Visual Document Exploration with Adaptive Level of Detail: Design,

Implementation and Evaluation in the Health Information Domain

L. Shao

1,2

, S. Lengauer

, H. Miri

, M. A. Bedek

, B. Kubicek

, C. Kupfer

, M. Zangl

B. C. Dienstbier

, K. Jeitler

, C. Krenn

, T. Semlitsch

, C. Zipp

, D. Albert

, A. Siebenhofer

5,6

and T. Schreck

Institute of Computer Graphics and Knowledge Visualization, Graz University of Technology, Austria

Fraunhofer Austria Center for Data Driven Design, Austria

Carnegie Mellon - KMITL (CMKL University), Thailand

Institute of Psychology, University of Graz, Austria

Institute of General Practice and Evidence-based Health Services Research, Medical University of Graz, Austria

Goethe University Frankfurt, Germany

Keywords:

Information Visualization, Document Exploration, Topic Modeling, Interactive Retrieval, Close-Distant

Reading.

Abstract:

Documents typically show a linear structure in which the content can be accessed. However, linear reading is not

always desired by users, nor is it the best presentation way, as information needs may be developing or changing

over time, and users would thus want to extract the relevant information by navigation and search. Therefore,

reading with adaptive focus and level of detail is needed. This is of utmost importance in the health information

domain where patient conditions and resulting information needs may evolve in different directions over time.

We report on the development of a visual document exploration system which supports navigating a document

at different levels of aggregation, from topic overview (high-level) to keyword occurrences (mid-level) to full

text (low-level). Our design smoothly integrates the different levels of detail from which the users can choose.

The system is designed to track explored topics and use this information to suggest additional content. We

evaluated the design and its corresponding web-based implementation through a formative user-study in the

domain of diabetes health information. The evaluation conﬁrmed that our design and implementation can raise

interest and curiosity, and also allow users to efﬁciently navigate content of interest.

1 INTRODUCTION

Nowadays, Consumer Health Information Systems

(CHIS) are indispensable in modern healthcare and

fulﬁl a myriad of functions. CHIS offer consumers a

comprehensive overview of a disease, and particularly

address general knowledge of health-related topics,

their effects and courses, as well as interventions to

maintain or restore health. They also enable the early

detection, diagnosis, treatment, palliation, rehabilita-

tion and follow-up care of diseases and associated

medical decisions, care and coping as well as daily life

with the diseases (Arbeitsgruppe GPGI, 2016). Usu-

ally, CHIS are provided statically and linearly, i.e., the

same medical content is presented to everyone with

the same structure. However, patients vary regard-

ing previous knowledge, information needs and health

treatment situations, e.g., depending on gender, age,

personality, perception, etc., and thus a linear reading

may not be the best solution for extracting relevant

information for everyone (Bunge et al., 2010). There-

fore, an adaptive and interactive visual CHIS is needed

that supports document exploration with adaptive fo-

cus and level of detail views.

Our main research objective in this work is to de-

velop novel concepts for advanced, interactive, adap-

tive, and visual CHIS (called A+CHIS). We are focus-

ing our research on the case of Type 2 Diabetes Mel-

litus (T2DM) because the disease is complex, highly

relevant to public health, and its topicality of con-

tents is changing over time, as a result of new groups

of drugs, availability of expertise, ﬂexibility in treat-

ment, improved patient education, as well as sustained

follow-up practices and screening for its complica-

Shao, L., Lengauer, S., Miri, H., Bedek, M., Kubicek, B., Kupfer, C., Zangl, M., Dienstbier, B., Jeitler, K., Krenn, C., Semlitsch, T., Zipp, C., Albert, D., Siebenhofer, A. and Schreck, T.

Visual Document Exploration with Adaptive Level of Detail: Design, Implementation and Evaluation in the Health Information Domain.

DOI: 10.5220/0011621800003417

In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 3: IVAPP, pages

133-141

ISBN: 978-989-758-634-7; ISSN: 2184-4321

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

133

tions. However, managing T2DM is still a difﬁcult and

time-consuming task, as it is common, serious, and

under-treated. Therefore, it is considered a major chal-

lenge to healthcare services and requires adaption of

patients and therapies, hence the affected groups have

a constant need for information (Standl et al., 2019).

