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# Understanding and controlling the filter bubble through interactive visualization: A user study

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The "filter bubble" is a term which refers to people getting encapsulated in streams of data such as news or social network updates that are personalized to their interests. While people need protection from information overload and maybe prefer to see content they feel familiar or agree with, there is the danger that important issues that should be of concern for everyone will get filtered away and people will lack exposure to different views, living in "echo-chambers", blissfully unaware of the reality. We have proposed a design of an interactive visualization, which provides the user of a social networking site with awareness of the personalization mechanism (the semantics and the source of the content that is filtered away), and with means to control the filtering mechanism. The visualization has been implemented in a peer-to-peer social network, called MADMICA, and we present here the results of a large scale lab study with 163 crowd-sourced participants. The results demonstrate that the visualization leads to increased users' awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over their data stream.
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Understanding and Controlling the Filter Bubble through
Interactive Visualization: A User Study
Sayooran Nagulendra and Julita Vassileva
Department of Computer Science
ABSTRACT
“The filter bubble” is a term popularized by Eli Pariser which
refers to people getting encapsulated in streams of data such as
news or social network updates that are personalized to their
interests. While people need protection from information overload
and maybe prefer to see content they feel familiar with and
viewpoint that they agree with, there is the danger that important
issues that should be of concern for everyone will get filtered
away and people will live in “echo-chambers”, blissfully unaware
of reality, and exposure to different views. We have proposed a
design of an interactive visualization, which provides the user of a
social networking site with awareness of the personalization
mechanism (the semantics and the source of the content that is
filtered away), and with means to control the filtering mechanism.
The visualization has been implemented in a peer-to-peer social
network and we present here the results of a qualitative and a
quantitative evaluation. The quantitative study with 163
participants demonstrates that the visualization leads to
increased users’ awareness of the filter bubble, understandability
of the filtering mechanism and to a feeling of control over the data
stream they are seeing.
Categories and Subject Descriptors
D.2.8 [Information Storage and Retrieval]: Information Search
and Retrieval – information filtering
General Terms
Design, Experimentation, Human Factors
Keywords
Visualization, Filter Bubble, Recommender Systems, Online
Social Networks
1. INTRODUCTION
Today, social networks provide a global platform for people to
share and collaborate with their friends and families. Facebook,
networks. With the growth of mobile and web technologies, these
social networks are growing rapidly and millions of users are
sharing data with their friends and families. As of September
2013, Facebook has 1.15 billion users and 699 million daily
active users [28]. Nearly a quarter (24 %) of the content that is
shared on the internet is shared on Facebook [27] and more than
3.5 billion pieces of content shared each week [26], creating a
stream of data that can overload any user. The social data
overload problem is commonly solved by filtering out the
irrelevant data. Personalized stream filtering mechanisms aim at
reducing information overload by presenting the user with only
the content deemed to be the most relevant. Social media sites,
personalized stream filtering.
Paradoxically, the main problem with information filtering is that
they could be “too good”. The high level of optimization to the
interest of the user that typical algorithms lead to items that
remain in the data fit the user’s scope of interest that has been
inferred by the system from the user’s previous behavior, users
tend to becoming encapsulated in a “bubble” of their comfort,
seeing only content related to their interests, and being spared of
anything else. This is referred as “the filter bubble” problem.
We proposed an approach to make the user aware of the filtering
mechanism and take control over it. It is based on an interactive
visualization that shows the filter bubble and some features of the
hidden filtered data (its semantics and origin). The intention is to
make the user aware of the user model that the recommender
system has developed, so that they can consciously decide to
explore items that are filtered away by changing interactively her.
But showing what is hidden and filtered away from the stream can
increase the social data overload problem again. Therefore, the
main challenge is to find an effective visualization technique that
can be seamlessly integrated into the activity stream without
to that the visualization design has to take into account of the
right amount of detail to expose in the hidden filtered social data
display the hidden social data stream in an understandable way to
the user.
In this paper we present a qualitative and a quantitative evaluation
of an interactive visualization which metaphorically visualizes the
filter bubble and provides awareness, understanding and control
of personalized filtering to alleviate the filter bubble problem.
2. RELATED WORK
Recommender Systems (RSs) are software tools and techniques
which adapt to the needs of an individual user and provide
personalized suggestions of most relevant information [1]. The
personalized suggestions help users to make decisions on various
types of items, such as what news items are interesting, what book
to read, what movie to watch and so on. Information filtering
systems can be considered as a type of recommender systems,
which select from a stream of data (e.g. news, certain events,
social updates, etc.) those that fit the scope of interest of the user.
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Conference’10, Month 1–2, 2010, City, State, Country.
