node in that hierarchy level. Because the purpose of
mambo is to provide interactive search but not to display the entire hierarchy, only a subset of all the
levels are displayed at a time. To facilitate visual distinction and navigation amonglevels, each level has
a different background color. To effectively view the data with long labels, the orientation of the widgets
can be changed from horizontal to vertical. The users can filter the results either by using pan-and-zoom
navigation or tap-and-center interaction. Quick jump to other levels is also supported.
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Figure 3.3Screenshot of mambo browser for browsing through songs
3.3.4 Audiomap:Graphs and nodes
Audiomap1 enables the users to discover and buy music of artists similar to a given seed artist. Once
we input the name of the desired seed artist, the application fetches similar artistsconnecting them to the
seed artist. The user can further explore the artists by expanding each artist node to fetch more similar
artists. The graph is updated and new connections are established between the nodes already present
and the new ones. The links are established based on the data obtained from last.fm and amazon. This
model uses user-generated metadata in last.fm and descriptive metadata in amazon to explore the music
artist catalog of amazon.
1http : //audiomap.tuneglue.net/ - Last visited on 30th April 2010
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Figure 3.4Audiomap screenshot displaying search results for keyword ‘boney’
3.3.5 Elastic Lists:faceted navigation
Elastic lists2 [46] are used to browse data with multiple attributes. They are inspired from faceted
navigation models. Users can select a value from the list entries for each of the attributes and the
list entries for the remaining attributes are displayed based on their selection.If the user creates an
impossible configuration, the displayed results are reduced to the nearestpossible configuration. Elastic
lists enhance the relevance of the metadata values by size and the characteristics of the metadata weight
by brightness. A sample application provides a facet browser of noble prize winners.
2http : //well − formed− data.net/experiments/elasticlists/ - Last visited on 30th April 2010
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Figure 3.5Screenshot of elasticlists for browsing through data of noble prize winners
3.3.6 Videosphere:spherical model
Videosphere3 uses a sphere model to connect related videos and allows user to browsesimilar
videos. All videos are placed on the surface of the sphere and are connected with lines when similar.
The necessary data is taken from user-generated and descriptive metadata of the videos.
Figure 3.6Videosphere screenshot
3http : //www.bestiario.org/research/videosphere/ - Last visited on 30th April 2010
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3.4 REM-Ray Exploration Model
Figure 3.7A prototype design for REM with terminology used in it
In this section, we put forth a novel and intuitive approach that builds upon the techniques discussed
above. Our approach draws the benefits of facet navigation overcoming few of its drawbacks [20, 30].
The limitations of facet browsing include the following: 1. Loss of browsing context, 2. Complex
relationships between two facets are difficult to show, 3. The user interfaces that support facet browsing
are often complex, 4. User needs to understand the used faceted classification and its paradigm; and 5.
It is difficult to access the popular items that are nested deep in the hierarchy. REM overcomes these
limitations in its own way with the help of connected graphs. We present a general overview of the
system and later demonstrate it with the help of a sample query. We choose Indian music to be our
working dataset. The metadata of Indian music has several descriptive attributes such as actor, music
director, lyricist, composer, album, year of release, singer etc. It is alsopossible that in a record, two or
more attributes have the same value and there are two or more values for the same attribute. The major
challenge lies in effectively depicting these inter-attribute relations and multiple roles in a way that can
be comprehended by a layman as cues for effective navigation through the catalog.
3.4.1 Design
Unique values for each metadata attribute are considered as independentnodes which we call suns.
If values (Mdv1 andMdv2) for two metadata attributes (Md1 andMd2) co-occur in the descriptive
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metadata of at least one song, then a ray emerges from the sun representing Mdv1, setting on the
sun representingMdv2 and vice versa. In other words, such inter-relations between values ofvarious
metadata attributes are used to organize the music data in two dimensions in such a way that any two
suns that are connected satisfy the above condition. To allow simple browsing through the music data,
we conceived a see-what-you-click approach. This avoids the data clutter on the screen which does not
make the screen complex like in most facet browsing interfaces. Our model employs the aforementioned
connected suns to arrange music data. Each sun represents a unique value for a metadata attribute and
each such attribute is represented as a ray that can be followed to reach aconnected sun. These inter-
attribute rays are useful when the user wishes to combine various metadata attributes in a single query
and also in exploring the catalog using multiple metadata attributes. In other words, the inter-attribute
rays span the breadth of the catalog. We call this approach Ray Exploration Model (REM). We now
define the basic components that constitute REM.
Figure 3.8REM-different screens
Observe the changes in each screen with regards to dock, ray labels, source and syncs. Notice that
the rays are void of handles in (d) where few rays link a single sync to the source.
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(a) Results obtained for initial input keyword are shown with diameter proportional to their relevance.
(b) The exploration screen with selected sunA2as source.
(c) The expansion of actor ray into subsets.
(d) Further expansion of a subset of the actor ray.
(e)A1 as a new source on its selection from syncs ofA2 in the previous screen.
