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

Anupama Gali 200502002 [email protected]

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 Gali and K 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 be completed 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 would be, Amitash and Saraschandra, two PhD candidates helped in every way to learn the topics in 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 about the 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 and professionally. 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 completely going 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

v vi knowledge I have gained, I am sure I will be able to continue my research in this 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 suggestions 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 usability 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 locations. Comparatively, less work has been done on interaction in between web 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 study whose results can be used to determine the blind spots of each widget in a webpage. Based on the results obtained from this experiment, conclusions are made to establish the interdependencies. It is noticed that out of the 4 widgets (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 form a 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

vii viii 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 functionality and aesthetic appearance of the interface. Suggestions on navigating back 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 social interaction history on users reading blogs. Previous studies report that the number of comments on a blog article play a role in attracting readers- users’ interest in an article increases with the number of 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 Relatedwork ...... 6 2.3 Hypothesisproposed ...... 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 Behavioralstudy ...... 11 2.4.3.2 Eye tracking study ...... 11 2.4.4 Tasks ...... 11 2.4.5 Variables ...... 11 2.5 Procedurefollowed ...... 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 Interestregionsforads ...... 18 2.6.2.4 Interest regions for login box/link ...... 19 2.7 Conclusions...... 19

ix x CONTENTS

3 REM-Ray Exploration Model that Caters to Exploratory Search ...... 21 3.1 Introduction...... 21 3.2 Faceted Navigation ...... 22 3.2.1 Usability Issues ...... 22 3.3 Related work: A Brief Survey of Catalog Exploration and Visualization Models . . . . 22 3.3.1 mSpace model:slicing through n-dimensional space ...... 22 3.3.2 Phlat:tagging UI ...... 23 3.3.3 Mambo:Visual zooming approach ...... 24 3.3.4 Audiomap:Graphs and nodes ...... 25 3.3.5 Elastic Lists:faceted navigation ...... 26 3.3.6 Videosphere:spherical model ...... 27 3.4 REM-RayExplorationModel...... 28 3.4.1 Design ...... 28 3.4.2 ExploratorySearch...... 30 3.5 Conclusions...... 31

4 Implementation and Evaluation of REM ...... 33 4.1 Introduction...... 33 4.2 Current Evaluation Practices ...... 33 4.3 Our Evaluation Methodology ...... 34 4.4 Participants ...... 34 4.5 Apparatus...... 34 4.6 DetailsOfEachScreen ...... 35 4.7 Procedure ...... 35 4.8 Results...... 36 4.8.1 Errorrate ...... 36 4.8.2 Satisfaction with the tool ...... 36 4.9 Conclusion ...... 38 4.10 Limitations ...... 39

5 Effect of Polarity of the Traces of Interaction History in Reading Blog Posts ...... 40 5.1 Introduction...... 40 5.2 Relatedwork ...... 41 5.3 Hypothesis ...... 43 5.4 Online survey- Feasibility test ...... 43 5.5 Method of the experiment ...... 43 5.5.1 Participants ...... 43 5.5.2 Apparatus...... 43 5.5.3 Design ...... 44 5.5.4 Tasks ...... 44 5.5.5 Variables ...... 45 5.6 Procedure ...... 45 5.7 Results and Discussion ...... 45 5.7.1 Effect of polarity ...... 45 5.7.2 Effect of positive and negative polarity on user’s order of reading ...... 46 5.7.3 Effect of positive and negative posts on user’s likings ...... 47 CONTENTS xi

5.8 ConclusionsandFutureWork...... 48

6 Conclusions and Future Work ...... 49 6.1 Contributions and Final Word ...... 49

Bibliography ...... 52 List of Figures

Figure Page

2.1 Version1 of page used for ‘Search’ Widget ...... 9 2.2 Version2 of page used for ‘Search’ Widget ...... 9 2.3 Version3 of page used for ‘Search’ Widget ...... 10 2.4 Results of behavioral study-‘Search’ widget ...... 13 2.5 Results of behavioral study-‘Navigation pane’ widget ...... 14 2.6 Results of behavioral study-‘Ads’ widget ...... 15 2.7 Results of behavioral study-‘Login box/link’ widget ...... 16 2.8 Example of interest regions in a heatmap generated for one version of a web page used forsearchwidget ...... 17