Copyright 2010 ACM 1-58113-000-0/00/0010 …$15.00. The difference between filtering and recommendation is that in filtering the irrelevant data is simply not displayed, i.e. remains hidden from the users, while in recommendation the relevant data is highlighted in some way (e.g. shown first in a list of search results, highlighted in a stream of data, etc.), but the irrelevant data is still available for the user to see. Recommendation techniques have been applied to personalize the streams in online social networks such as Facebook, Google+ and Twitter [2, 3]. Facebook’s edge rank algorithm is one such filtering technique which presents a personalized stream of news and friends’ status updates to the user by ranking every interaction on the site [4]. While all these social networks are centralized, Tandukar & Vassileva [5] have developed an interest-based filtering model for a decentralized online social network (OSN), which enables each peer to learn the user’s interests and to filter away messages received from the user’s friends,. Many researchers have worked on developing new RSs and improving the accuracy of their filtering algorithms. However the ultimate measure of success in this area is the user acceptance and trust of the recommendations [6]. The way recommendations are presented is critical for the user acceptance of recommender systems. Visualization techniques can be deployed to provide an intuitive “at a glance” explanation for recommendations and can also motivate the user to accept the recommendation. Presenting the recommendations in a ranked list according to their recommendation score is the most simple and commonly used visualization technique. Features like colour and font-size can be used to emphasize recommended items in a stream or list or items [7]. iBlogViz is a system to visualize blog archives. It uses many visual cues to represent the blog content and social interaction history with the blog entry which help to navigate the blog archive quickly and easily. Particularly, visual cues about the social response (comments) to the news can be used to help users navigate stream data quickly to find interesting news [8]. Webster & Vassileva [9] proposed an interactive visualization of a collaborative filtering that allows the user viewer to see the other users in her “neighborhood”, who are similar to her, and also to change manually to degree of influence that any of the other users can have on the recommendations of the viewer. Rings is a visualization of social data stream developed by Shi [10]. It is organized around the people who post in the user’s Facebook stream and empathizes users who have posted many and influential posts recently, without filtering any posts. It helps the users of OSN to browse social data efficiently and find out the active users and the time pattern of their social updates. As the activity stream in Online Social Network is personalized according to the user’s interests, the user will ultimately only see activities related to her interests and will thus have no opportunity of discovering items not related to her current interests, or developing new interests. “The filter bubble” is a term introduced by Eli Pariser [11] to denote a limited scope of information defined by the user’s interests and isolated from anything that doesn’t belong to this scope. Isolating the user in a filter bubble has its advantages and disadvantages. The main advantage is that it can help users get relevant information a lot faster while not causing social data overload. On the other hand, there are number of problems outlined by Eli Pariser [11]. The first one is the problem of distortion of the content posted on the site or by the user’s friends and the user does not know in what way the way is biased. Users become less likely to be recommended information that is important, but not “likeable”. The second problem is the information equivalent of obesity. Because of the users’ tendency to give positive feedback, they will give feedback only to information items they are most compulsively attracted to. Using an analogy from food, users will be eating candy all the time, and the filter bubble leave users locked in a world consisting of information junk food. As a result the users are increasingly surrounded by the ideas with which they are already familiar and agree, while being protected from surprising information, or information that challenges their views, the filter bubble threats people’s open-mindedness and prevents learning. Psychologist Lowenstein mentions that the “curiosity is aroused when we are presented with an ‘information gap’” and Pariser suggests that the existence of curiosity, is based on the awareness that something exists that is hidden or unknown [11]. The third problem is the matter of control i.e. the growth of user knowledge will be greatly influenced by the algorithms and systems giving excessive power to the computer scientists who develop the personalization techniques. The importance of these three problems increases rapidly, as an increasing proportion of users are using OSN to get all their information and news; and nearly all OSN deploy information filtering to personalize their streams to users. Yet, most of the personalization systems do not create awareness about what is being hidden from the user. Resnick et al. [12] outline some strategies for promoting diverse exposure. They discuss two approaches: the first one is to build diversity aware recommender systems and filtering mechanisms. As an example of this approach, Tandukar and Vassileva [13] developed an interest-based stream filtering technique, which allows for diversity exposure by allowing popular items to pass through the filter to ensure some serendipity. The second approach is to provide tools and techniques that encourage users to consider themselves searching for diverse exposure. Munson has implemented a browser extension which displays the bias in a user’s online news reading over time, which encourages users to seek the diverse exposure of news [14]. Though algorithmic personalization approaches can certainly find the most relevant content related to what users are already interested in a more efficient manner human curators and especially the user herself is probably the most appropriate agent to take the responsibility for ensuring a diverse exposure, to address the third problem outlined by Pariser. This means enabling the users to select what they want to see as well