Figure 3.1 gives an overview of the terminology we use throughout this paper. The sun at the center
of the screen is called the source. The top k suns from the catalog, which share maximum songs with the
source are shown connected to source, and are called as syncs. If the desired sun for further exploration
is not found in syncs, the user can choose to expand a ray by clicking onthe ray-handle. If the ray
connects a single sync to the source, then the ray will not have a handle. At any point of time during the
exploration, the model facilitates to mark the sun at the center of the graph as amandatory criterion by
clicking on mandate button. By this, the catalog is reduced to a subset where all nodes need to have the
corresponding criterion enforced, i.e., if the source has a valueMdv1 for a particular metadata attribute
Md1, and it is made a mandatory criterion, the catalog is reduced to a subset where the criterion that
Md1 has the valueMdv1 for all the songs considered for further exploration. When the user marks the
source as mandatory, the source, syncs and rays that are shown in further screens will be in accordance
with the intersection of the choices made until that point of time. These choices can be revoked later at
any point of time, and in any order, using the dock which contains such choices. The songs which match
the intersection of the choices in the dock are shown below/beside the exploration screen in a playlist.
The dock and the source together keep the user informed about his/her location in exploring the catalog.
Hence, the chances of a user getting lost in the process of exploration are very minimal.
3.4.2 Exploratory Search
To avail the exploratory search of REM, initially the user has to type in a keyword in the search
field. Unlike using values of each and every attribute as input to a traditionalkeyword-based search
systems, the user here can start with just one keyword to identify his/her query. All the suns in the
catalog which are related to this keyword are displayed. The diameter of each displayed sun is directly
proportional to its relevance with the keyword. When a user selects a sun by clicking on it, the sun is
made the source and all its inter-attribute rays are displayed. Each of theserays is in turn connected
to a sync which hosts the value of a metadata attribute corresponding to the connecting ray. If the
user finds a relevant sun in the syncs, he/she can choose the sync. Ifnot, the user has to click on the
ray-handle to expand the selection based on the respective attribute. When a ray-handle is clicked, the
data values of the metadata attribute corresponding to the ray are categorized and subsets are formed
in the alphabetical order. The inter-attribute ray expands into a number of intra-attribute rays, each of
which represents the aforementioned subset. All the remaining inter-attributerays collapse to a single
ray which we call collapsed ray. It serves to navigate back in the history of exploration. The collapsed
ray expands again by clicking on its handle. If intra-attribute rays collapseto a single ray then it is
named asMdi: collapsed ray, whereMdi is the name of the corresponding metadata attribute. These
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intra-attribute rays are again connected to the syncs that are chosen by their statistical relevance to the
source. The statistical relevance quantifies the popularity of the node. Asthe popular nodes are often
the most accessed nodes, when buried deep in the hierarchy they are difficult to access. At this level of
exploration, these most popular items are displayed as syncs, which allow theuser to choose among the
most popular nodes without much toiling for their information. Thus, in a see-what-you-click approach,
REM provides an easy access to popular nodes. The new intra-attribute rays are labeled with the first
letter of the data value of the syncs they are connected to. If the user doesnot find the relevant sync,
he/she once again has to click on an appropriate ray-handle and this process repeats recursively till the
user homes in on the desired sync. This sync can be now made a source and the catalog can be explored
further using its rays. Because these rays will now be the inter-attribute rays, an inter-attribute ray can
be selected to find a desired value of that particular kind. This is how REM handles multiple selections
within same facet. Analogous to inter-attribute rays, the intra-attribute rays span the depth of values for
each metadata attribute in the catalog. On selecting the desired sync, it immediately assumes the place
of the source, and the process is repeated with this new source.
We will walk through the steps in this model with an example query to appreciate it better. Consider
that a user wishes to listen to duets of actorsA1 andA2 which are sung by singerS1 and composed
by composerC1. Figure 2 illustrates the steps involved with screenshots. The user starts bygiving a
keyword that relates to any ofA1, A2, S1 andC1. Let us suppose that user has searched forA2 and
the system displays the results. The user proceeds to the next screen byselecting a relevant sun. In this
screen, the selected sun is made the source and is surrounded with syncsconnected by inter-attribute
rays. The user makes the sourceA2 mandatory and this choice is updated in the dock. Now, the user can
select an actor or a composer or a singer to proceed with his/her search.Let us suppose the user decides
to look for an actor and clicks on the actor ray. Then, the inter-attribute raycorresponding to metadata
attribute actor is expanded which presents him/her a screen with intra-attributerays setting on syncs
which are subsets of values of metadata attribute actor. The user has to select that alphabetical group
which containsA1. On selecting a ray corresponding to the correct alphabetical group, all the suns in
that group are shown as syncs and nodeA1 is selected. In the previous step, it is not necessary that the
user expands the actor ray; singer or composer rays can be chosen as well. In any case, the procedure
stands the same. The user has to make the source a mandatory sun whenever it matches initial criteria -
A1, A2, S1 andC1. The songs which match the intersection of choices in the dock are output atevery
step in the playlist. So, in the end, the user will have those songs which are acted byA1 andA2, sung
by S1 and composed byC1.
3.5 Conclusions
In this work, we introduced REM, a catalog exploration model that uses connected graphs and facet
classification. The uniqueness of this model lies in the following: 1) its simple interface, 2) its ca-
pability to handle complex search queries which may involve multiple selections in a facet, and 3) the
31
underlying approach for navigation using connections between values of metadata attributes. We termed
this approach as see-what-you-click. We claim that our model is superiorto other traditional models
based on facet browsing because REM models the user interactions whenthey are browsing through
multi-attribute data to the nearest possible.
