TagSpheres: Visualizing Hierarchical Relations in Tag Clouds

Stefan J

anicke and Gerik Scheuermann

Image and Signal Processing Group, Institute for Computer Science, Leipzig University, Leipzig, Germany

Keywords:

Tag Clouds, Text Visualization, Hierarchical Data.

Abstract:

Tag clouds are widely applied, popular visualization techniques as they illustrate summaries of textual data in

an intuitive, lucid manner. Many layout algorithms for tag clouds have been developed in the recent years,

but none of these approaches is designed to reﬂect the notion of hierarchical distance. For that purpose,

we introduce a novel tag cloud layout called TagSpheres. By arranging tags on various hierarchy levels and

applying appropriate colors, the importance of individual tags to the observed topic gets assessable. To explore

relationships among various hierarchy levels, we aim to place related tags closely. Three usage scenarios

from the digital humanities, sports and aviation, and an evaluation with humanities scholars exemplify the

applicability and point out the beneﬁt of TagSpheres.

1 INTRODUCTION

The usage of tag clouds to visualize textual data is

a relatively novel technique, which was rarely ap-

plied in the past century. In 1976, Stanley Milgram

was one of the ﬁrst scholars who generated a tag

cloud to illustrate a mental map of Paris, for which

he conducted a psychological study with inhabitants

of Paris, aiming to analyze their mental represen-

tation of the city (Milgram and Jodelet, 1976). In

1992, a German edition of “Mille Plateaux”, written

by the French philosopher Gilles Deleuze, was pub-

lished with a tag cloud printed on the cover to summa-

rize the book’s content (Deleuze and Guattari, 1992).

This idea to present a visual summary of textual data

can be seen as the primary purpose of tag clouds (Sin-

clair and Cardew-Hall, 2008). But the popularity of

tag clouds nowadays is attributable to a frequent us-

age in the social web community in the 2000s as

overviews of website contents. Although there are

known theoretical problems concerning the design of

tag clouds (Vi

egas and Wattenberg, 2008), they are

generally seen as a popular social component per-

ceived as being fun (Hearst and Rosner, 2008). With

the simple idea to encode the frequency of terms to

a given topic, tag clouds are intuitive, comprehensi-

ble visualizations, which are widely used metaphors

(1) to display summaries of textual data, (2) to sup-

port analytical tasks such as the examination of text

collections, or even (3) to be used as interfaces for

navigation purposes on databases.

In the recent years, various algorithms that com-

pute effective tag cloud layouts in an informative and

readable manner has been developed. One of the most

popular techniques is Wordle (Vi

egas et al., 2009),

which computes compact, intuitive tag clouds and can

be generated on the ﬂy using a web-based interface.

Although the produced results are very aesthetic, the

different used colors do not transfer information and

the ﬁnal arrangement of tags depends only on the

scale, and not on the content of tags or potential re-

lationships among them. Some approaches attend to

the matter of visualizing more information than the

frequency of terms with tag clouds – most often to

compare textual summaries of different data facets.

In this paper, we present the tag cloud design Tag-

Spheres, which endeavors to effectively visualize hi-

erarchies in textual summaries. The motivation arose

from research on philology. Humanities scholars

wanted to analyze the clause functions of an ancient

term’s co-occurrences. Querying the large database,

the scholars often face numerous results in the form

of text passages. When only plain lists are provided

to interact with the results, the discovery of signiﬁcant

text passages and the analysis of the contexts in which

the chosen term was used becomes laborious. To sup-

port this task, we provide summaries of text passages

in the form of interactive tag clouds that group terms

in accordance to their distance to the search term. So,

the humanities scholar gets an overview and is able to

retrieve text passages of interest on demand.

http://www.wordle.net/

Jänicke, S. and Scheuermann, G.

TagSpheres: Visualizing Hierarchical Relations in Tag Clouds.

DOI: 10.5220/0005654100150026

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 2: IVAPP, pages 17-28

ISBN: 978-989-758-175-5

Figure 1: Wordle of Edgar Allan Poe’s The Raven.

We designed TagSpheres in a way that various

types of text hierarchies can be visualized in an intu-

itive, comprehensible manner. To emphasize the wide

applicability of TagSpheres, we list several examples

from the digital humanities, sports and aviation.