as what they do not want to see over the personalization presented by the algorithms. To enable them to do this, it is necessary first to make them aware of their filter bubble, as well as understanding of how they got inside it, and how they can control it to let different kind of information in and out, enlarge it or make it smaller… To our best knowledge there is currently no existing work that aims to create this kind of awareness and control in users. This is the aim of our work. 3. VISUALIZATION DESIGN The visualization of filter bubble has been designed and implemented based on the personalized stream filtering used in MADMICA [15] - an implementation of a privacy-aware decentralized (peer-to-peer) OSN using the Friendica open source framework [16]. MADMICA implements an approach to filtering social data, according to a model of the strength of the user’s interests in different semantic categories overlaid over a model of their social relationships, which was originally developed and evaluated in a simulation [13]. The intuition behind the filtering approach is that two people can be friends, but not share the same level of interest in different topics or categories and not trust each other’s judgment with regard to these categories. In essence, the filtering approach is based on a model of the user’s interest in a finite set of categories of social data that is overlaid with a model of the strength of user interpersonal relationships (over each category). The visualization design is based on a bubble metaphor to make the effect of the personalized stream filtering in OSNs more understandable for the users (see Figure 1). The main goal of the visualization is to creating awareness, understanding, and control of personalized stream filtering in an OSN to alleviate the filter bubble problem and increase the users’ trust in the system. It divides the space of the screen in two parts - outside and inside the bubble. The items that are inside the bubble are visible for the user, those outside the bubble are those that have been filtered away and are invisible in the stream (but they are shown in the visualization). The visualization is personalized to the user viewing it (let’s say Anna), and provides two alternative points of view: one focusing on the user’s (Anna’s) friends (see Figure 2) and one focusing on the semantic categories of the social data originating from them in the OSN (see Figure 1). We assume that there is a finite, enumerable set of sematic categories in which the content can be classified. For practical reasons, these are categories of higher level of generality, e.g. “news”, “technology”, “health”, “sport”, similar to the categorization used by news websites, Google, Yahoo, etc. The category view shown in Figure 1 represents all the categories of posts shared by Charlie during last week that were shown in Anna’s newsfeed or filtered out by the system. All the category circles inside the bubble represent the categories of posts that are shown in Anna’s newsfeed; they represent the common categories of interest between Anna and her friend Charlie. But Charlie has more interests, which are outside Anna’s filter bubble and are therefore being filtered out by the filtering mechanism based on the past history of actions that Anna performed on the posts shared by Charlie in the category “health”. The “friends view” of Anna’s bubble visualization is shown in Figure 2. It represents all the friends who shared some posts in the “health” category during the last week that were shown in Anna’s newsfeed or filtered out by the system. The position of each friend circle relative to the big bubble is intended to create awareness about the filtering i.e. whose posts the user (Anna) can see in her newsfeed. Moreover, the filter bubble shape itself metaphorically creates the awareness that the user is encapsulated in a bubble and that there are friends outside of the bubble who have posted on the topic but the user has not seen these posts. As mentioned earlier, providing some understanding about the personalized stream filtering is one of the main goals of this visualization. Organizing posts by categories and friends gives some understanding about the personalized filtering: that there is a relationship between the categories of posts and the post origin (the friends who shared them), and the underlying filtering mechanism. In addition to that, it visualizes the common interests between user and her friends i.e. what is shown inside the big bubble are common interests between the user and her friends. Providing control of the personalized stream filtering to the users i.e. users can manually override the filtering system is another main goal of this visualization. This is achieved by allowing users to drag and drop the circles in and out of the big bubble. For example, if Anna drags and drops the circle representing the “games” category (see in Figure 1) from inside the big bubble to its outside the user effectively tells the system that she does not like to see that category of posts in her newsfeed in the future. Similarly, the user could also drag and drop a friend from within her “friends-view” bubble to the outside and it signals the system to filter out the posts shared by that friend in the future. In the reverse situation, when the user realizes that she is interested in posts in category “health” shared by a friend (say, Glen), who is outside her “friends-view” bubble in Figure 2 and wants to see his posts in her newsfeed homepage in the future, she will drag and drop that particular friend inside the big bubble. Apparently, this Figure 1. Anna’s “category view” of her filter bubble related to Charlie’s posts Figure 2. Anna’s “friends view” of her filter bubble related to a certain category (“health”) of posts action is equivalent of the Anna coming out of her filter bubble and explore new interests. If Anna wants to see all posts by Glen in any category, she will select the “Friends” view and the generic category “All” from the “Categories” menu and drag him in her bubble. Nagulendra and Vassileva presented justification of the visualization design decisions and a pilot user study to evaluate the usability and user acceptance of the visualization and whether it achieves its goals of providing awareness, control and trust in the filtering mechanism in MADMICA in [17]. Eleven (11) graduate students from the MADMUC research lab used the MADMICA system with the filter bubble visualization instead of Facebook and shared interesting and research-related links over a period of three weeks in March 2013. The results of the study showed that the filter bubble visualization makes the users aware of the filtering mechanism, engages them in actions to correct and change it, and as a result, increases the users’ trust in the system [17]. Next we present the results of two more studies: a qualitative study with 5 participants and a larger scale quantitative study with 163 Mechanical Turk participants. 