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Chapter 4
Implementation and Evaluation of REM
4.1 Introduction
In the previous chapter, we introduced REM-Ray Exploratory Model that uses faceted classification
to browse through huge music catalogs. It is necessary to perform a usability study on this model to
evaluate it. The reports of this study will help us understand the potential andlimitations of our model.
This chapter discusses implementation and evaluation of REM. Evaluation of exploratory models is
itself a research problem [56] [42] [55]. We first discuss the current practices and then the method we
used to evaluate our model.
4.2 Current Evaluation Practices
Catherine [42] reports four types of such practices based on the results from a survey.
1. Controlled experiments comparing design elements: In these studies, specific widgets (e.g. al-
phaslider designs [3]) or mappings of information to graphical display [26] are compared.
2. Usability evaluation of a tool: These studies provide feedback on the problems users encounter
with a tool and how the design has to be redefined [9] [58].
3. Controlled experiments comparing two or more tools: These studies are used to compare a novel
technique with the state of the art model.
4. Case studies of tools in realistic settings: Because these kind of studies are conducted in realistic
settings, they report the feasibility and in-context problems and usefulness. However, they are
time-consuming and results may not be generalizable [50].
33
4.3 Our Evaluation Methodology
Because using REM is an explorative task, users have to browse through the data set to find interest-
ing items. Our tool provides a new way of grouping related items based on the features of the content
rather on the metadata. To the best of our knowledge, there is no other exploratory tool which does
a similar grouping which can be used for comparison. However there are several models with similar
purpose and similar tasks (exploratory) [16] [13]. Hence, our methodsare based on earlier studies [25].
Thus, our evaluation method is mainly focused on feedback on the visualization design and assessment
of utility of such a tool in exploring music data.
REM was evaluated using both objective and subjective performance measures. The objective mea-
sure was the error rate of the completed tasks, while the subjective measurewas the user satisfaction
with the tool. The user satisfaction was measured using a set of tool-specificquestions. We used Likert-
scale items to measure user satisfaction. The users were also asked to explain their ratings to understand
user expectations and the areas in which the model failed. REM offers a different way of browsing
through music catalogs, hence user satisfaction with the model is very important. Quantitative results
from this measurement, combined with observation and comments from the participants, can give valu-
able feedback for improving the visualization tool. The three main aspects tested in this model are: 1)
How useful and effective it is to display data based on the similarity in features. 2) How effective it is
to visualize music as suns and rays and 3) how well the model works in helpingusers do the normal
browsing they do with typical music exploratory models (seeing the filtered playlist based on selection,
revoking the selection made, etc.)
4.4 Participants
The evaluation study was performed on 13 participants (8 male and 5 female).Average age of the
participants was 22. All of them are advanced users in terms of the usage of Internet. As a prerequisite,
the subjects had to be familiar with the film music on which this model was implemented. Ona scale
of one (beginner) to five (expert), the participants self rated their knowledge about the film music as an
end-user at level three or above (two at level three, eleven at level five).
4.5 Apparatus
REM stands for Ray Exploration Model. It was implemented as a web-based application using
Raphael JS library and PHP-MySQL. A pre-processing module computedthe tags and similarities in be-
tween songs based on the features like pitch, raga and tempo. This data wasstored in MySQL tables and
was shown in sun-ray visualizations using vector graphics. The music dataset we used contained about
330 songs of various artists. All the songs chosen attracted regular audience and are pretty renowned.
34
4.6 Details Of Each Screen
REM essentially consists of three screens. The start screen is for the initial search and contains a
small text box which inputs the key word from the user. The second screen displays the matching results
in the form of colored circles. Each circle can be clicked to expand further. When a circle with a data
value is clicked, it opens to a new screen which resembles sun and its rays.The sun is the current data
value and each ray corresponds to each attribute in the dataset. The related items are displayed according
to the similarity in between the features of the sun and attribute data value. Each ray consists of a handle
which can be clicked further to expand, thereby viewing more of that attribute. This selection can be
revoked by clicking the “view-all-ray”. A playlist of all the filtered songs based on the selection appears
on the right side of the screen which can be played.
Figure 4.1REM-Screenshots
4.7 Procedure
Participants were introduced to the purpose of the study and the meaning of the model was explained
clearly. Because all participants had used some model earlier (either search/exploratory), they were
introduced to the new features of REM directly. The tasks were designed insuch a way that they test
the participant when he/she is exploring the model for the first time and after they have learned about
the model by using each and every feature of it. There was no time limit for completing any task. The
tasks were divided into three main categories:
35
• Browsing tasks: Finding an entry of a data item, identifying the related data items based on
attributes.
• Navigational tasks: Finding a data item related to another item, revoking the current selection.
• Overview tasks: Understanding the visualization, identifying the current item, getting the sense
of playlist and its items.
User responses were also taken in text so understand what difficulties they faced when performing
a task. Most favorite and least favorite features were noted for each user. Also suggestions about any
feature that was not currently accommodated in the model were noted.