2 RELATED WORK

Although tag clouds rather became popular in the so-

cial media, research in visualization attended to the

matter of developing various layout techniques in the

last years. A basic tag cloud layout is a simple list of

words placed on multiple lines (Vi

egas et al., 2007).

In such a list, tags are typically ordered by their im-

portance to the observed issue, which is encoded by

font size (Murugesan, 2007). An alphabetical order

is also often used, but a study revealed that this or-

der is not obvious for the observer (Hearst and Ros-

ner, 2008). Later, more sophisticated tag cloud lay-

out approaches that rather emphasize aesthetics than

meaningful orderings were developed. A represen-

tative technique is Wordle (Vi

egas et al., 2009; Jo

et al., 2015), which produces compact aesthetic lay-

outs with tags in different colors and orientations, but

both features do not transfer any additional informa-

tion. A Wordle showing the most important terms in

Edgar Allan Poe’s The Raven is given in Figure 1.

Various approaches highlight relationships among

tags by forming visual groups. In thematically clus-

tered or semantic tag clouds, the detection of tags

belonging to the same topic is supported by placing

these tags closely (Lohmann et al., 2009). Tradi-

tional, semantic word lists place clustered tags suc-

cessively (Schrammel and Tscheligi, 2014). More so-

phisticated layout methods often use force directed

approaches with semantically close terms attracting

each other (Cui et al., 2010; Wang et al., 2014; Liu

et al., 2014). After force directed tag placement, tag

cloud layouts can be compressed by removing occur-

Figure 2: TagPie supports the comparison of co-occur-

rences of the Latin terms gibbus, gibbum and gibbosus.

ring whitespaces (Wu et al., 2011).

Some methods generate individual tag clouds for

each group of related tags, and combine the resultant

multiple tag clouds to a single visual unity afterwards.

An example is the Star Forest method (Barth et al.,

2014), which applies a force directed method to pack

multiple tag clouds. Other approaches use predeﬁned

tag cloud containers, e.g., user-deﬁned polygonal

spaces in the plane (Paulovich et al., 2012), polygonal

shapes of countries (Nguyen et al., 2011), or Voronoi

tesselations (Seifert et al., 2011). Newsmap uses a

treemap layout (Shneiderman and Plaisant, 1998) to

group newspaper headlines of the same category in

blocks (Weskamp, 2015). Morphable Word Clouds

morph the shapes of tag cloud containers in order to

visualize temporal variance in text summaries (Chi

et al., 2015). For the comparison of the tags of var-

ious text documents, a ConcentriCloud divides an el-

liptical plane into sectors that list shared tags of sev-

eral subsets of the underlying texts (Lohmann et al.,

2015). Due to the rather independent computation

of individual tag clouds – which often leads to large

whitespaces in the ﬁnal composition step – the above

mentioned methods can be seen as sophisticated small

multiples. A rather traditional small multiples ap-

proach is Words Storms (Castell

a and Sutton, 2014)

that supports the visual comparison of textual sum-

maries of documents.

Tag clouds also have been used to visualize trends.

Parallel Tag Clouds generate alphabetically ordered

tag lists as columns for a number of time slices and

highlight the temporal evolution of a tag placed in

various columns on mouse interaction (Collins et al.,

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

Table 1: Characteristics of TagSpheres usage scenarios.

domain digital humanities sports aviation

(see Section 4.1) (see Section 4.2) (see Section 4.3)

task analyzing the clause func-

tions of the co-occurrences

of a search term T

comparing the performances

of teams in championships

observing all direct ﬂights

from an airport or a city

search term T best performing teams departure airport/city

, . . . , H

co-occurrences in depen-

dency on the word distance

to T

teams grouped by decreas-

ing performance

direct federal (H

), conti-

nental (H

) and worldwide

ﬂights (H

)

n 4 6, 8 2..4

w(t) number of (co-)occurrences

of t

number of a team’s appear-

ances

inverse distance weighting

between departure and ar-

rival airports/cities

p(t) equally labeled tag of a

higher hierarchy level

same team if already placed

on a higher hierarchy level

previously placed tag of the

same country/continent

strong tag

relations

equally labeled tags same teams if placed on

multiple hierarchy levels

departure/arrival air-

ports/cities

weak tag

relations

spelling variants N/A airports/cities of the same

country/continent

2009). In contrast, SparkClouds attach a graph show-

ing the tag’s evolution over time (Lee et al., 2010).