4. EVALUATION 4.1 Qualitative User Study A qualitative study was carried out to understand in-depth the user perception of the filter bubble visualization i.e. what do users think about the visualization. Five (5) participants from different departments in the university took part in this study. 4.1.1 Experimental Setup The study was carried out in a lab environment where users were given computers to use the MADMICA system and the visualization. The subjects were 5 university students from different fields of study such as public education, public health and statistics. They were recruited through a mailing list of potential subjects for HCI studies. First, the users were given some introduction to MADMICA and then about the filter bubble problem. After the introduction, users were given instructions to get familiar with the MADMICA newsfeed homepage and the filter bubble visualization for 10 minutes. Once they have explored the system, an interview was conducted. The interview consists of a set of tasks related to with 15 different views that are generated using the filter bubble visualization. They were asked to interact with the systems and think aloud, the users’ actions were observed and recorded and the users’ voice responses were recorded. The views in the questionnaire were generated to collect the perceptions about the visualization’s main goals: providing awareness, understanding and control. Moreover, the views included both the category view and friends view. 4.1.2 Methods The recorded users’ voice responses were imported into NVivo software [18] which is a platform for qualitative research analysis. Then the voice responses were transcribed into text. With the help of the NVivo software, thematic analysis was carried out to identify the desirable and undesirable perceptions of the visualization. Thematic analysis categorizes qualitative data into themes. It encodes the qualitative information into codes that act as labels for sections of data [19]. The users’ responses were coded and the codes were grouped into three: position of circle, size of circle and drag action. While coding, the number of references for each code was also recorded i.e. the frequency of that code in the transcript of users’ responses. Then based on the three criteria, the number of desirable references and undesirable references as calculated. The three criteria were: 1. Circles inside the big bubble represent content or friends that was shown in the user’s newsfeed. 2. Circles outside the big bubble represent content of friends that were filtered out by the system 3. The visualization only shows the newsfeed shared by friends organized into categories and friends. 4.1.3 Results The thematic analysis results are summarized in Table 1. The desirability percentage for a perception category is calculated as the number of references that are desirable in that perception category divided by the total number of references for the position of circle visual representation multiplied by 100. Regarding the position of circle visual representation, 108 total references were made i.e. users mentioned 108 times in all of their responses together that the position of circles relative to the wall of the big bubble represents the user’s interest. This is the most referred perception category (16.67%) about the position of circle that is desirable. Some excerpts from the transcript for the user’s interest perception category follow: “categories outside the bubble represent the posts that the user doesn’t want to see”, “categories inside the bubble represent my interests”, “categories inside the bubble represent users main interests for the selected duration”, “All the categories outside the bubble represent that none of user’s friends posts are related”, “categories outside the bubble represent the areas outside of my interest for that period”, and “categories inside the bubble represent that the user wants to focus on them”. The least referred (1.85%) desirable perception category regarding the position of circle is relationship. Some excerpts from the transcript for the least referred desirable perception follows: “friend circle outside the bubble for a category doesn’t mean that the user unfriended with that friend”, “having some categories inside the bubble for last month for a friend might mean an acquaintance relationship”, and “friend relationship is maintained regardless of user’s friends are outside the bubble”. Some excerpts from the transcript for the least referred desirable perception follows: friend circle outside the bubble for a category doesn’t mean that the user unfriended with that friend”, “having some categories inside the bubble for last month for a friend might mean an acquaintance relationship”, and “friend relationship is maintained regardless of user’s friends are outside the bubble”. Some excerpts from the transcript for the least referred desirable perception follows: “friend circle outside the bubble for a category doesn’t mean that the user unfriended with that friend”, “having some categories inside the bubble for last month for a friend might mean an acquaintance relationship”, and “friend relationship is maintained regardless of user’s friends are outside the bubble”. Table 1. Thematic analysis results Feature/Visual Representation Perception Category Sources (number of users) References (desirable: undesirable) Desirability percentage (%) Undesirability percentage (%) Position of circle (friend/category) Common interest 4 13 (10:3) 9.26 (10/108) 2.78 Friends’ interest 4 40 (16:24) 14.81 22.22 Friends’ sharing 5 25 (18:7) 16.67 6.48 Interaction with newsfeed 1 3 (3: 0) 2.8 0 User’s interest 5 23 (19:4) 17.59 3.7 Relationship 3 4(2:2) 1.85 1.85 Size of circle Number of posts 5 7 (6:1) 37.5(6/16) 6.25 Frequency of sharing 2 2 (2:0) 12.5 0 Friends’ interest 2 5 (4:1) 3.7 6.25 Common interest 1 2 (0:2) 0 12.5 Drag action Common interest 4 5 (4:1) 57.14 (4/7) 14.29 Relationship 1 2 (0:2) 0 28.57 