4.8 Results
Each user spent less than five minutes to get acquainted with the system. The error rate in the
learning tasks was approximately zero. The whole experiment with one usertook around 20min to
complete.Users were in general satisfied with the model. The error rate was low and the satisfaction
level was high. Overall, this model received positive response from participants.
4.8.1 Error rate
Out of 10 tasks, the number of mistakes made by the participants ranged fromone to three. Out of 13
participants, Seven participants performed all the 10 tasks correctly. Oneparticipant made 3 mistakes,
three participants made 2 mistakes and two participants made 1 mistake. In total outof 130 tasks by 13
participants, 13 tasks were performed incorrectly which means error rateis 10%.
The most common mistake was done in revoking the selection using view-all-ray.The view-all-ray
was not distinguishable from the other attribute rays as stated by some participants whereas some opined
that the name was not apt. It would have been more accurate to make it more evident for navigating
back for another selection. This has been noted down as a suggestion.
4.8.2 Satisfaction with the tool
The questionnaire used for measuring user satisfaction in the functionality of REM was a fixed-scale
with five points: not at all easy,not easy,no comments,easy and extremely easy. Overall, the participants
were highly satisfied with the model. Many tasks were rated“extremely easy”and“easy” . However,
tasks like “revoking a selection” were rated as“not easy” by five participants. This indicates that some
improvement has to be done in this section. Other ratings can be seen in the tablebelow. In addition
to rating the task-specific functionalities, participants also rated the visual appearance and terminology
used. All of them were extremely satisfied with the visualization and this got the highest amongst all
the ratings. Most participants thought that such a visualization is novel andalso apt.
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No. Questionnaire items Not at all easy Not easy Comfortable Easy Very easy
1 Searching for a data item was 0 0 0 0 132 Finding a matching sun from the dis-
played values was0 1 2 6 4
3 Identifying the current sun in the screen 0 1 0 1 114 Finding the most relevant syncs of cur-
rent sun was0 0 0 5 8
5 Finding all syncs related to an attributewas
0 1 0 5 7
6 Navigating back to make a new selec-tion was
2 6 0 3 2
7 Viewing all the related songs was 0 0 2 3 8
Table 4.1Summary of the results of the task-specific questionnaire
“Colorful interface and innovative way of visual approach to navigation.”
Identifying the clickable and non-clickable parts on the screen was a problem to some users. One of
them rated it very low whereas eight users rated it high/normal.
No. Questionnaire items Not at all easy Not easy Comfortable Easy Very easy
1 Understanding the sun-ray visualiza-tion was
0 1 6 4 2
2 Understanding the relationship in be-tween connected items was
0 1 4 4 4
3 Understanding the meaning ofhandle/ray-collapse was
0 3 3 2 5
4 Identifying the clickable/non clickableparts on the screen was
1 4 3 2 3
5 Understanding the playlist generationwas
0 1 0 5 7
6 Learning the system was 0 1 6 4 2
Table 4.2Summary of the results of the screen-specific questionnaire
Learning the system was easy to major of the users and understanding the playlist generation was
also majorly intuitive.
“This is very very easy to learn. Requires no practice to use.”
The users’ overall satisfaction with the model was also high. The most favorited feature was the
visual appearance and the least favourited feature was navigating back to make another selection.
“I like how the navigation is shown but do not like how it is done in going back.”
This is mainly because some participants expected the view-all ray to display more data items of the
attribute currently shown. Some of them opined that navigation to a previous selection has to be shown
in a more clear way.
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No. Questionnaire items 1 2 3 4 5
1 Satisfaction with the search screen 0 0 3 5 52 Satisfaction with the visualization 0 0 4 4 53 Satisfaction with the flow of the system 0 0 4 5 44 Satisfaction in viewing the playlist 0 1 4 5 35 Satisfaction in going back to the previous selection0 3 4 4 16 Overall satisfaction with the system 0 2 0 10 1
Table 4.3Summary of the results of the user-satisfaction questionnaire (scale of 5)
Some desirable features by the users were that the number of data items shown in a screen be con-
trolled by the user. The current model limits the number of data items to those that have matched
beyond a threshold value. User controlling the number of items shown is not implemented yet and has
been noted down as a suggestion. Some of the other interesting comments by theusers are listed below:
“All possible metadata attributes are considered. I like it.”
“The style of the interface is novel and good.”
“User with minimum knowledge about all attributes of a song will be very much benefited.”
4.9 Conclusion
We discussed the design, implementation, and evaluation of REM: an exploration tool for facilitating
exploration of music data according to feature similarity. The tool was evaluated using various methods.
First, the design and implementation of the model was explained. Second, the usability of the tool was
evaluated by using subjective and objective measures. The results of these measures showed that user
satisfaction was high and the average error rate of the given tasks was low. Third, the study explored
the reasons behind the user satisfaction ratings qualitatively, using observation and comments from the
participants.
REM is superior to its competitors (discussed in earlier chapter) in the followingways:
a) Composition of complex query patterns is not difficult in this model becausemultiple selections can
be made in a single column (facet) just like any other query unlike Mspace model [16].
b) This model is portable without loss of any information unlike Phlat [12].
c) Occupies less (and fixed) screen space and hence can be used in hand held devices unlike videosphere1
and elastic lists [46].
d) Because the model visualizes grouped data based on their similarity in content, this similarity measure
1http : //www.bestiario.org/research/videosphere/ - Last visited on 30th April 2010
38
can be varied according to the user’s choice and the same model can be used for exploration using that
measure. The current implementation of the model does not have an option to switch in between such
measures but addition of such a feature will prove to be of good use.