In this work, we introduce a visual document explo-

ration system, including multi-dimensional adaptivity

for health information consumers, aiming for a better

understanding of the medical content by combining

close and distant reading approaches. To this end, we

propose a multi-level text aggregation approach, which

supports document navigation from a high-level (topic

overview) to a mid-level (topic/keyword occurrence)

and a low-level (full text and keyword highlighting).

Our idea is that a ﬂexible system allows the user to

efﬁciently navigate a document, overview the content,

ﬁnd speciﬁcs of interest, and hence follow an efﬁ-

cient information perception. In our design, we set

out to make use of well-known document visualiza-

tion approaches which are tailored and integrated in

an efﬁcient system.

To visualize the high-level structure of a document,

a dynamic table-of-contents is employed that repre-

sents sub-chapters by means of a Word Cloud con-

taining keywords that are pre-generated by a topic-

modeling approach. We use a visual navigator based

on tile-bars to link high-level structures with mid-level

document information. The visual navigator shows

topic occurrences within the underlying document and

allows users to quickly explore the content by text snip-

pets. We conducted a user study to characterize the

usage behavior of health information seekers adopting

our approach. We show the usability of our system

by comparing linear reading with our multi-level ap-

proach.

The main contributions of our work include: (i) a

visual document exploration system for health infor-

mation on T2DM, (ii) a multi-level text aggregation

approach, including three levels of aggregation, and

(iii) a holistic formative evaluation of our interactive

system with a user group.

2 RELATED WORK

Our work mainly relates to the ﬁeld of document and

health visualization.

2.1 Document Visualization Techniques

One popular and widely-used visualization technique

for text data is the Word Cloud representation (also

known as Tag Cloud). This visualization technique is a

distant-reading technique (Moretti, 2005) that presents

a visual overview of text collections by using different

type sizes for frequent, or otherwise deemed impor-

tant, words (Heimerl et al., 2014). Distant-reading

techniques for textual data allow users to approach

literature in a completely new way. Instead of read-

ing texts in the traditional way, i.e., linear reading or

so-called close-reading, the focus of distant reading

approaches is to count, to graph, and to map textual

data by a visual representation (J

anicke et al., 2015).

Over the last years, much research has been con-

ducted on Word Cloud visualizations. For instance,

WordBridge by (Kim et al., 2011), utilizes graph-based

visualization techniques to connect several groups

within a Word Cloud with information-rich edges.

Moreover, other Word Cloud extensions exist that fo-

cus on semantic contour lines (Wu et al., 2011) and

images (Gu et al., 2017). In our work, we rely on tra-

ditional Word Clouds to foster distant-reading within

single documents.

For larger document collections, explorative sys-

tems such as (G

org et al., 2013) and (Isenberg et al.,

2017) can be used, which consider further document

features (e.g., metadata information or co-authorship).

Another interesting approach to visualizing large doc-

ument collections is the Document Cards concept by

(Strobelt et al., 2009), which represents the document’s

key semantics by using a mixture of images and impor-

tant keywords. In order to visualize explicit term dis-

tribution within a document, Tile Bars (Hearst, 1995;

Keim and Oelke, 2007) may also be used. Tile Bars is

a compact pixel-based visualization technique which

simultaneously reveals the relative length of a doc-

ument, the relative frequency of one or more query

terms, and their distributional properties with respect

to the document. In this work, we utilize a Tile Bars

representation to display the relative frequency and

distribution of terms from a Word Cloud.

2.2 Data Visualization in Health Care

Data visualizations are becoming increasingly impor-

tant for medical applications, e.g., information on med-

ical diagnostics, treatments, and health. Electronic

health records enable novel visualization applications

for patient data (Rind et al., 2013). In the current

survey of (Wang and Laramee, 2022), more than 40

papers in the core of visualization of electronic health

record data are identiﬁed. For example, the LifeLine

system was among the ﬁrst to visually represent pa-

tient treatment histories and support interactive ex-

ploration (Plaisant et al., 1998). For instance, (Cao

et al., 2010) used linked Word Clouds to support multi-

faceted data analysis of diseases such as diabetes. By

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

134

using linked Word Clouds, they visualized cluster con-

nections between T1Dm and T2DM. Furthermore, So-

larMap (Cao et al., 2011) is a visual analytic technique

for visually exploring topics in multi-relational data. It

combines a labeled contour-based cluster visualization

with a radially-oriented tag cloud.