The most referred (22.22%) undesirable perception category is the friend’s interest. But here only 4 users have referred this whereas in the most desirable perception all the 5 users referred it at some point in the transcript. Following are some excerpts from the transcript regarding the most undesirable perception: “categories inside the bubble represent friend’s interest and outside represents not interested” and “friend circle more in the middle more interest in the category selected”. Like the least referred desirable perception, the least referred undesirable perception is relationship and the excerpt follows: “friend circle outside the bubble represents unfriending”. The number of posts related perception category is the most referred (37.5%) desirable perception for the size of the circle. Users perceive it as follows: “bigger circle for friend/category represents more number of posts and small for less number of posts”. As for the least referred (3.7%) desirable perception for the size of the circle, users perceive it as friend’s interest i.e. “larger circle for category means the selected friend has more interest on that category”. In case of undesirable perception category, the most referred (12.5%) one is common interest (“small friend circle means more common interest between the user and friends” and “larger circle represents user has less interest on that friend”) and the least referred (6.25 %) one is number of posts and friends’ interest (“small circle means actually posted and big circle means less posted” and “bigger circle outside the bubble represents less interest of friend on that category”). There are two perception categories that emerged by the thematic analysis for drag action: common interest and relationship. Common interest is the most referred desirable perception category (57.14%) and there are no references for relationship on desirable perception. The excerpt from the transcript for common interest perceptions follows: “dragging a category inside means to share more on that category with the friend”, “dragging in may represent my future interest”, “drag out because I don’t want to have common interest”, “drag out means lost interest in that category from that friend”, and “drag all the friends outside the bubble means I want to ignore all the news from them”. The most and least referred undesirable perception category for the drag action are, relationship (28.57%) and common interest (14.29%) respectively. Excerpt related to relationship is “dragging outside a friend/category means unfriend” and for the common interest is “drag inside represents forcing the friend to take interest on that category” 4.1.4 Discussion The results of the qualitative data suggests that the subjects had both perceptions which are desirable and undesirable. Desirable perceptions (62.96%) regarding the position of circle had more references than undesirable perceptions (37.04%). This shows that most of the time the visualization users were aware of and had a good understanding about the filtering. In particular, the emergent codes such as common interests, friends’ interest, friends’ sharing, interaction with newsfeed, user’s interest and relationship from the thematic analysis clearly show that the users have some understanding about the filtering. On the other hand the users also had undesirable perceptions. This could be due to poor graphical language of the visualization and interface as a whole. For example, the reason that friends’ interest was perceived as the most undesirable perception category, could be the poor label texts of the dropdown menus which are used to view the filter bubble in different dimensions. During the experiment, the labels for the first two dropdown menu were as follows, Friend(s) and Category(s). This creates a false perception when “Charlie” was selected and the category was selected as “All” i.e. Charlie’s interests were shown inside the bubble and what lies outside the bubble were not the interests of Charlie. As a result the labels were later changed into “From Friend(s)” and “On Category(s)” (shown in Figure 1 depicting the updated version) before the quantitative study. The size of the circle is another indicator for creating awareness about the filtering, i.e. having bigger size of the circle outside the filter bubble would let the users know that there are more of posts that have been filtered out by the system on that category from that friend. Having 75% of desirable perceptions for size of the circle shows that it is intuitive enough to create an awareness about the filtering. The 25% of undesirable perceptions regarding the size of the circle shows that the graphical language needs improvement. For example, it would be clearer if there is a number shown with the varying size. Moreover, the false perceptions of common interest for the size of the circle showed that users may have wrong perceptions about the meaning of the size of circles. For example, size of the circles represent the interests of the friends i.e. smaller circle means that the friend has less interest on that category. The drag action has 57.14% of desirable perception and 42.86% of undesirable percentages. Despite the small difference, considering the number of users who referred the perception gives some clear indication that the majority of the participants (60%) were able to understand the control functionality of the filter bubble visualization. Though the perceptions were classified as desirable and undesirable, both of them helped to get more insight about the users perceptions about the visualization, improve the visualization and helped to prepare the questions and answers for the questionnaire of the quantitative study, presented in the next section. 4.2 Quantitative User Study A quantitative study was carried out to evaluate the understandability of the visualization and whether the users understand that the visualization provides awareness, understanding and control of filtering and the filter bubble. The study was conducted as an online survey and 163 participants from different parts of the world participated in the study. 4.2.1 Hypotheses The goal of this user study was to find out if the visualization is understandable, if it creates awareness and understanding of the personalized stream filtering mechanism and ability to control it to alleviate the filter bubble. So the evaluation aims at testing the following hypotheses. 