3.1 Screenshot of mspace model used for browsing through data of research papers . . . . 23 3.2 Phlatscreenshot...... 24 3.3 Screenshot of mambo browser for browsing through songs ...... 25 3.4 Audiomap screenshot displaying search results for keyword ‘boney’ ...... 26 3.5 Screenshot of elasticlists for browsing through data of noble prize winners...... 27 3.6 Videospherescreenshot...... 27 3.7 A prototype design for REM with terminology used in it ...... 28 3.8 REM-differentscreens ...... 29

4.1 REM-Screenshots...... 35

5.1 Percentage of users reading posts based on polarity distribution of comments . . . . . 44

xii List of Tables

Table Page

2.1 Result summary of behavioral study ...... 16 2.2 Result summary of blind spots noticed for ‘search’ widget across versions ...... 18 2.3 Result summary of blind spots noticed for ‘navigation’ widget across versions . . . . . 18 2.4 Result summary of blind spots noticed for ‘ads’ widget across versions...... 19 2.5 Result summary of blind spots noticed for ‘login’ widget across versions...... 19 2.6 Result summary of eye tracking study ...... 19

4.1 Summary of the results of the task-speciﬁc questionnaire ...... 37 4.2 Summary of the results of the screen-speciﬁc questionnaire ...... 37 4.3 Summary of the results of the user-satisfaction questionnaire(scale of 5)...... 38

5.1 One way ANOVA results for effect of polarity ...... 46 5.2 Paired T-Value tests of user given order for posts with positive and negative polarities . 47 5.3 Paired sample statistics ...... 47 5.4 Paired T-Value tests of user given liking for posts with positive and negative comments 47

xiii 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 effectiveness, efficiency and satisfaction in a specified context of use”. The three core terms are defined as follows: effectiveness is specified as the ‘accuracy and completeness with which users achieve specified 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, effectiveness 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, how quickly 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 by accuracy, 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 effectiveness, 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 so as 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 they are in control with respect to the hypertext, thanks to the comprehension of available contents and 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 using it and achieve their goals more efficiently. This will help them cultivate confidence and trust in the website. 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 the correct web page that satisfies his/her requisites in terms of the content, organization (non-linear structure of 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 with current 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 overload 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 where they 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 navigational 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. Location of 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 of polarity 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 work and 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 been done 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 on the location of another widget. To test this hypothesis, a behavioral study and an eye tracking study are conducted on eight different websites. The results collected from the study have been analyzed and it is found 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 widget is 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, allowing users to explore by filtering available information. Each facet corresponds to the possible values of a property 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 to the 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 powerful tool 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 visualization 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 such models 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 is not 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 if type 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. Conclusions includes the observations and interesting results that we have found in our studies. It also has a future work section which shortly describes how this research can 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 to appear. 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 the familiar 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 the page 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 set of 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 on it 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 searching for navigation links even when the link have been placed in unconventional locations [37]. Another eye-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 the common web widgets. Constructing a web site that reflects these expectations will help in increasing the 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 the visual 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 survey of commercial websites, it has been found that consistency of location conventions among websites is not maintained [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 interdependent. If the location expectations of two or more widgets are dependent on 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 on the 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 easier to 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 participants 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 same widget 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 of visible 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 2.1 Version1 of page used for ‘Search’ Widget