2.3 CHIS

Within the scope of this work, we performed an explo-

rative search on currently available CHIS on T2DM in

different media sources (websites, digital documents,

print media, apps, videos) and focused on existing ele-

ments by which users can adapt the presentation and

their use. We found that the possibility to do adap-

tations in currently available CHIS is only used to a

limited extent. We could not identify any adaptive

elements in print media, digital documents or videos.

Adaptations were only provided on websites and in

apps, and mainly concerning the type of presentation,

such as adjusting the font size and font color. In some

CHIS, it was also possible to select different languages

or a read-aloud function for the text (Government of

Canada, 2022), (Bundesministerium f

ur Gesundheit,

2022). In addition to these more general adaptive ca-

pabilities, only a few CHIS provided adaptations of

personalized and relevant medical information. The

adaptation mechanisms pre-ﬁltered medical informa-

tion or speciﬁc chapters based on a previously gen-

erated user proﬁle representing the current diabetes

related situation (British Diabetic Association, 2022).

Most CHIS provided a usual table of contents with

or without hyperlinks to corresponding chapters. Ad-

ditionally, some CHIS used links in the text or cross-

references to other text passages or chapters. No CHIS

in our sample of T2DM used a visual document ex-

ploration system with multi-dimensional adaptivity for

health information consumers.

3 NEED FOR A+CHIS

As mentioned in the previous section, only a few CHIS

on T2DM are either interactive, adaptive and/or per-

sonalized. The wide range of information sources

(brochures, websites, medical doctors, etc.) and the di-

versity of topics (such as symptoms, treatments, nutri-

tion, etc.) might be overwhelming for medical layper-

sons. The knowledge domain of T2DM is complex

and comprehensive, and thus, characterized by high

intrinsic cognitive load (Sweller, 2005). Information

seekers tend to apply heuristics and cognitive biases at

every stage of information processing when confronted

with such complex situations. Cognitive biases, mis-

Figure 1: Our design concept allows to smoothly switch

from the Table of Contents as the highest level of abstraction,

to the subsections (top) to the content aggregation (bottom)

using word clouds and images.

conceptions and believing in myths about T2DM may

have severe health-related consequences. An inter-

active CHIS has the potential to (i) track behavioral

patterns and explicit feedback of consumers, (ii) in-

terpret these indicators in terms of certain cognitive

biases (e.g., the conﬁrmation bias), and (iii) intervene

if necessary (e.g., by suggesting other pieces or sources

of information). An adaptive CHIS can ensure that the

consumer is neither too bored nor too overwhelmed.

Providing information units for which the consumer

is just ready to read, understand and learn, reduces

the current intrinsic cognitive load to a medium level.

A personalized CHIS has the potential to increase a

consumers’ personal commitment and thus, help to

close the ‘intention-behaviour gap’ (Schwarzer, 2008),

considered as the ultimate goal of a CHIS. To ensure

that consumers appreciate to engage with our advanced

CHIS, a set of added values compared to more ‘tradi-

tional’ digital CHIS (e.g., a brochure in PDF format or

plain webpage), need to be fulﬁlled: the guarantee of

high quality and evidence-based medical information,

the reduction of complexity to a medium level, and

recommending information units that ﬁt a consumers’

information needs. Also, tools and functionalities that

help the consumer to get an overview of the knowledge

domain, to efﬁciently answer certain questions, and to

easily navigate through different sub-topics should be

Visual Document Exploration with Adaptive Level of Detail: Design, Implementation and Evaluation in the Health Information Domain

135

provided. Formative evaluation activities (Section 5)

ensure that such performance goals will be fulﬁlled.

4 ADAPTIVE DOCUMENT

EXPLORATION DESIGN

Our proposed document exploration concept supports

various degrees of visual granularity, ranging from

rough abstraction of the document as a whole, to sec-

tion and sub-section headings, to word clouds, topic

models, and down the original full text content (Fig-

ure 1 and 2). To this end, we designed a set of inter-

linked sub-systems addressing different levels of detail.

These components are interconnected via user interac-

tion, allowing for an unintermitted exploration process.

In the background, we track user interactions to de-

termine which parts of the content have already been

visited and consumed by the user. This information is

also displayed to the user in order to indicate which

information has not yet been scrutinized.

The components as well as their relations are illus-

trated in Figure 2. Next, we provide in-depth details

on how they are implemented in our prototype.