Other approaches overlay time graphs with tags char-

acteristic for certain time ranges (Shi et al., 2010).

Only few approaches generate multifaceted tag

cloud layouts in a single, continuous ﬂow that in-

cludes the positioning of all tags belonging to vari-

ous groups. RadCloud visualizes tags belonging to

various groups within a shared elliptical area (Burch

et al., 2014). In Compare Clouds, tags of two media

frames (MSM, Blogs) are comparatively visualized in

a single cloud (Diakopoulos et al., 2015). To support

the comparative analysis of multiple tag groups, Tag-

Pies are arranged in a pie chart manner (J

anicke et al.,

2015a). An example showing the comparative visual-

ization of the co-occurrences of Latin terms is shown

in Figure 2.

Although techniques like TagPies or Parallel Tag

Clouds are capable of visualizing sequences of tag

groups, none of the mentioned approaches endeavors

to visually encode generic hierarchical information

intuitively in a single, compact, aesthetic tag cloud.

TagSpheres – presented in this paper – are designed

to ﬁll this gap.

3 DESIGNING TAGSPHERES

The central idea of TagSpheres is the visualization of

textual summaries that comprise hierarchical infor-

mation. This paper provides three usage scenarios

that exemplify hierarchies in textual data (see Sec-

tion 4). An overview of the characteristics of these

examples is given in Table 1.

Given n hierarchy levels H

, . . . , H

, the top hier-

archy level H

contains tags representing the focus of

interest of a usage scenario. All other tags are divided

into n − 1 groups in dependency on their hierarchical

distance according to the observed topic, or to the tags

on H

. Each tag t in TagSpheres has a weight w(t) re-

ﬂecting its importance, and an optional predecessor

tag p(t) representing a relationship to another tag that

was placed before t and usually belongs to a higher

hierarchy level. In dependency on the observed topic,

it might be necessary to place the same tag on several

hierarchy levels to encode the change of a tag’s impor-

tance among hierarchies. In such cases, predecessor

tags help to visually link these tags.

3.1 Design Decisions

When designing TagSpheres, we use the following,

well-established design features for tag clouds:

• Font Size: Evaluated as the most powerful prop-

erty (Bateman et al., 2008), font size encodes the

weight w(t) of a tag.

• Orientation: As rotated tags are perceived

as “unstructured, unattractive, and hardly read-

able” (Waldner et al., 2013), we do not rotate tags

to keep the layout easily explorable.

• Color: Being the best choice to distinguish cat-

egories (Waldner et al., 2013), various colors are

assigned to tags belonging to different hierarchy

levels. As TagSpheres encode the distance to a

TagSpheres: Visualizing Hierarchical Relations in Tag Clouds

(a) Resultant color maps for n = 2, . . . , 8

hierarchy levels.

(b) Using spheres for the tags of dif-

ferent hierarchy levels.

guarantee hierarchical coherence.

Figure 3: TagSpheres layout algorithm details.

(a) Placing all tags of H

. (b) Placing a tag without predecessor. (c) Placing a tag with predecessor.

Figure 4: Determining tag positions using an Archimedean spiral.

given topic, the usage of a categorial color map is

inappropriate. Unfortunately, suitable sequential

color maps as provided by the ColorBrewer (Har-

rower and Brewer, 2003) produce less distinctive

colors even for a small number of hierarchy levels,

so that adjacent tags belonging to different hierar-

chy levels are hard to classify. Following the sug-

gestions given by Ware (Ware, 2013), we deﬁned

a divergent cold-hot color map using red for the

ﬁrst hierarchy level and blue for tags belonging to

the last hierarchy level n. To avoid uneven visual

attraction of tags, we only chose saturated colors

that are in contrast to the white background. Ex-

ample color maps for up to eight hierarchy levels

are shown in Figure 3(a).

3.2 Layout Algorithm

In preparation, the tags are sorted by increasing hier-

archy level, so that all tags within the same hierarchi-

cal distance to H

are placed successively. The tags of

each hierarchy level are ordered by decreasing weight

to ensure that important tags are circularly well dis-

tributed.