1. Users understand that the visualization provides awareness of the filtering and the filter bubble. 2. Users understand that visualization provides understanding of the filtering and the filter bubble. 3. Users understand that visualization provides control of the filtering and the filter bubble. 4. Users understand the visualization and its functions. 4.2.2 Experimental Setup The study was carried out as an online survey. Unlike the conventional online surveys, this survey had the interactive visualization embedded into the survey so that users could explore it and get some hands-on experience with it before answering the survey. First, the participants were given some introduction about the MADMICA social network and the filter bubble problem in general. Then a sample newsfeed homepage was displayed in the survey so that users could actually browse through the newsfeed without leaving the survey page. The sample newsfeed contained around 15 newsfeed items on 5 different categories such as Health, News, Movies, Music and Sports from five different friends named Alice, Bob, Charlie, Dave and Frank. The participants were given instructions to assume that the aforementioned people are their friends in MADMICA social network and to browse through the newsfeed homepage as they would do in Facebook. In addition to this, the newsfeed did not show around 7 posts out of those five categories from different friends i.e. the system filtered out some of the posts. Then the users were presented with the interactive visualization exactly as in the MADMICA system and were instructed to explore the visualization. Then they were directed to the questionnaire to answer the questions. The link to the online survey is given in the appendix section of this paper. 4.2.3 Method The online survey was conducted using Amazon Mechanical Turk (MTurk) which is a popular crowd-sourced participant pool. We ensured the data quality by placing attention check questions (ACQs) and restricting participation to MTurk workers with certain qualifications [20]. The suggested qualification among researchers to ensure data quality was to allow participants who have the HIT Approval Rate (%) for all Requesters' HITs greater than or equal to 95 [20]. But we set even higher qualification to ensure the high data quality as follows: HIT Approval Rate (%) for all Requesters' HITs greater than or equal to 98% AND Number of HITs Approved greater than or equal to 5000. The data collection continued for 1 week and reached our target sample of 230. Then we analyzed the data and checked the ACQ for validity and as a result, 163 valid responses were collected. For each participant with a valid response, we paid a compensation of 1$, which is a good rate for
an approximately 30-45 min. long study on MTurk.
The questionnaire contained 25 questions. The questions were
grouped according to the metrics which they intend to measure.
The metrics for understandability of the visualization are adapted
based on the International Standards for Software Quality
Evaluation [21]. Table 2 summarizes the metrics chosen for
measuring the understandability of the visualization [21]. There
are 3 independent variables: awareness, understanding and control
to assess the understandability of the visualization. Each of the
independent variables was evaluated using the metrics given in
Table 2 i.e. understandability of each independent variable was
calculated. In addition to that, the overall understandability
(referred as understandability hereafter) was also calculated using
the understandability metrics. Six (6) questions (2 Yes/No and 4
Multiple Choice Questions) were used to evaluate each of the
independent variables. Altogether, there were 18 questions that
were used to evaluate the overall understandability with 6
questions for each metrics. Our original hypotheses mentioned in
section 4.2.1 were converted into the statistical form with the
corresponding null hypothesis (see Table 3).
Table 2. Understandability Metrics
Metric Name Purpose Formula
Interpretation
of measured
value
Evident Functions
What proportions of functions
users were able to identify by
exploring the visualization
X = A / B
A = Number of functions identified by
the user
B = Total number of actual functions
0<=X<= 1
The closer to
1.0 is the better.
Function understand-ability What proportions of functions X= A / B 0<=X<= 1
users were able to understand
correctly by exploring the
visualization
A= Number of functions whose purpose
is correctly described by the user
B= Number of functions available
The closer to
1.0 is the better.
Understandable input and
output
Can users understand what is
required as input data and what is
provided as output by the
visualization?
X= A / B
A= Number of input and output data
items which user successfully
understands
B= Number of input and output data
items available from the visualization
0<=X<= 1
The closer to
1.0 is the better
Table 3. Statistical Hypotheses
Test H0 (null) H1 (alternative)
1 μ
Awareness
0.5 μ
Awareness
> 0.5
2 μ
Understanding
0.5 μ
Understanding
> 0.5
3 μ
Control
0.5 μ
Control
> 0.5
4 μ
Understandability
0.5 μ
Understandability
> 0.5
As shown in Table 3, we considered the mean value of
understandability for our hypothesis testing. The mean value is
0.5 according to the scale of metrics used to measure the
understandability. We set the null hypothesis as the mean value of
understandability is less than or equal to 0.5 i.e. users do not have
a clear understanding about the visualization. Our research
hypothesis is the mean value is greater than 0.5 i.e. users do have
clear understanding about the visualization. As mentioned in the
metrics Table 2, the closer this mean value to 1.0 is, the better the
understanding.
4.2.4 Results
4.2.4.1 Reliability Test
The internal consistency (reliability) of question items was
measured using the Cronbach’s alpha. Higher value of a reliability
coefficient (Cronbach’s alpha) is associated with lower random
error and greater measurement of the true score of the
understandability. The acceptable value of Cronbach’s Alpha
should be the range of 0.70 to 0.95 [22]. The rules of thumb when
considering Cronbach’s Alpha value explanation are as follows:
greater than 0.9 means excellent, greater than 0.8 means good,
greater than 0.7 means acceptable, greater than 0.6 means
questionable, greater than 0.5 means poor, and less than 0.5 is
unacceptable [23]. The measured value for the Cronbach’s alpha
is 0.7 for our questionnaire. This value is in the acceptable range.