Table of Contents. For the outermost level of visual

granularity (document level) we provide an interactive

abstraction of a document’s Table of Contents. To this

end, we present the user with a view showing the main

section headers (Figure 1 and 2

⃝

). Upon clicking

such a header, the respective section is expanded, illus-

trating the section’s content with an abstraction loosely

following the document card design concept (Strobelt

et al., 2009) i.e., different visualization techniques are

used to display the textual and visual contents.

For the former, we use a Word Cloud visualization –

an established visualization method for encompassing

texts – while an Image Slider is used for the latter. On

the very left-hand side of a section container, we addi-

tionally display the section’s subsections as a further

hint on the content. On the right-hand side, the already

inspected content is indicated with a ‘history’ version

of the Word Cloud and the Image Slider. Speciﬁcally,

terms and images are added to these components af-

ter they have been reviewed (clicked on) by the user.

This history cloud keeps the context of the exploration

for the user. Alternatively, it can be used to display

non-clicked terms as to suggest content to the user.

Word Cloud. To generate the word clouds, natu-

ral language processing is used to extract ‘signiﬁcant’

terms from each chapter. In this pre-processing step, an

input text is initially separated into individual parts (to-

kenization) and irrelevant words are ﬁltered out (stop-

word removal). In the following, the set of remaining

words is transformed to its canonical form or dictio-

nary form (lemmatization) and grammatically tagged

(part-of-speech tagging). Finally, the Latent Dirich-

let Allocation (Blei et al., 2003) approach is used to

generate topic models on all nouns. For each chap-

ter, we deﬁne 6 topic models that are comprised to

different extents (term frequency) by a subset of the

chapter’s terms. This information is used as a basis for

content visualization in the form of a Word Cloud as

depicted in Figure 2

⃝

. The terms are arranged using

the Wordle word cloud algorithm (Steele and Iliinsky,

2010). The terms are not exclusive for a topic, but

topics can exhibit overlapping term compositions. In

the Word Cloud, the individual terms are uniquely dis-

played for the topic they have the most inﬂuence on.

A ‘toggle-able’ legend (Topicbar) at the bottom of the

word cloud allows the user to inﬂuence the selection of

the displayed topics. Upon changing this selection, the

word cloud is re-computed and re-drawn. Hovering

over a term toggles the Tilebar for the respective term

above it while clicking it initiates the Snippets view.

To this end, we track the interactions – how often the

user has clicked a certain term – with the word cloud.

These click counts are the basis for the so-called His-

tory Word Cloud on the right-hand side of a chapter

visualization where the count determines the size of

a term in the cloud. The same hover-and-click inter-

actions as with the ‘regular’ word cloud are possible

with the History Word Cloud.

Tilebar. Upon hovering over a term in the word

cloud, a Tilebar component is displayed above it which

allows the user to efﬁciently grasp the term’s occur-

rences over the whole document. This visualization

is inspired by the literature ﬁngerprinting concept by

Keim and Oelke (Keim and Oelke, 2007) which shows

various document properties in a drilled-down manner.

To this end, those real-valued properties are computed

for equal-sized text chunks and visualized through an

intensity map following the linear structure of the doc-

ument, i.e., from top to bottom and from left to right.

In our case, we show the term frequency for which we

use a black-to-white color coding, where white indi-

cates that the term does not appear in the respective

chunk, and black for the document-wide maximum

occurrence of the term. The Tilebar component can

be seen in Figure 2

⃝

. The individual blocks stand for

the book chapters and the red border indicates from

which document chapter the word cloud is. Note that

the chapters are arranged from left to right instead of

top to bottom since the aspect ratio of this layout is

more appropriate for the display above the term. The

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

136

Image Slider

Table of Contents

Snippets

Tilebar

Full Text

Word Cloud with Topicsbar

Subsections

History Word Cloud/

History Image Slider

(hover)

(click)

Figure 2: The main components of the interactive visual document system for exploring a German diabetes health brochure (c.f.

Section 5).

⃝

Table of Contents,

⃝

Word Cloud,

⃝

Tilebar,

⃝

Snippets,

⃝

Full Text and

⃝

Image Slider. Different interactions

(illustrated as orange arrows) allow a user to navigate from one view to another.

Tilebar allows a user to quickly answer such questions

as “does another chapter also cover this topic?” and

how frequently is it mentioned.