Option to avail this feature is absent in all other exploratory models (to our knowledge).
4.10 Limitations
One prime limitation of REM is the difficulty in the selection of similarity measure to group data. At
this point, we do not work on the betterment of this measure. We hope to take it astep further in future
research.
39
Chapter 5
Effect of Polarity of the Traces of Interaction History in Reading Blog
Posts
5.1 Introduction
Recently, web sites have started moving from static to interactive mode. Blogs are such websites
which are distinguished by the user interaction with them. A typical blog is an online dairy written
by an individual or a set of individuals with entries of commentary, description of events or multime-
dia and is interactive allowing the readers to comment their response on eachblog post (blog article).
That is, a user can not only read the static document but also actively writehis/her opinion below the
article and can also respond to the opinions written by other readers. Today’s blogging platforms like
wordpress,blogger,posterous,tumblr,etc support user responses in theform of both text and media. In
our study, by the term ‘comments’ or ‘responses’, we mean only the text responses. These comments
may contain some valuable information which can help user decide in reading thearticle or not. The
importance of comments is usually not very obvious because not many readers completely read all the
comments an article has received and judge its potential [18] [25]. However, some studies [25] [24]
propose that number of responses indicate the degree of interestingness in an article. In research on
blogs, not much study has been done on the type of the comments and its role in determining user’s
interest in a blog post.
The type of comments can also play an interesting role in navigating through blogarchives as ex-
plained below.Navigation in blogs is an interesting research topic1. Previous related work [27] tells us
that navigation of any website is very important for the user to discover interesting articles and revisit
the site. Current navigation in blogs is of two main types.
1. Calendar navigation: All the articles in a blog are listed according to their timestamps and the
reader has to browse the posts in reverse chronological order.
1http : //vandelaydesign.com/blog/blog − design/navigation− issues/ - Last visited on 15th February 2011
40
2. Tag cloud navigation: All the posts in a blog are generally tagged. To explore, the reader has to
select a tag to view all related posts to that tag [27] [35].
These two navigational patterns do not provide any cue to the user aboutthe number and type of com-
ments on blog posts on a particular topic. To see posts with varied comments, the user has to manually
go to each post and look at the comments section.
Those readers who like to browse through blog archives according to the type of comments on the
posts will find it easy if the type of comments is clearly visualized so that they need not go through
the entire comments section. This kind of navigation that enables such readers to explore the posts
according to the type of responses will thus save their time and effort hence adding value to the current
navigation in blogs. But before proposing such a navigation model, we first need to establish that users
will be interested in knowing the type of comments on the post before reading the post.
This study is an experiment to test if type of comments on the post really affectreader’s choice in
choosing it to read. We first discuss the related work and the hypothesis of this study. Then we talk
about the initial quantitative survey that we conducted followed by the actual experimental design. We
then discuss the results and finally conclude how this can be used as navigation support for exploring
blogs.
5.2 Related work
We first describe the work related to visualization of information spaces andthen brief the work done
on the user comments on articles in these information spaces.
Takama [48] proposed a visualization method of news distribution in the blog space. The types of
objects that are to be visualized as well as their relationships are defined, based on which interactive
information visualization system is proposed. Experimental results show thatusers can examine news
distribution in Blog space from various viewpoints, which affects their estimation of the impacts of news
articles.
Fujimura [14] proposed a method for displaying large-scale tag clouds. They used a topographical
image that helps users to understand the relationship among tags intuitively as abackground to the tag
clouds. This topography is applied to the blog navigation system and it is easyfor the users to find
the desired tags easily even if the tag clouds are very large. It also helps inunderstanding the overall
structure of tagged posts.
Gregory [18] presented a methodology for blog analysis using a mature document visualization
tool. In this study, IN-SPIRE, developed by Pacific Northwest National Laboratory, is used to analyze
the content of blog data. In addition to stated above, there has been much research focusing on the
visualization of blogs or web search results [53] [57].
Visual representation of computational wear [21] on document processing depicts the interaction
history of author (edit wear) and reader (read wear) with the document.This work suggests that reader’s
interaction with the document has a novel utility- category indices will help readers find other readers
41
with similar interests. Read wear visualization relates to reader history and is recorded based on the
number of seconds a reader pauses on a given line. Read wear and its variants are found to improve
navigation through information spaces (As cited by Indratmo [25]).
Social navigation refers to the movement of a user from one piece of information to another piece
where this movement is influenced by the activity of other users in the information space [24]. For
example, while browsing a discussion forum, users may select a particular message because they see
that the message has received a high rating from other users. Such a decision is essentially made based
on observation of what others have done to the message.
Studies done by Indratmo [25] reveal that decision to select which entriesto read in a blog is effected
by many factors like length and number of comments on the entry, besides topic and time of posting.
iBlogVis, a desktop application, was built on the hypothesis that providing anoverview of a blog and
user interaction history will help users to browse through a blog archive.Evaluation of iBlogVis reveals
that visualizing comments will be useful when users do not want to have to retrieve specific information
but rather learn about the information space to find interesting information.