Figure 2.2 Version2 of page used for ‘Search’ Widget

9 Figure 2.3 Version3 of page used for ‘Search’ Widget

Version1 has navigation links on the left, ads on the right and login box on the bottom 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 required any 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 effect users’ 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 experiment was to get additional information about where the participants are looking as they search for the missing 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 widget which 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 experiment and 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 web page 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 bottom of 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 terminology used 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 fixation on each page for each widget. It has to be cautioned that the presence of high density of fixation in a 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 complexities in 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 were shown 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 over the area of the web page where they expected the widget to be and click on that region using the mouse. All the clicks made by each participant were recorded in separate videos. There was no time limit on any task and the participants were asked to verbally state the reason why they expected the widget to be in that region. Because there is no right or wrong region, all the expected regions by all the participants were 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 as they 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 regions/widgets in the page associated with the missing widget were tabulated and the value of the corresponding 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 login box) 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 participants were 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 the people corresponding to each location expectation are mutually exclusive. For each widget, expected regions in all the 6 versions were obtained. The data was analyzed separately for each version to see if the location of missing widget was expected to be 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 2.4 Results 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 participants expected the search to appear at a ﬁxed place-top right of the main content of the page for all the versions used. For 20 participants, 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 of any other widgets on the web page. It is also interesting to note that the search bar is least expected to be seen in the 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 a reader 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) expected it to appear on the left of the page. This suggests that people generally prefer the book like viewing of internet. Closely following (28.33% of the responses) was the top of the page irrespective of other widgets. 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 2.5 Results of behavioral study-‘Navigation pane’ widget

14 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 expected to 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 navigation pane of the page (3.33%).It is also interesting to note that most of the participants expected to ﬁnd the ads on the 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 2.6 Results 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 in provides 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 2.7 Results of behavioral study-‘Login box/link’ widget

These results are summarized as follows:

Widget Expected location Search top of main content of the page Navigation pane Independent of other widgets-on the left side of the page Ads Independent of other widgets-on the right side of the page Login with the navigation pane

Table 2.1 Result 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 at that 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 the web 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 2.8 Example of interest regions in a heatmap generated for one version of a web page used for search 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 of a heat map contained a heat spot, the corresponding cell was given a value 1. If the interest region was 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 and bottom left,bottom center and bottom right of Login on the right the page Version2 Navigation on the right, Ads on the left and bottom center of the page Login on the left Version3 Navigation on the top, Ads on the left and Lo- bottom left and bottom center of the page gin on the top left below the logo

Table 2.2 Result 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 page1 and version1 of web page2,so on) have same blind spots. The participants did not look at the bottom-right of the page, so this can be

Version Description Blind spot (s) Version1 Search on the top right, Ads on the bottom bottom right of the page and Login on the top of the page Version2 Search on the right, Ads on the left and Login bottom right and bottom left of the page. on the top with the logo of the page Version3 Search on the top, Ads on the right and Login bottom right of the page on the right of the page

Table 2.3 Result 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 the blind 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 results from eye tracker. Following table summarizes the results for blind spots.

18 Version Description Blind spot (s) Version1 Search on the top with logo, Navigation on bottom right of the page the top and Login on the left of the page Version2 Search on the right, Navigation on the left and bottom right of the page. Login on the top Version3 Search on the left, Navigation on the right and bottom right of the page Login on the right of the page

Table 2.4 Result 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, Navigation bottom left, bottom center and bottom right of on the top and ads on the right of the page the page Version2 Search on the left, Navigation on the left and bottom left,bottom center and bottom right of ads on the bottom of the page the page. Version3 Search on the right, Navigation on the right bottom center of the page and ads on the left of the page

Table 2.5 Result 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 center Navigation pane Top left corner and top center Bottom right Ads Top center and top right corner Bottom right Login Top right and top left corners Bottom center

Table 2.6 Result summary of eye tracking study

2.7 Conclusions

Our experimental results suggest that there are some interdependencies between location expectations of some widgets. Search is expected to appear with the main content of the page 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 user will save time in searching for a widget and thus usability of the page can be improved.

20 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 researchers in 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: the user 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 the search 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. In the 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.

21 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 Solr and 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 of heirarchical 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]. These models 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 arrange the n-dimensional space in such a

22 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 can explore the information based on their interests by changing the orientation of the design. mSpace’s interface 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 specifying one 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 3.1 Screenshot 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 DS like other metadata 2. Tags and data can be easily ported together 3. Single tagging system can be used 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 or can 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 independent of 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. Tags are available only through Phlat i.e., only in the retrieval time.