Snippets/Full Text. Upon clicking a term in the

word cloud, a Snippets view expands on the right-hand

side of the interface (Figure 2

⃝

). Within this view,

all sentences containing the clicked term are displayed

with a highlighting of the term. Handles at the be-

ginning and the end of a sentence allow to reveal the

preceding and succeeding sentence. Those could be

clicked iteratively to display larger parts of the docu-

ment before and after the found position. Alternatively,

the section headers, which are also shown in the snip-

pets view, can be clicked to display a section’s whole

content immediately (Figure 2

⃝

Image Slider. An off-the-shelf Image Slider compo-

nent is used to display chapters’ images (Figure 2

⃝

Since we only show three images at a time, we aim

to determine an image’s relevance in order to sort the

list of images, resulting in the most relevant images

being shown initially. To this end, we make two as-

sumptions. Firstly, we assume that images without a

caption (e.g., scenic backgrounds at the beginning of

a chapter) are rather unimportant. Secondly, we split

the set of images with captions into two tiers with the

ﬁrst tier being comprised of images showing tables,

diagrams, ﬂow charts or convey any sort of structured

information, while all the others belong to the second

tier. The information whether or not an image has a

caption results from the extraction process. We pro-

vide an additional image slider to the bottom of the

History Word Cloud, showing exclusively the chap-

ter’s images which have already been clicked (and thus

‘consumed’) by the user.

Implementation. For the implementation of the pro-

totype, we chose a web stack with a backend written

in Python with the Flask web framework. The back-

end is responsible for data management as well as all

the text processing tasks, such as the weights compu-

tation for the word clouds. The section-wise topics

are pre-computed and cached. For the frontend, we

chose the React web framework as well as the D3.js

visualization library which offers an implementation

of the Wordle word cloud algorithm. The system is

available online allowing an easy access for evaluation

participants.

5 FORMATIVE EVALUATION

The goal of our formative study was to (a) investi-

gate how the system’s design and its components are

perceived by information seekers, (b) compare it to a

linear and static CHIS in document (PDF) format, (c)

identify potentials for improvement, and (d) identify

future research questions.

As data basis, we used a T2DM information

brochure of the German health insurance AOK (Baum-

gart et al., 2021). The text document is stored in

PDF format and contains extensive health informa-

tion of over 130 pages, including ﬁgures, tables and

info-graphics. To extract the underlying health infor-

mation, we utilized the Adobe PDFBox library for full

texts and extracted images manually. In a further pre-

processing step, we assigned sub-sections and images

to the main chapters.

5.1 Participants

A total of 12 participants (four females) representing

different potential users, took part in the study. Par-

ticipants were between 26 and 62 years of age (M =

40 yrs., SD = 14 yrs.). They were asked to self-assess

their prior knowledge about T2DM (M = 1.00, SD =

1.21), computer and software skills (M = 2.25, SD =

.97) as well as previous experiences with visualiza-

Visual Document Exploration with Adaptive Level of Detail: Design, Implementation and Evaluation in the Health Information Domain

137

tions (M = 2.58, SD = 1.24) on a 5-point rating scale

(from 0 - very low to 4 - very high).

5.2 Procedure

The content of the brochure (Baumgart et al., 2021)

was displayed as PDF in Adobe Acrobat Reader and in

A+CHIS. A short explanation of basic Adobe Acrobat

Reader and A+CHIS functions, such as search func-

tions, was provided to ensure a fair starting point for all

participants regardless of their prior experiences. The

audio and on-screen activities were recorded. Over-

all, the study lasted between 60 and 90 minutes per

participant.

Cognitive Walk Through (CWT). Participants

started with a CWT (Hollingsed and Novick, 2007)

in which they were given pre-deﬁned tasks (e.g., ‘In

which chapter would you most likely start if you

wanted to ﬁnd out more about blood pressure?’) and

used them to explore the A+CHIS. This method was

used to assess the intuitiveness of the system and how

quickly the content can be grasped. Participants were

asked to express their thoughts during the tasks (i.e.,

think-aloud). Two parallel versions of 11 CWT tasks

were created to compare the information seeking in

Adobe Acrobat Reader and A+CHIS. Each participant

completed all 22 tasks in a within-subject design, with

balanced conditions in terms of system/component

order, and parallel versions of tasks.

Forced Choice. The forced choice required the

participants to make choices between the PDF and

A+CHIS regarding performance goals of system use.

These comparisons were introduced by the statement

’Would you rather use Adobe Acrobat Reader or the

A+CHIS to ...’, successively followed by the nine per-

formance goals to (a) get an overview of the domain,

(b) develop a general understanding on T2DM, (c)

search for speciﬁc keywords, (d) capture the main con-

tent, (e) search for speciﬁc images, (f) get an overview

of the most important images, (g) efﬁciently navigate

through different topics of the content, (h) get answers

to questions you might have in mind, and ﬁnally, (i)

trace past searches.