To avoid large whitespaces, a problem addressed

by Seifert (Seifert et al., 2008), our method follows

the idea of the Wordle algorithm (Vi

egas et al., 2009)

– permitting overlapping tag bounding boxes if the

tags’ letters do not occlude – to determine the posi-

tions of tags. So, we obtain compact, uniformly look-

ing tag clouds for the underlying hierarchical, textual

data. To ensure well readable tag clouds, we use a

minimal padding between letters of different tags.

As shown in Figure 3(b), we aim to visually com-

pose tags of the same hierarchy level in the form of

spheres around the tag cloud origin at (0, 0). Ini-

tially, we iteratively determine positions for the tags

of H

in the central sphere using an Archimedean spi-

ral originating from (0, 0). An example is given in

Figure 4(a). For each tag t of the remaining hierarchy

levels H

, . . . , H

, we also use (0, 0) as spiral origin,

if p(t) is not provided (see Figure 4(b)). If p(t) is

deﬁned, we use the predecessor’s position as spiral

origin (see Figure 4(c)). As a consequence, hierar-

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

chically related tags are placed closely and visually

compose in the form of rays originating from (0, 0) as

shown in Figure 8. In contrast to other spiral based tag

cloud algorithms, we avoid to cover whitespaces with

tags of hierarchy level H

within spheres of already

processed hierarchy levels H

, . . . , H

i−1

. Dependent

on the quadrant in the plane, in which a tag shall be

placed, we search for already placed tags intersect-

ing two vectors originating from the dedicated posi-

tion as illustrated in Figure 3(c). If no intersections

are found, we place the tag. This approach coheres

all tags of a hierarchy level as a visual unity outside

the inner bounds of the previously processed hierar-

chy levels’ spheres.

3.3 Interactive Design

Implemented as an open source JavaScript library,

TagSpheres can be dynamically embedded into web-

based applications. With mouse interaction, we en-

able the user to detect hierarchically related tags

quickly. Thereby, we distinguish between strongly

and weakly related tags, which are deﬁned in depen-

dency on the underlying usage scenario (see Table 1).

Related tags are shown on mouseover (see Figure 5).

For strongly related tags we use a black font on trans-

parent backgrounds having the hierarchy levels’ as-

signed color. In contrast, weakly related tags retain

their saturated font color, but gray, transparent back-

grounds indicate relationships.

TagSpheres provide a conﬁgurable tooltip dis-

played when hovering or clicking a tag to be used,

e.g., to list all related tags and their weights. The

mouse click function can be used for displaying ad-

ditional information. e.g., to link to external sources,

or to show text passages containing the chosen tag.

4 USE CASES

TagSpheres are applicable whenever statistics of un-

structured text shall be visualized in the form of a tag

cloud and a decent hierarchy among the tags exists.

This section illustrates usage scenarios of TagSpheres

for text-based data from three different domains: dig-

ital humanities, sports and aviation.

4.1 Digital Humanities Scenario

Within the digital humanities project eXChange,

his-

torians and classical philologists work with a database

containing a large amount of digitized historical texts

http://exchange-projekt.de/

in Latin and ancient Greek. Usually, humanities

scholars pose keyword based search queries and often

receive numerous results, which are hard to revise in-

dividually. As a consequence, the generation of valu-

able hypotheses is a laborious, time-consuming pro-

cess. To facilitate the humanities scholars’ workﬂows,

we develop visual interfaces that attempt to steer the

analysis of search results into promising directions.

TagPies – also developed within the eXChange

project – are tag clouds arranged in a pie chart man-

ner that support the comparison of multiple search

query results (J

anicke et al., 2015a). Using a Tag-

Pie, humanities scholars analyze contextual similar-

ities and differences of the observed terms – an ex-

ample is given in Figure 2. Whereas the tags of the

same groups are placed in the same circular sectors

in TagPies to support their comparative analysis, the

intention of TagSpheres is the visualization of hierar-

chical information. This supports approaching a fur-

ther research interest of the humanities scholars: the

analysis and classiﬁcation of a term’s co-occurrences

according to their clause functions. For this purpose,

the scholars required four-level TagSpheres display-

ing the following tags:

: search term T ,

: co-occurrences of T with word distance 1,

: co-occurrences of T with word distance 2, and

: co-occurrences of T with word distance 3 up to

word distance m.

