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DOI: https://doi.org/10.34069/AI/2023.64.04.10
How to Cite:
Solodovnyk, D., Havryliuk, I., Sypchenko, I., Ishchenko, O., & Kyrylenko, O. (2023). Data journalism visualization in Ukraine and
in Europe: a comparative analysis. Amazonia Investiga, 12(64), 102-116. https://doi.org/10.34069/AI/2023.64.04.10
Data journalism visualization in Ukraine and in Europe:
a comparative analysis
Візуалізація журналістики даних в Україні та Європі: порівняльний аналіз
Received: January 23, 2023 Accepted: March 30, 2023
Written by:
Solodovnyk Dmytro1
https://orcid.org/0000-0002-9951-0312
Havryliuk Inna2
https://orcid.org/0000-0001-7454-3540
Sypchenko Inna3
https://orcid.org/0000-0003-3323-3786
Ishchenko Olena4
https://orcid.org/0000-0001-9274-8566
Kyrylenko Oksana5
https://orcid.org/0000-0001-6385-1441
Abstract
The article aims to investigate the peculiarities of
data visualization in Ukrainian and European
data media, which will help outline trends and
development tendencies in data journalism. The
research results will form a list of promising
methods and forms of data visualization in
journalistic practice. The research used the
method of content analysis of journalistic
materials and comparative analysis of Ukrainian
and European data media. The use of such a
methodological toolkit is due to the visual
component of the analyzed objects and the need
to fill a gap in data journalism research as a
comparative analysis of the Ukrainian and
European practice of visualizing materials in data
media has not been conducted before. The study
results showed common trends in using visual
resources as the most common way of visualizing
data. The European edition uses more types of
visual objects in the same material. It was also
found that the European data media pay more
attention to the text, while the Ukrainian one
focuses on the visual component. Common
trends are observed when analyzing visualization
and interactivity functions. Both editions mostly
use visualization to reinforce and supplement
1
PhD student, Sumy State University, Sumy, Ukraine.
2
Candidate of Science in Social Communications, Associate Professor of the Department of Journalism and Philology, Sumy State
University, Sumy, Ukraine.
3
Candidate of Science in Social Communications, Associate Professor of the Department of Journalism and Philology, Sumy State
University, Sumy, Ukraine.
4
PhD in Philology, Lecturer at the Department of Journalism and Philology, Sumy State University, Sumy, Ukraine.
5
Graduate student of Sumy State University, Sumy State University, Sumy, Ukraine.
Solodovnyk, D., Havryliuk, I., Sypchenko, I., Ishchenko, O., Kyrylenko, O. / Volume 12 - Issue 64: 102-116 / April, 2023
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textual information, and the hypothesis of a
fading trend toward interactivity is confirmed.
Keywords: data visualization, data media, data
journalism, content analysis, comparative
analysis.
Introduction
The development of digital technologies and the
global trend of "datafication" are increasingly
important in journalism. One of the forms of
manifestation of digitization and the
development of open data is data journalism. A
data journalist is engaged not only in collecting
information and creating text but also in the
production of complex text formats that combine
words and numbers with graphic elements. This
can only be done with the help of new software
tools, which means the data journalist performs
the functions of an analyst, a journalist, a
designer, and partly a programmer. Adequate
visualization of information is considered a
fundamental factor in data journalism, as it
affects the quality of content, attracts users, and
allows them to interact with content
(Engebretsen et al., 2018).
Visualization in data journalism develops along
with techniques and technologies. Although the
methods and tools for creating visual elements
are similar worldwide, the practice of using
"visual language" and visual content have their
own characteristics in different countries. Such
differences can be caused by norms and
principles of information submitted in the
country, technical and financial capabilities of
the media resource, and access to open data.
Regarding the last factor, it should be noted that,
according to the research of the public
organization Open Data Watch, the Open Data
Inventory (ODIN) was calculated - the identifier
of the openness of official statistics and open data
for 187 countries of the world for 2020-2021
(Open Data Inventory, 2021). The results of a
comparative analysis with European countries
showed that Ukraine is in 45th place according to
the ODIN indicator, ahead of Great Britain by 20
steps and France by five. Poland, Holland, and
Sweden were identified as unexpected data
quality and availability leaders. Therefore,
Ukraine has good potential in this regard.