Semi-Structured Interviews. Last, semi-structured

interviews enabled targeted inquiries about users’ opin-

ions of the system, such as usefulness or appeal. Ex-

amples include ‘How helpful do you ﬁnd the various

components?’ and ‘How much does this interactive

system encourage you to explore further content?’

Figure 3: The global evaluation.

5.3 Results and Discussion

Global Evaluation. In the course of the semi-

structured interviews, participants were asked if they

had already seen or used a (i) Word Cloud, (ii) Topic

Bar, (iii) Tile Bar, or (iv) Image Slider prior to the ses-

sion. In addition, they were asked if the components

were considered as helpful and if they would like to

use them again. A ’yes-answer’ has been coded as ’1’,

a ’no-answer’ as ’0’, and indifference as ’0.5’.

As indicated in Figure 3, around 3 out of 4 par-

ticipants have seen a Word Cloud or an Image Slider

before, no participant has seen a Tile Bar and only one

a Topic Bar. The Image Slider has been used most

often before (ca. 63%). More than half of the par-

ticipants found the Word Cloud, the Tile Bar and the

Image Slider helpful and would consider using them

again. Only around 1 out of 4 found the Topic Bar (in

its current form) helpful and would consider using it

again.

Figure 4: The forced choice results with respect to perfor-

mance goals.

Performance Goals. The results of the forced

choices are shown in Figure 4. The potential per-

formance goals in information processing have been

sorted from left (more abstract) to right (more speciﬁc).

A ’+1’ was added if a participant chose A+CHIS, a ’-1’

in case of the Adobe Acrobat Reader and ’0’ for an

indecisive choice. Thus, the ordinate ranges from -12

(all participants chose the Adobe Acrobat Reader) to

12 (all chose A+CHIS). Figure 4 shows the values for

the whole sample (blue circles), for older participants

(n = 6,

≥

38 yrs., grey circles) and for younger ones (n

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

138

= 6,

38 yrs., orange circles). Taking into account that

A+CHIS ’competed against’ the well-known Adobe

Acrobat Reader, the overall results of the forced choice

are promising. The differences between the two age

groups should not be over-interpreted due to the small

sample size and should rather be considered an avenue

for future research. The resulting overall values for

the nine performance goals are either close to the hor-

izontal ’middle-line’ (indicating that participants are

equally inclined towards A+CHIS and Adobe Acrobat

Reader and/or are indifferent) or clearly above, such

as for efﬁciently navigating through different topics of

the content, getting an overview on the most important

images, or tracing searches.

Non-Linear Content Exploration. Think aloud and

interview data revealed that the system arouses cu-

riosity and invites to further exploration of system

features and contents. The main reasons given were

the aesthetic color design and the efﬁcient and enjoy-

able search in the system. Several participants particu-

larly emphasized that the Word Cloud, in contrast to a

non-interactive system, motivates to engage with the

content further via making interesting terms visible.

However, some participants did not yet feel they had

sufﬁciently ﬁgured out how the system works to effec-

tively explore the content. In particular, the lack of

a familiar linear structure made it difﬁcult for some

participants to maintain an overview of the content.

While these participants were generally open to explor-

ing the content, they may have needed more support

for using the current A+CHIS.

6 OVERALL DISCUSSION AND

CONCLUSION

Our design allows users to seamlessly navigate the

content of a document and change the visual repre-

sentation and level of detail on the ﬂy. Our approach

makes use of well-known visual analysis techniques

(word clouds, topic models, tile bars, and keyword

search). It is useful for users who would either pre-

fer to follow a traditional linear document navigation

and also move non-linearly between content and detail.

To the best of our knowledge, there are few empiri-

cal studies comparing the cognitive and motivational

aspects of using document visualizations such as tile

bars and word clouds, with those of linear document

readers like Adobe PDF viewer. Our evaluation is a

ﬁrst step that conﬁrmed our concept could heighten

interest and raise curiosity, which also allows users

to more efﬁciently navigate content of interest by a

distant-reading approach.