The above works suggest that reader interest in an article will be affected by the interaction history of
other users with that article. However, mere count of the number of comments isnot a good measure to
determine the potential of a post. This is because comments from users may be meaningful, random, or
debate. That is, it is possible that a few comments on a blog post are not in accordance with the post/not
related to the post and yet the number of comments is high. Hence it is also important to know the type
of comments rather than just the number of comments.
Spectrum [4] is one such system presented by BBC for visualizing the debate sparked by the BBC
White season of programs which aired on BBC2. It classifies comments of online discussion and vi-
sualizes them. Each comment is represented as a colored circle according toits feelings. This work is
similar to wefeelfine2 developed by Harris and Kamvar [19].They developed a tool that looks for the
phrases ‘I feel’ and ‘I am feeling’ in blog entries and extracts human feelings from the entries along
with information about the bloggers (age, gender, and location) and the local weather conditions while
the entries were posted. This information is saved; the feeling is identified (e.g., happy, sad) and then
visualized as a particle. The attributes of a particle (e.g., color) representsome encoded information. A
particle can be clicked to see the full sentence that describes the human feeling.
It is difficult to derive the interestingness of each blog entry in visualized view by Spectrum because it
only provides information about the emotion of the comment. The relevance/irrelevance of the comment
to the post is yet unknown. There is also a chance that the analyzed key terms being misinterpreted.
TRIB, a visualization system built for graphically depicting numerous replying messages in blogs,
is closely related to our work. It is based on the analogy of solar system and visualization and gives
an interactive 2D layout interface to show the whole collection of responding messages. The textual
clustering of replying comments for the main subject articles is shown in this mode.The previous and
the next responding messages can be navigated in 2D layout form of TRIB [31].
2http : //wefeelfine.org - Last visited on 15th February 2011
42
With these initial foundations, we believe that visualization effectively helps inhandling huge num-
ber of comments. Hence to test our hypothesis, we visualize the type of comments for each post. The
next section contains our hypothesis in detail.
5.3 Hypothesis
We hypothesize that type of comments of on each blog post will also contributeto the user interest
in reading that post. We limit our current study to only the polarity of the commentin terms of its
agreement with the article. So if our hypothesis is true, then the reader’s interest in a blog post will
be affected by its comments’ polarity distribution. This can be used to suggestsimilar posts or arrange
posts based on the polarity distribution of comments. This will be beneficial to those users who are
interested to know this data while reading posts thus increasing usability of the blogging website.
5.4 Online survey- Feasibility test
Main experiment was preceded by a small survey on about 27 participants(19 male and 8 female, all
of them are advanced users of Internet in terms of time they spend on web). The survey was conducted
to understand and test the feasibility of our hypothesis-type of comments influences a reader’s interest
in the post. In this study, the participants were asked to choose among the several factors that affect their
interest in reading a blog post. Results from the survey show that nearly 59.3% of the participants (50%
female and 63.15% male) have interest in choosing to read a blog post basedon the polarity distribution
of the comments it received. It is to be noted that the validity of hypothesis cannot be claimed from this
survey. It has to be methodogically tested on participants and the results have to be statiscally analyzed.
5.5 Method of the experiment
5.5.1 Participants
The experiment was conducted on 15 participants [9 male and 6 female]. Theaverage age of the
participants was 23 and all of them are graduate and post graduate students with computer science as
their major. All the participants are advanced users of Internet in terms of timethey spend on web.
5.5.2 Apparatus
The experiment was done on a dataset from Metafilter.com, a community weblog3. Four categories
of posts were selected- war, technology, politics and neuroscience. Each category had five posts in turn,
thereby totaling to 20posts. Each post in each category had a minimum of 55 comments. To disable
3http : //metafilter.com
43
Figure 5.1Percentage of users reading posts based on polarity distribution of comments
the effect of writer’s style on the reader, all the posts in a category werechosen from a single author.
Also, to enable randomness in style, each category had different authorfrom another. The polarity of
the comments with respect to the post was calculated using sentiwordnet4 released in LREC’10. The
polarity was normalized with respect to the number of words in each comment.
5.5.3 Design
The experiment primarily consists of set of tasks to be performed on a set of posts in the presence and
absence of polarity distribution of comments of each post. Given the polarity distribution of comments,
the reader had to also rate his liking on the post on a scale of 5. The screenshowing this polarity
distribution was same for each participant.
5.5.4 Tasks
Each user was initially presented a screen that briefs about the experimental procedure. After the
user clicks to begin the experiment, he/she had to choose a topic among the given four topics which
opens to a screen showing polarity distribution of comments plotted on a column graph for each post.
The user had to primarily state the order in which he/she would like to read the posts and then read each
post. After reading each post, the user had to give his liking on a scale of 5. The same procedure had to
be repeated for all the other categories in order.
Similarly, each user had to state his/her preference order in reading the posts for each category when
no information about the polarity distribution was shown.