4.2.4.2 Normality Test
The assessment of the normality of the data is a prerequisite and
essential to t-tests. The Normal Q-Q plot for understandability
was generated using SPSS (see Figure 3). If the data are normally
distributed, the data points will be close to the diagonal line. If the
data points move away from the line in a non-linear way then the
data are not normally distributed [24]. As we can see from the
Normality Q-Q Plot shown in Figure 3, the data is normally
distributed because the data points stay close to the diagonal line.
4.2.4.3 Hypothesis Test
One-sample t-test was used to determine whether the mean of a
particular data set is different from the particular value. Before
doing the t-tests, the following 4 assumptions were met:
understandability is measured at the ratio level,
the collected data
are independent which means that there is no relationship between
the observations, there are no significant outliers in the data, and
the understandability is approximately normally distributed [25].
Then the t-tests were conducted for the 4 hypothesis tests and the
results are summarized in the Table 4.
The first t-test was conducted for the hypothesis 1 defined in
section 4.2.1. The Mean understandability of awareness (M =
0.7117, SD = 0.2379) was higher than the tested understandability
value of 0.5, a statistically significant mean difference of 0.21,
95% CI [0.18 to 0.25], t (162) = 11.358, p < .001. Similarly, the t-
tests for hypothesis 2, 3, 4 were conducted and the results follow
respectively, the Mean understandability of understanding the
filtering (M = 0.6176, SD = 0.2159) was higher than the tested
understandability value of 0.5, a statistically significant mean
difference of 0.12, 95% CI [0.08 to 0.15], t (162) = 6.953, p <
.001, the Mean understandability of control (M = 0.7607, SD =
0.2246) was higher than the tested understandability value of 0.5,
a statistically significant mean difference of 0.26, 95% CI [0.23 to
0.30], t (162) = 14.824, p < .001 and the Mean understandability
of visualization (M = 0.6967, SD = 0.1808) was higher than the
tested understandability value of 0.5, a statistically significant
mean difference of 0.20, 95% CI [0.17 to 0.23], t (162) = 13.884,
p < .001. In all four tests, there were a statistically significant
difference between means (p < .001) and, therefore, we can reject
the null hypotheses defined in Table 3, and accept the alternative
hypotheses.
Figure 3. Normality Q-Q Plot of Understandability
Table 4. Hypothesis Analysis
Test Variable Mean 2-
tailed t
Degree of
freedom
(df)
1-tailed
Critical t
1-tailed
t <
2-tailed
t
Means
are in
correct
order
Alternative
Hypothesis
Accepted
1 Awareness .7117
11.358
162
1.6543
YES YES YES
2 Understanding .6176
6.953
162
1.6543
YES YES YES
3 Control .7607
14.824
162
1.6543
YES YES YES
4 Understandability .6967
13.884
162
1.6543
YES YES YES
4.2.4.4 Additional Test on Graphical Language
The key graphical language constructs of this visualization are,
1. The relative position of user's circles to the bubble (inside /
outside)
3. Dragging user circles in and out (showing / filtering away)
In addition to the above 3 constructs, we identified another
potential construct from the qualitative study as follows: the
position of circles inside the bubble (closer to the center or to the
periphery). All the 3 other constructs were as part of each function
of the visualization (providing awareness, providing
understanding, and providing control) and were tested for
statistical significance. In order to test whether users interpret this
fourth construct or not, we included the answers based on this
construct for two of the questions in the survey. During the
analysis, we created a score for users based on how many out of
the 2 questions they did not select this construct as an answer.
Then the hypotheses were formed as follows: H0: μ
Score
0.5,
H1: μ
Score
> 0.5. One sample t-test was conducted and the results
are as follows: the Mean score for not selecting the graphical
construct (M = 0.9571, SD = 0.1405) was much higher than the
test score value of 0.5, a statistically significant mean difference
of 0.46, 95% CI [0.44 to 0.49], t (162) = 41.523, p < .001.There
were a statistically significant difference between means (p <
.001) and, therefore, we can reject the null hypothesis, and accept
the alternative hypothesis.
4.2.5 Discussion
The results of the quantitative study suggest that overall the new
comparing the means of variables Awareness, Understanding, and
Control, we can see that users have a better understanding
(0.7607) about the control of filtering and the filter bubble
provided by the visualization. This can be linked with the drag
and drop feature of the visualization, which is very popular and
commonly used action in many user interfaces and it is a very user
friendly user interface construct. On the other side, the users’
understanding about the visualization providing understanding to
the filtering and the filter bubble has the lower value (0.6176).