Our design allows following both the edited con-

tent of a given document (supervised structure) as well

as an automatically computed topic models (unsuper-

vised structure). In our study, users did not seem to

make great use of the topic model structure. This may

be in part due to them not being familiar with topic

models, but also difﬁculties to make sense of topics

comprised by lists of keywords. Recent studies have

investigated the impact of word clouds for topic under-

standing (Smith et al., 2017) and keyword summaries

(Felix et al., 2018). It turned out that the main advan-

tages of word clouds lie in speed (e.g., recognizing

most frequent terms) while disadvantages may arise in

numeric encoding and for larger sets of keywords.

A second main element of our design is its observ-

ing of the users document exploration. Speciﬁcally,

we track which keywords have been hovered and se-

lected. This is considered important information prove-

nance data, which, in our system, can be used in two

ways. First, a history word cloud shows which topics

have been already explored. Second, a word cloud of

under-explored keywords can be created to motivate

the user to explore unseen content. The latter is an

important functionality for content recommendation,

and possibly mitigation of cognitive biases or harmful

pre-conceptions. Our evaluation is a ﬁrst step in this

regard. Future work should look for the speciﬁc ad-

vantages which aggregated document representations

can offer, but also possible misunderstandings which

can occur due to the highly aggregated nature of some

of the content presentations.

Document visualization techniques provide an am-

plitude of opportunities for improved support of in-

formation seeking tasks. We presented a document

exploration design, based on the idea of allowing users

to seamlessly navigate document content at different

levels of visual abstraction and detail. We made use

of established document visualization techniques and

applied our design to the T2DM information use-case.

We evaluated our implemented system by performing

comparison against a traditional document reader. The

results are promising in showing that our approach

motivates content exploration and is easy to learn. We

also presented a concept for possible automatic adapta-

tion of main display parameters, which could provide

opportunities to support speciﬁc information needs

and visualizations as well as reading preferences. In

future work, we intend to research automatic recom-

mendation and develop adaptation methods based on

the current system.

Visual Document Exploration with Adaptive Level of Detail: Design, Implementation and Evaluation in the Health Information Domain

139

ACKNOWLEDGEMENTS

This work was funded by the Austrian Science Fund

(FWF) as part of the project ’Human-Centered Inter-

active Adaptive Visual Approaches in High-Quality

Health Information’ (A+CHIS; Grant No. FG 11-B).

REFERENCES

Arbeitsgruppe GPGI (2016). Gute praxis gesundheitsin-

formation. Zeitschrift f

ur Evidenz, Fortbildung und

Qualit

at im Gesundheitswesen, 110-111:85–92.

Baumgart, J., Viegener, U., and Pohl, C. (2021). Den

diabetes im griff: Ein handbuch f

ur patientinnen

und patienten mit diabetes mellitus typ 2. AOK-

Bundesverband, Berlin.

Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent

dirichlet allocation. Journal of machine Learning re-

search, 3:993–1022.

British Diabetic Association (2022). Diabetes UK - Know

diabetes. Fight diabetes. (accessed on 2022-06-27).

Bundesministerium f

ur Gesundheit (2022). Verl

assliche

Informationen f

ur Ihre Gesundheit. (accessed on 2022-

06-27).

Bunge, M., Muhlhauser, I., and Steckelberg, A. (2010).

What constitutes evidence-based patient information?

Overview of discussed criteria. Patient Educ Couns,

78(3):316–28.

Cao, N., Gotz, D., Sun, J., Lin, Y.-R., and Qu, H. (2011).

Solarmap: Multifaceted visual analytics for topic ex-

ploration. In 2011 IEEE 11th International Conference

on Data Mining, pages 101–110.

Cao, N., Sun, J., Lin, Y.-R., Gotz, D., Liu, S., and Qu, H.

(2010). Facetatlas: Multifaceted visualization for rich

text corpora. IEEE Transactions on Visualization and

Computer Graphics, 16(6):1172–1181.

Felix, C., Franconeri, S., and Bertini, E. (2018). Taking word

clouds apart: An empirical investigation of the design

space for keyword summaries. IEEE Transactions on

Visualization and Computer Graphics, 24(1):657–666.

Government of Canada (2022). Health Canada. (accessed

on 2022-06-27).

Gu, Y., Wang, C., Ma, J., Nemiroff, R. J., Kao, D. L., and

Parra, D. (2017). Visualization and recommendation of

large image collections toward effective sensemaking.