Figure 3.2 Phlat 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 horizontal bar 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 among levels, 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.

24 Figure 3.3 Screenshot of mambo browser for browsing through songs

3.3.4 Audiomap:Graphs and nodes

Audiomap 1 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 artists connecting 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

25 Figure 3.4 Audiomap screenshot displaying search results for keyword ‘boney’

3.3.5 Elastic Lists:faceted navigation

Elastic lists 2 [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 nearest possible 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.

2 http : //well − formed − data.net/experiments/elasticlists/ - Last visited on 30th April 2010

26 Figure 3.5 Screenshot of elasticlists for browsing through data of noble prize winners

3.3.6 Videosphere:spherical model

Videosphere 3 uses a sphere model to connect related videos and allows user to browse similar 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 3.6 Videosphere screenshot

3http : //www.bestiario.org/research/videosphere/ - Last visited on 30th April 2010

27 3.4 REM-Ray Exploration Model

Figure 3.7 A 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 also possible 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 independent nodes which we call suns. If values (Mdv1 and Mdv2) for two metadata attributes (Md1 and Md2) co-occur in the descriptive

28 metadata of at least one song, then a ray emerges from the sun representing Mdv1, setting on the sun representing Mdv2 and vice versa. In other words, such inter-relations between values of various 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 a connected 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 3.8 REM-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.

29 (a) Results obtained for initial input keyword are shown with diameter proportional to their relevance. (b) The exploration screen with selected sun A2as 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 of A2 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 on the 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 a mandatory 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 value Mdv1 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 value Mdv1 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 traditional keyword-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 these rays 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. If not, 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-attribute rays 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 collapse to a single ray then it is named as Mdi: collapsed ray, where Mdi is the name of the corresponding metadata attribute. These

30 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. As the 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 the user 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 does not 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 actors A1 and A2 which are sung by singer S1 and composed by composer C1. Figure 2 illustrates the steps involved with screenshots. The user starts by giving a keyword that relates to any of A1, A2, S1 and C1. Let us suppose that user has searched for A2 and the system displays the results. The user proceeds to the next screen by selecting a relevant sun. In this screen, the selected sun is made the source and is surrounded with syncs connected by inter-attribute rays. The user makes the source A2 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 ray corresponding to metadata attribute actor is expanded which presents him/her a screen with intra-attribute rays setting on syncs which are subsets of values of metadata attribute actor. The user has to select that alphabetical group which contains A1. On selecting a ray corresponding to the correct alphabetical group, all the suns in that group are shown as syncs and node A1 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 and C1. The songs which match the intersection of choices in the dock are output at every step in the playlist. So, in the end, the user will have those songs which are acted by A1 and A2, sung by S1 and composed by C1.

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 superior to other traditional models based on facet browsing because REM models the user interactions when they are browsing through multi-attribute data to the nearest possible.

32 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 and limitations 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. alphaslider 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 interesting 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 methods are 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 measure was the error rate of the completed tasks, while the subjective measure was the user satisfaction with the tool. The user satisfaction was measured using a set of tool-specific questions. 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 valuable 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 helping users 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. On a 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 computed the tags and similarities in between songs based on the features like pitch, raga and tempo. This data was stored 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 4.1 REM-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 in such 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 user took 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 from one to three. Out of 13 participants, Seven participants performed all the 10 tasks correctly. One participant made 3 mistakes, three participants made 2 mistakes and two participants made 1 mistake. In total out of 130 tasks by 13 participants, 13 tasks were performed incorrectly which means error rate is 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 table below. 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 and also apt.