The font size of T on level H

encodes how frequent

the search term occurs in the underlying text corpus;

the font sizes of all other terms reﬂect their number

of co-occurrences with T in dependency on the corre-

sponding distance. On H

, font sizes are normalized

in relation to the distance range m − 2. A tag on hi-

erarchy level H

receives a predecessor tag if the cor-

responding term occurs on one of the previous layers

i−1

, . . . , H

A use case provided by one of the humanities

scholars involved in the eXChange project shall il-

lustrate the utility of TagSpheres to support the clas-

siﬁcation of a term’s co-occurrences by their clause

functions. Analyzing the co-occurrences of morbo

(disease), terms in similar relationships to the given

topic were discovered and classiﬁed (see Figure 5). In

large distances, the humanities scholar found objects

in form of affected parts of the body, e.g., head (ca-

put), soul (animo) and limbs (membrorum), affected

persons, e.g., son (ﬁlius), woman (mulier) and king

(rex), and related places, e.g., Rome (romam), church

(ecclesia) and villa. Closer to morbo (most often with

distance 1 or 2), typical attributes and predicates can

be found. Whereas attributes describe the type or

TagSpheres: Visualizing Hierarchical Relations in Tag Clouds

Figure 5: The analysis of co-occurrences of the Latin term morbo (disease) on word distance.

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

Figure 6: Close reading of text passages containing morbo

and comitiali with word distance 1.

intensity of the disease, e.g., pestilential (pestifero),

heavy (gravi), deadly (exitiali) and acute (acuto), the

occurring predicates illustrate the disease’s progress,

e.g., seize (correptus), dissappear (periit) and wors-

ening (ingravescente). Adjacent to morbo, speciﬁc

terms for “moral” diseases, e.g., greediness (avari-

tiae), arrogance (superbiae) and lust (concupiscen-

tiae), and actual diseases like jaundice ([morbo] re-

gio), leprosy (leprae) and two common names for

epilepsy ([morbo] comitiali, [morbo] sacro) occur.

In this usage scenario, the interaction capabili-

ties of TagSpheres are tailored according to the needs

of the humanities scholars. Hovering a tag opens a

tooltip showing the term’s number of occurrences on

all hierarchy levels as strongly related tags. Addition-

ally, variant spellings or cases of the term are listed

with their corresponding frequencies as weakly re-

lated tags to support the analysis process. An impor-

tant requirement for the humanities scholars was the

discovery of potentially interesting text passages, but

they desired a straightforward access to the underly-

ing texts in general. This so-called close reading is of-

ten reported as an important component when design-

ing visualizations for humanities scholars (J

anicke

et al., 2015b). TagSpheres support close reading by

clicking a tag, which displays the corresponding text

passages containing the search term and the clicked

term with the chosen distance. An example for text

passages containing the adjacent terms morbo and

comitiali is shown in Figure 6.

4.2 Championship Performances

This scenario illustrates how TagSpheres can be used

to comparatively visualize performances in champi-

onships. An example is given in Figure 7 that illus-

trates the success of football clubs ever played in Eng-

Figure 7: Performances of English ﬁrst league football

clubs from 1888/89 – 2014/15.

land’s ﬁrst league. Here, we use the average rank at

the end of the seasons to cluster 68 clubs into eight

hierarchy levels, and font size encodes the number of

appearances.

For another example, we processed a dataset con-

taining the results of all national teams ever qualiﬁed

for the FIFA World Cup. We receive the following

six-level hierarchy:

: FIFA World Champions,

: second placed national teams,

: national teams knocked out in the semiﬁnal,

: national teams knocked out in the quarterﬁnal,

: national teams knocked out in the second round

(second group stage or last 16), and

: national teams knocked out in the (ﬁrst) group

stage.

The nations’ names are used as tags and font size en-

codes how often a national team partook a champi-

onship round without reaching the next level. There-

fore, most nations occur on various hierarchy levels.

If a tag t for a nation to be placed on H

was already

placed at a higher hierarchy level H

i−1

, . . . , H

, we use

the corresponding tag as predecessor p(t).