4http : //sentiwordnet.isti.cnr.it/
44
5.5.5 Variables
For each category, to analyze the effect of polarity distribution of posts,we used the grade of the post
as dependent variable and the presence of polarity distribution as the independent variable. By ‘Grade’
of a post, we mean the position of the post in the user given order. For example, if the participant had
given an order 54132, the grade of post5 is 1; post4 is 2 and so on. Similarly for each category, to
analyze the effect of positive and negative polarity of comments on users’ ordering, we used the grade
of the post as dependent variable and the polarity of the comments (positive/negative) as the independent
variable. To analyze the effect of positive and negative polarity of comments on users’ liking, we used
the user given liking as the dependent variable and the polarity of the comments (positive/negative) as
the independent variable.
5.6 Procedure
The participants had to do this experiment individually. There was no time limit on any task. Initially,
they were asked to rate their knowledge about the blog topics on a scale of five. Care was taken to see
that no participant was significantly ignorant/wise about the topics. That is,the participants’ knowledge
about the topic was approximately equal for each category. Then each participant was presented with a
screen to choose from the topics. For each selected topic, he/she was shown the polarity distribution of
the comments. The order of preference in reading the posts was noted down for each user. He/she was
then asked to read each post. After reading each post, the participants had to rate their liking on a scale
of five. With 20 posts and 15 participants, 300 data points have been recorded for the whole experiment.
Similarly, another 300 data points have been recorded as “user liking for each post”. Similarly, the order
of preference in reading the posts was noted down when no polarity distribution was shown to the user.
The participants were asked to think aloud all through the experiment and their thoughts and inputs have
been noted down.
5.7 Results and Discussion
5.7.1 Effect of polarity
To see if the presence of polarity made any difference in participants’ choices in choosing posts to
read, we analyzed the user given choices in both presence and absence of polarity conditions.A one-
way within subjects (or repeated measures) ANOVA was conducted to compare the effect of presence
of polarity distribution of comments on grade of each post for each category. Like discussed earlier,
grade means the position in which the post appears in the participant given order. The F-values and
significance values for each post are tabulated below.
45
Following table shows the statistical values for the analysis on various categories of posts. There was
a statistically significant difference between the presence of polarity and absence of polarity conditions
for some posts (post1 belonging to war, post1 belonging to politics and post1,post3,post4 and post5
belonging to technology). But for other posts, the significance (p>0.05) was low. This is discussed
below. The reason for the significance to be low for such posts can be attributed to the randomness in
S.No Post category Postspost1 post2 post3 post4 post5
F Sig. F Sig. F Sig. F Sig. F Sig.1 War 3.133 0.05 0.640 >0.05 0.00 >0.05 0.151 >0.05 2.236 >0.052 Politics 6.931 0.014 0.069 >0.05 0.197 >0.05 0.599 >0.05 1.890 0.053 Technology 2.922 0.05 0.205 >0.05 0.127 >0.05 0.016 0.05 5.036 0.0334 Neuroscience 1.635 >0.05 0.818 >0.05 0.201 >0.05 0.671 >0.05 0.000 >0.05
Table 5.1One way ANOVA results for effect of polarity
participants’ choice in choosing the next post to read after reading 1-2 posts. A participant choosing a
post at random means that he/she merely picks a post to read irrespective of any other factor. Two such
examples are given below:
” I do not generally read same type of posts at a time”.
Some users quoted that they do not read the same type of posts at a time. So in acategory, only their
first choices were genuinely made. The remaining choices apparently mighthave been made at random.
”I do not like this topic. So my choice is only random irrespective of polarity”.
Some users opined that when the topic is not of interest to them, they read at random. This behavior
has been noticed in nearly 48% of the participants (from the results noted from think-aloud session).
From the table, it is clear that the effect is polarity is significant on some posts(6 out of 20posts) and
majorly insignificant on the others.
5.7.2 Effect of positive and negative polarity on user’s order of reading
The posts in each category were divided into two types- (1) posts with major positive and (2) posts
with major negative comments. The grades given for each post were averaged and mean grade was
computed for posts of type1 and type2 respectively. Paired-samples T-tests were conducted to compare
the means of grades in positive polarity and negative polarity conditions. There was not so significant
difference in the scores for type1 and type2 conditions. The t and p values for each category are reported
in the table below.
These results suggest that neither of the polarities has a greater effectthan the other. Specifically, we
can say that the type of polarity of comments has no effect on participants’ choice of reading posts.
46
S.No Post category pairs t df Sig. (2-tailed)
1 war positive and negative -1.542 14 >0.052 politics positive and negative 0.539 14 >0.053 technology positive and negative 0.539 14 >0.054 neuro science positive and negative -1.461 14 >0.05
Table 5.2Paired T-Value tests of user given order for posts with positive and negative polarities
5.7.3 Effect of positive and negative posts on user’s likings
Again, the posts were divided into two types-type1 with major positive comments and type2 with
major negative comments based on polarity distribution. Because each user was asked to rate each post
based on his/her liking, each post had a rating associated with it. The liking/rating was averaged across
posts of same type and mean liking was computed for positive and negative posts respectively. Paired-
samples T-tests was conducted to compare the mean of likings to see which type of posts was mostly
liked by the participants. A significant difference was reported for type1and type2 conditions. The t
and p values for each category are reported in the table below. In the tables below, positive means posts
that have major positive comments and negative means posts that have major negative comments.