36 No. Questionnaire items Not at all easy Not easy Comfortable Easy Very easy 1 Searching for a data item was 0 0 0 0 13 2 Finding a matching sun from the dis- 0 1 2 6 4 played values was 3 Identifying the current sun in the screen 0 1 0 1 11 4 Finding the most relevant syncs of cur- 0 0 0 5 8 rent sun was 5 Finding all syncs related to an attribute 0 1 0 5 7 was 6 Navigating back to make a new selec- 2 6 0 3 2 tion was 7 Viewing all the related songs was 0 0 2 3 8

Table 4.1 Summary 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- 0 1 6 4 2 tion was 2 Understanding the relationship in be- 0 1 4 4 4 tween connected items was 3 Understanding the meaning of 0 3 3 2 5 handle/ray-collapse was 4 Identifying the clickable/non clickable 1 4 3 2 3 parts on the screen was 5 Understanding the playlist generation 0 1 0 5 7 was 6 Learning the system was 0 1 6 4 2

Table 4.2 Summary 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.

37 No. Questionnaire items 1 2 3 4 5 1 Satisfaction with the search screen 0 0 3 5 5 2 Satisfaction with the visualization 0 0 4 4 5 3 Satisfaction with the ﬂow of the system 0 0 4 5 4 4 Satisfaction in viewing the playlist 0 1 4 5 3 5 Satisfaction in going back to the previous selection 0 3 4 4 1 6 Overall satisfaction with the system 0 2 0 10 1

Table 4.3 Summary 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 controlled 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 the users 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 following ways: a) Composition of complex query patterns is not difﬁcult in this model because multiple 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 a step 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 multimedia and is interactive allowing the readers to comment their response on each blog post (blog article). That is, a user can not only read the static document but also actively write his/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 the form 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 the article 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 blog archives as explained 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 about the number and type of comments 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 affect reader’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 and then 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 that users 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 a background to the tag clouds. This topography is applied to the blog navigation system and it is easy for the users to find the desired tags easily even if the tag clouds are very large. It also helps in understanding 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 entries to 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 an overview 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 is not 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 visualizes them. Each comment is represented as a colored circle according to its 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) represent some 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 in handling huge number 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 contribute to the user interest in reading that post. We limit our current study to only the polarity of the comment in 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 suggest similar 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 based on 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]. The average 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 time they 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 5.1 Percentage 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 were chosen from a single author. Also, to enable randomness in style, each category had different author from 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 screen showing 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 Posts post1 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 >0.05 2 Politics 6.931 0.014 0.069 >0.05 0.197 >0.05 0.599 >0.05 1.890 0.05 3 Technology 2.922 0.05 0.205 >0.05 0.127 >0.05 0.016 0.05 5.036 0.033 4 Neuroscience 1.635 >0.05 0.818 >0.05 0.201 >0.05 0.671 >0.05 0.000 >0.05

Table 5.1 One 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 a category, only their ﬁrst choices were genuinely made. The remaining choices apparently might have 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 effect than 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 >0.05 2 politics positive and negative 0.539 14 >0.05 3 technology positive and negative 0.539 14 >0.05 4 neuro science positive and negative -1.461 14 >0.05

Table 5.2 Paired 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 type1 and 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 1.01419 2 politics positive and negative 1.8333 and 2.4333 0.91937 and 1.29376 3 technology positive and negative 2.4444 and 3.0667 0.78343 and 0.79881 4 neuro science positive and negative 2.6333 and 2.0667 0.69351 and 1.16292

Table 5.3 Paired sample statistics

S.No Post category pairs t df Sig. (2-tailed) 1 war positive and negative -2.474 14 0.027 2 politics positive and negative -2.016 14 0.05 (app.) 3 technology positive and negative -2.226 14 0.043 4 neuro science positive and negative 1.679 14 >0.05

Table 5.4 Paired 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 1 and 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 a vital 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 comments section. In speciﬁc, an immediate extension to this work could be a new experiment to test the following on a different 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 in Chapter 1 are in accordance with the related studies done by Bernard [5] and Hinesley [22]. Our experiment has done this study on 4 commonly 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 in Chapter 2 and Chapter 3 is of exploratory use to browse through music data. Suggestions given by the users are implemented as a part of the study. The current 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 different set 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 category of 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 as heuristics, 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 and Human 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 Information Manage- 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.

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