Figure 8 shows the resultant TagSphere. Espe-

cially this scenario illustrates the beneﬁt of using the

positions of predecessor tags as spiral origins for suc-

cessor tags. In most cases, the various tags of a nation

are closely positioned. Hovering a tag displays the all-

time performance of the corresponding national team

for all championship rounds in a tooltip. Expectedly,

Brazil and Germany achieved very good results, es-

pecially in the last championship rounds. In contrast,

Italy was often knocked out in the ﬁrst round, but in

case of reaching the semiﬁnal (8x), Italy often became

TagSpheres: Visualizing Hierarchical Relations in Tag Clouds

Figure 8: Performances of all nations qualiﬁed for the FIFA World Cup.

FIFA World Champion (4x). England and Spain show

nearly equal performances. With the same number of

appearances (38x), both nations reached the semiﬁnal

only twice. Few nations have a 100% success rate in

the group stage. Qualiﬁed three times for the FIFA

World Cup, Senegal always reached the quarterﬁnals.

Most nations, e.g., Sweden and Cameroon, show the

expected pattern “the higher the championship round,

the smaller the number of appearances.”

4.3 Airport Connectivity

To analyze the federal, continental and worldwide

connectivity of airports, we derived a dataset from the

OpenFlights database,

which provides a list of di-

http://openﬂights.org/data.html

rect ﬂight connections between around 3,200 airports

worldwide. With the selected departure airport d (or

city) on H

, all other airports (or cities) reachable with

a non-stop ﬂight cluster into three further hierarchy

levels:

: airports/cities in the same country as d,

: airports/cities on the same continent as d, and

: all other reachable worldwide airports/cities.

As tags we chose either airport names, the pro-

vided IATA codes,

or the corresponding city names.

In this scenario, font size encodes the inverse geo-

graphical distance between the departure airport d =

{lat

, lon

} and an arrival airport a = {lat

, lon

}. To

keep the deviation to the actual distance as small as

http://www.iata.org/services/pages/codes.aspx

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

Figure 9: Direct ﬂight connections from airports in Sydney, Rome, Frankfurt and Cagliari.

G = 6378 · arccos



sin(lat

) · sin(lat

) + cos(lat

) · cos(lat

) · cos(lon

− lon

)



(1)

possible, we apply the great circle distance G (Head,

2003), deﬁned by Equation 1. Predecessor tags are

used to place airports or cities of the same country or

continent closely. For a tag t to be placed on H

, we

choose the ﬁrst placed tag with the same associated

country as predecessor, if existent; for H

, we choose

the ﬁrst placed tag with the same associated continent.

Thus, a predecessor tag p(t) in this scenario always

TagSpheres: Visualizing Hierarchical Relations in Tag Clouds

belongs to the same hierarchy level as t.

Figure 9 shows TagSpheres for non-stop ﬂights

from various airports or cities. All examples show

that airports/cities of the same countries/continents

are placed closely in clusters. For Sydney, no tags

are placed on H

, and for Cagliari, no connections to

airports outside Europe exist. When the user hovers a

tag, the corresponding connection and the travel dis-

tance are shown in a tooltip. Clicking a tag redirects

to Google Flights

listing possible ﬂight connections.

5 DISCUSSION

The original motivation to design TagSpheres was to

support humanities scholars in analyzing the clause

functions of a search terms’ co-occurrences (see Sec-

tion 4.1 for details). Some aspects of evaluating the

design during the corresponding eXChange project

are outlined below. Furthermore, we discuss general

limitations of TagSpheres.

5.1 Evaluation

To ensure creating a valuable interface for our tar-

geted user group, we closely collaborated with the hu-

manities scholars in the design phase aiming to trans-

form their notion of hierarchical distance as appropri-

ate as possible. This included project workshops and

regular meetings, where we demonstrated current pro-

totypes, and the humanities scholars were able to sug-

gest their ideas on the design, the interactivity and the

embedding of TagSpheres into their research environ-

ment. Finally, we conducted a small evaluation with

seven humanities scholars (ﬁve female, two male) –

ﬁve of them were members of the eXChange project.