S.No Post category pairs mean Std.deviation
1 war positive and negative 1.9667 and 2.8 0.61140 and 1.014192 politics positive and negative 1.8333 and 2.4333 0.91937 and 1.293763 technology positive and negative 2.4444 and 3.0667 0.78343 and 0.798814 neuro science positive and negative 2.6333 and 2.0667 0.69351 and 1.16292
Table 5.3Paired sample statistics
S.No Post category pairs t df Sig. (2-tailed)
1 war positive and negative -2.474 14 0.0272 politics positive and negative -2.016 14 0.05 (app.)3 technology positive and negative -2.226 14 0.0434 neuro science positive and negative 1.679 14 >0.05
Table 5.4Paired T-Value tests of user given liking for posts with positive and negative comments
These results suggest that except for the category “neuro science”, all other posts in other categories
were mainly preferred to be liked if their comments’ polarity distribution was mainly negative. The
special behavior with this category of posts can be attributed to the participants’ background. All of
them rated that they have a minimum knowledge in neuroscience compared to other categories.
47
5.8 Conclusions and Future Work
From the analysis of survey and experiment, we have got mixed results regarding the hypothesis
of the experiment. Few posts (posts in technology category except post 1and post 2 in war and post
1 in politics) seem to be supporting the hypothesis but from the results of the other posts, this cannot
be inferred. However, it has to be cautioned that this experiment was performed only on the students
with computer science as their major. Participants’ background may also play avital role in their
choosing articles to read. To obtain more generalized results, this experiment has to be on conducted
on participants with varied backgrounds to see the difference. It is also found that type of polarity
distribution will effect the users’ liking of the post. Posts with major negative comments are preferred
to posts with major positive comments (except in neuroscience category). Evidently, the results also
vary according to the type of posts. If our experimental results regarding the hypothesis had been
supportive, they can be exploited to develop new user interfaces that facilitate exploratory browsing.
The basic notion of exploratory browsing is that people explore the data collection to find interesting
articles. New user interfaces with posts and visualization of polarity of comments will aid readers in
directly choosing a post to read rather than going through the entire commentssection. In specific, an
immediate extension to this work could be a new experiment to test the following on adifferent users:
• Visualization of polarity of comments helps users find interesting articles.
• It will reduce the effort of users in finding such articles.
• User satisfaction is more when they use such interfaces.
48
Chapter 6
Conclusions and Future Work
The interdependencies obtained inChapter 1 are in accordance with the related studies done by
Bernard [5] and Hinesley [22]. Our experiment has done this study on 4commonly used web widgets.
This study can be extended further to various other widgets to discover more interesting results. If
such interdependencies are taken into account when designing web pages, it becomes easy for a user to
identify a new widget based on the location of familiar one. This can also be experimentally tested.
REM, as discussed inChapter 2 andChapter 3 is of exploratory use to browse through music data.
Suggestions given by the users are implemented as a part of the study. Thecurrent model is on a selected
songs database which needs to be expanded for its more extensive usage. Current similarity measure
that is used to build this model is emotion (Raaga). Several other similarity measures can be applied to
REM without changing the functionality of the model.
Chapter 4 has more room for future work. Users’ background also has considerable effects in their
choice of reading. The experiment needs to be conducted on differentset of participants to see the
difference it makes. Also, the results from our study only suggest that polarity of user responses effects
reader’s interest in choosing a blog article only (depending on categoryof posts). This result will have
more empirical evidence if it is done again on users with varied backgrounds in different experimental
conditions. This can be extended further by proposing a new navigation among posts based on the
polarity. This will add to the current navigational models in blogs- calender navigation and tag-cloud
navigation.
6.1 Contributions and Final Word
The outcomes of this thesis are the following:
• The conclusions obtained from the study on web widgets, when followed asheuristics, can be
used as navigational aids for people browsing foreign language web pages ( pages that are not in
their own language). This will thus add to web page usability.
49
• We have built a web navigational model (REM) that can be used to browse through music data.
It can be used to explore a music collection based on the similarity in their features. This model
can be extended and used in hand held devices too because it occupies less of screen space.
• The influence of polarity of social interaction history can be used to build a new navigational
model for browsing through blog catalogs according to the polarity of responses (comments)
each blog post has received.
• A simple algorithm that computes the total positive and negative polarity of responses with respect
to the given article and a web screen for showing these values using a Google Columnchart API.
50
Related Publications
• Anupama Gali and Bipin Indurkhya. The interdependencies in between location expectations
of web widgets,Proceedings of the IADIS International Conference on Interfaces andHuman
Computer Interaction, Freiburg, Germany 26-30 July 2010.
• Anupama Gali and Bipin Indurkhya. The interdependencies in between location expectations of
web widgets,International Journal on Computer Information Systems and InformationManage-
ment Applications, In print, April 2011.
• Koduri, G. K., Gali, A., and Indurkhya, B. 2010. REM: a ray exploration model that caters to
the search needs of multi-attribute data.In Proceedings of the 2010 ACM Workshop on Social,
Adaptive and Personalized Multimedia interaction and Access (Firenze, Italy, October 29 - 29,
2010). SAPMIA ’10. ACM, New York, NY, 49-54.
51
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