Though it is higher than 0.5, it clearly shows that the visualization
has to be improved on this aspect. A possible improvement could
be to provide some context sensitive help to the visual cues in the
visualization. The overall understandability value of the
visualization (0.6967) shows that the users had a better
understanding about the visualization after exploring it for the
first time and it could be considered as an intuitive visualization.
But it can be envisioned that the users will better understand if
there is a context sensitive help provided with the visualization.
Analyzing the t-test values gives us more insight into the
understandability measures. As mentioned earlier, the
understandability of visualization is calculated using the three
variables awareness, understanding and control. These three
variables are understandability variables and are measured using
the metrics presented in Table 2. The variables awareness,
understanding and control obtained a high 2-tailed value
respectively 11.358, 6.953, and 14.824. These values are
comparatively very high when compared with their relevant one-
tailed t-test value, which is 1.65. This indicates that these three
variables are a very good measure for the understandability of this
visualization.
The additional test on graphical language results suggest that the
users very rarely interpreted the position of circles inside the
bubble (closer to the center or to the periphery) i.e. very few users
selected it. A possible reason for this might be the nature of the
question; the users might have only focused on the first 3
graphical constructs which are intuitive and obvious. But it seems
a useful construct and could be added as an improvement to the
visualization in future.
5. CONCLUSION AND FUTURE WORKS
This paper presented the results of a qualitative and a quantitative
evaluation of an interactive visualization which metaphorically
visualizes the filter bubble in a P2P Social Network. The
qualitative study reveals several user perceptions which provide
desirable explanation for the awareness, understanding and
control of the filter bubble provided by the interactive
visualization. The quantitative study with 163 participants
demonstrates that the visualization leads to increased users’
awareness of the filter bubble, understandability of the filtering
mechanism and to a feeling of control over the data stream they
are seeing. Future work directions include conducting a study of
evaluating the intuitiveness of the visualization by comparing it to
the same interactive visualization provided with guided help.
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APPENDIX
The online survey link used for the quantitative user study:
... Ways of providing readers with feedback on their past media diet have already been explored by several studies and browser plug-ins (e.g. Blue Feed Red Feed; 6 Munson et al., 2013;Nagulendra and Vassileva, 2014). Research from other domains, such as movie recommendations, can also serve as an inspiration here (e.g. ...
... Generally, these tools provide a comprehensive overview of users' overall media diets compared with those of other users (e.g. Nagulendra and Vassileva, 2014) or with an ideal standard, such as ideological balance (e.g. Munson et al., 2013). ...
... While this approach might appear comparably drastic, there are also more subtle ideas. For example, visual exploration tools have been proposed to explore dissonant viewpoints (Graells-Garrido et al., 2016), new topics (Sullivan et al., 2019) or content beyond one's own filter bubble (Nagulendra and Vassileva, 2014). Relatedly, Bountouridis et al. (2018) developed a tool that lets readers explore content that particular articles omitted. ...
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... However, from the user's point of view, exploration-oriented recommender systems seem to be essential to fulfill users' needs for variety and novelty seeking (Liang, 2019;McAlister & Pessemier, 1982). Additionally, these recommenders can help to broaden users' taste horizons, encourage users to explore their blind spots (Tintarev et al., 2018), and mitigate potential filter bubble issues (Nagulendra & Vassileva, 2014). ...
... Users may not always be aware of the exploration possibilities, and from the recommender perspective, system-initiated exploration is a difficult task but could be supported by using psychological mechanisms such as visualizations and nudging to create a psychology-informed recommender system (Lex et al., 2021). To encourage users to explore away from their current preferences, previous work has utilized visualizations to help users identify their blind spots (Tintarev et al., 2018) or filter bubbles (Nagulendra & Vassileva, 2014) by comparing their tastes with the global tastes of other users or the tastes of their friends. By exposing users to their under-explored tastes, users were nudged to explore their blind spots (Tintarev et al., 2018). ...
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... Gobo, a web platform, tries to increase transparency by explaining how algorithms work and by enabling users to regain control over their news feeds, by managing and filtering contents [1]. Another example is a filter bubble visualization design from Nagulendra & Vassileva [9]. This design allows users to see which of their friends' feeds they are currently exposed to, which visually explains how algorithms manage friends' influence on social media. ...
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... One approach is preventive -trying to avoid filter bubbles by running algorithms in the background to ensure balance and diversity (e.g., [17]). The other approach is permissive -allowing filter bubbles but giving the user control over them, allowing the user to play the corrective factor (e.g., [4,5,16,25,26]), and enabling a kind of value co-creation between the user and the recommender system. We want to focus on the latter -the manageability of filter bubbles. ...
... Nagulendra and Vassileva [25,26] introduced an interactive visualization approach to illustrate and increase the awareness of the filter bubble in Online Social Networks. They designed their visualization based on a bubble metaphor to improve the comprehensibility of the filtering process in a way that is intuitive for the user. ...
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