Information Visualization, 16(1):21–47.

org, C., Liu, Z., Kihm, J., Choo, J., Park, H., and Stasko,

J. (2013). Combining computational analyses and in-

teractive visualization for document exploration and

sensemaking in jigsaw. IEEE Transactions on Visual-

ization and Computer Graphics, 19(10):1646–1663.

Hearst, M. A. (1995). Tilebars: Visualization of term dis-

tribution information in full text information access.

In Proceedings of the SIGCHI conference on Human

factors in computing systems, pages 59–66.

Heimerl, F., Lohmann, S., Lange, S., and Ertl, T. (2014).

Word cloud explorer: Text analytics based on word

clouds. In 2014 47th Hawaii International Conference

on System Sciences, pages 1833–1842.

Hollingsed, T. and Novick, D. G. (2007). Usability inspec-

tion methods after 15 years of research and practice.

In Proceedings of the 25th annual ACM international

conference on Design of communication.

Isenberg, P., Heimerl, F., Koch, S., Isenberg, T., Xu, P.,

Stolper, C. D., Sedlmair, M., Chen, J., M

oller, T., and

Stasko, J. (2017). Vispubdata.org: A metadata collec-

tion about ieee visualization (vis) publications. IEEE

Trans. Vis. Comput. Graph., 23(9):2199–2206.

anicke, S., Franzini, G., Cheema, M. F., and Scheuermann,

G. (2015). On close and distant reading in digital

humanities: A survey and future challenges. In EuroVis

(STARs), pages 83–103.

Keim, D. A. and Oelke, D. (2007). Literature ﬁngerprint-

ing: A new method for visual literary analysis. In

2007 IEEE Symposium on Visual Analytics Science

and Technology, pages 115–122. IEEE.

Kim, K., Ko, S., Elmqvist, N., and Ebert, D. S. (2011).

Wordbridge: Using composite tag clouds in node-link

diagrams for visualizing content and relations in text

corpora. In 2011 44th Hawaii International Conference

on System Sciences, pages 1–8.

Moretti, F. (2005). Graphs, maps, trees: abstract models for

a literary history. Verso.

Plaisant, C., Heller, D., Li, J., Shneiderman, B., Mushlin, R.,

and Karat, J. (1998). Visualizing medical records with

lifelines. In Conference Summary on Human Factors in

Computing Systems, CHI ’98, page 28–29. Association

for Computing Machinery.

Rind, A., Wang, T. D., Aigner, W., Miksch, S., Wongsupha-

sawat, K., Plaisant, C., and Shneiderman, B. (2013). In-

teractive information visualization to explore and query

electronic health records. Foundations and Trends® in

Human–Computer Interaction, 5(3):207–298.

Schwarzer, R. (2008). Modeling health behavior change:

How to predict and modify the adoption and mainte-

nance of health behaviors. Applied psychology, 57(1).

Smith, A., Lee, T. Y., Poursabzi-Sangdeh, F., Boyd-Graber,

J., Elmqvist, N., and Findlater, L. (2017). Evaluating

Visual Representations for Topic Understanding and

Their Effects on Manually Generated Topic Labels.

Transactions of the Association for Computational Lin-

guistics, 5:1–16.

Standl, E., Khunti, K., Hansen, T. B., and Schnell, O. (2019).

The global epidemics of diabetes in the 21st century:

Current situation and perspectives. Eur J Prev Cardiol,

26(2 suppl):7–14.

Steele, J. and Iliinsky, N. (2010). Beautiful visualization:

Looking at data through the eyes of experts. ” O’Reilly

Media, Inc.”.

Strobelt, H., Oelke, D., Rohrdantz, C., Stoffel, A., Keim,

D. A., and Deussen, O. (2009). Document cards: A

top trumps visualization for documents. IEEE Trans.

Vis. Comput. Graph., 15(6):1145–1152.

Sweller, J. (2005). Implications of cognitive load theory

for multimedia learning. The Cambridge handbook of

multimedia learning, 3(2):19–30.

IVAPP 2023 - 14th International Conference on Information Visualization Theory and Applications

140

Wang, Q. and Laramee, R. S. (2022). EHR STAR: the state-

of-the-art in interactive EHR visualization. Comput.

Graph. Forum, 41(1):69–105.

Wu, Y., Provan, T., Wei, F., Liu, S., and Ma, K.-L. (2011).

Semantic-preserving word clouds by seam carving.

Computer Graphics Forum, 30(3):741–750.

Visual Document Exploration with Adaptive Level of Detail: Design, Implementation and Evaluation in the Health Information Domain

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