Due to that small number of participants, diversiﬁed

research interests and the exploratory nature of the

humanities scholars’ tasks, a formal user study with

performance data was not viable. To encourage the

participants to intensely work with TagSpheres, we al-

lowed them to query the database with terms of their

own interest (preference data). A survey on close and

distant reading visualizations designed for humanities

scholars discusses in detail that quantitative, system-

atic evaluations are hardly realizable in digital human-

ities projects (J

anicke et al., 2015b).

In a questionnaire, we asked the humanities schol-

ars for subjective ratings on several aspects concern-

ing TagSpheres. They needed to choose a value on a

Likert scale from 1 (very bad) to 7 (very good), and

we also asked them to justify their decisions. The re-

sults are shown in Figure 10.

https://www.google.com/ﬂights/

Before developing TagPies (J

anicke et al., 2015a),

we performed a case study on state-of-the-art tag

clouds where we found out that the aesthetics of tag

clouds plays an important role for humanities schol-

ars. The aesthetics of TagSpheres was generally per-

ceived as good. Very important for us were the opin-

ions of humanities scholars if our design would intu-

itively transmit their notion of hierarchical distance.

Only two scholars were undecided, but four schol-

ars gave the best rate stating that TagSpheres are

“easily understandable.” Especially, the chosen col-

ors “clearly visualize the word distance between co-

occurrences and the search term.” As the tags are

shown in different colors and varying font size, we

further asked for the readability of tags, which was

mostly justiﬁed positively. Although the humanities

scholars stated that “all important co-occurrences of

the search term are visible at ﬁrst glance,” it was hard

for them to detect often closely positioned similar or

related terms on different hierarchy levels. But all par-

ticipants stated that the provided means of interaction

facilitate this task and overall foster the understanding

of the visualization and the explorative analysis of re-

sults. Finally, the utility of TagSpheres to support the

humanities scholars in examining research questions

regarding the clause functions of a search terms’ co-

occurrences was also rated as good.

5.2 Limitations

The main objective of the presented layout algo-

rithm is to combine a hierarchical information of tex-

tual data with the aesthetics of tag clouds. In con-

trast to the usual approach to always initialize an

Archimedean spiral at the tag cloud origin (0, 0) when

determining the position of a tag, the usage of prede-

Figure 10: TagSpheres questionnaire results.

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

cessor tags as spiral origins slightly affects the uni-

form appearance of the result in some cases (e.g., see

Figure 9). Occasionally, little holes occur, and – at

the expense of visualizing the hierarchical structure

of the underlying data – the tag cloud boundaries get

distorted.

The proposed hot-cold color map used to visu-

ally convey hierarchical distance generates well dis-

tinguishable colors when the number of hierarchy lev-

els is small. For a larger number of hierarchies as dis-

played in Figure 7, closely positioned tags of differ-

ent levels may become visually indistinct, especially

when only few tags belong to a certain level.

The current TagSpheres design does not take the

distribution of tags throughout different hierarchy lev-

els into account. In use cases with a steadily increas-

ing or decreasing number of tags per hierarchy level

it gets possible that a considerable proportion of the

color maps’ bandwidth is used for a comparatively

small portion of tags. An assignment of colors tak-

ing the density distribution of the tags’ weights into

account could overcome this issue.

6 CONCLUSION

We introduced TagSpheres that arrange tags on sev-

eral hierarchy levels to transmit the notion of hierar-

chical distance in tag clouds. We accentuate relation-

ships between different hierarchy levels by placing hi-

erarchically related tags closely. Applied within a dig-

ital humanities project, the design of TagSpheres was

evaluated as aesthetic and intuitive, and the humani-

ties scholars emphasized the utility of TagSpheres for

their work. Further usage scenarios in sports and avi-

ation outline the inherence of hierarchical textual in-

formation in various domains and the usefulness of

TagSpheres as they provide an interesting view on this

type of data.

Despite few listed limitations, TagSpheres might

be applicable to a multitude of further research ques-

tions from other areas. Also imaginable is the combi-

nation of TagSpheres and TagPies to support the com-

parative analysis of different textual summaries with

hierarchical information.

ACKNOWLEDGEMENTS

The authors thank Judith Blumenstein for preparing

the digital humanities usage scenario. This research

was funded by the German Federal Ministry of Edu-

cation and Research.

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