However, European data media still have more
opportunities for development due to higher
indicators of transparency of state policy and
availability of statistical information.
Differences in access to open data, methods,
techniques, and visualization styles determine
the need for a comparative data visualization
analysis in Ukrainian and European media. And
although the study of aspects of data
visualization in data journalism has long been a
field of interest of the scientific community, a
comparative analysis of leading European and
Ukrainian media in terms of data visualization in
data media has not been conducted.
The purpose of the study is to identify the
features of visualization of data information in
Ukrainian and European data media, which will
provide an understanding of trends in the
development of visualization of data journalism.
This study can become a road map for finding
new ways and forms of visualization in
journalistic practice. This study seeks to answer
the question: what characterizes the data
visualization processes in European and
domestic editorial offices?
The following sections will first outline the
theoretical approaches of the study (Section 2). A
description of the applied methodology is in
Section 3. Then the results and discussion will be
presented: key features of data visualization in
different countries and their comparative
characteristics (Section 4). The article concludes
with a summary of the main findings and a
consideration of further research perspectives
(Section 5).
Theoretical Framework
Data journalism, becoming a new media
phenomenon, has caused a real boom in scientific
research.
The review of scientific works made it possible
to draw conclusions that three groups of
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scientific research can be distinguished in the
context of the mentioned topic. Some scientists
deal with the conceptual aspects of data
journalism, developing theoretical approaches
and studying the conceptual apparatus of the
principal terms used in the direction mentioned
above, as well as consider the history of the
formation and development of data journalism
(Paraise & Dagiral, 2012; Coddington, 2015;
Howard, 2014; Royal & Blasingame, 2015;
Borges-Rey, 2016; Gray, Bounegru &
Chambers, 2012; Weber & Rall, 2012;
Medvedeva, 2020; Bidzilya & Kravets, 2019
Polyuga, 2019; Hannaford, 2015 ).
The second group of scientific investigations is
aimed at researching journalists' work as data
journalism subjects. Within this direction, the
professional profile of a journalist is considered,
and his "hard" and "soft" skills, methods of
software application, and the need for a team of
analysts and programmers are studied (Tabary et
al., 2016; Appelgren & Nygren, 2014; Royal,
2010; Weber & Rall, 2016; Paraise & Dagiral,
2012; De-Maeyer et al., 2015; Fink & Anderson,
2015; Uskali & Kuutti, 2015; Hermida & Young,
2017).
The third and most significant group of works
analyzes content available in media practice.
Such studies focus on data collection, analysis,
processing, and visualization features. At the
same time, scientists pay special attention to
solving practical problems related to the
typology, quality, quantity, interactivity, and
functionality of visual objects in data journalism
(Knight, 2015; Nguyen, 2017; Loosen et al.,
2020; Hamilton, 2016; Flew et al., 2012; Cohen
et al., 2011; Medvedeva, 2020; Lichenko, 2018).
It is worth noting that the views of scientists on
the definition of the concept of "data journalism"
coincide. For example, H. Hamilton (2016) and
S. Sunne (2016) believe that data journalism is
collecting, cleaning, organizing, analyzing,
visualizing, and publishing data. L. Rinsdorf and
R. Boers (2016) consider data journalism as a
process (analysis, collection, and processing of
information) and a product (the result of which is
journalistic material – text and visualization) at
the same time. So, scientists identify data
visualization in journalism as a critical element
of information design, which allows consumers
to understand the material. Note that "data
journalism" cannot be equated with "data
visualization"; visualization exists as an
independent phenomenon, but data journalism
often uses visualization as a storytelling tool.
"Data Visualization," according to R. Borgo et al.
(2013) and W. Loosen et al., (2020), is a visual
representation of primarily numerical data (but
not only numerical) designed to improve the
cognitive processing of information by
consumers.
Scientists from all over the world actively
research visualization in journalism. E. Burdina
considers abstract thinking to be the key to
visualization, stressing that it precedes analysis,
and therefore, thanks to visual objects,
information is absorbed faster (Burdina, 2016).
V. Shevchenko (2014) offers a classification of
visualization forms, which is a continuation of
the opinion of S. McMillan (2006). Among the
visualization studies in media practice, we should
highlight the work of F. Tandoc and
O. Soo-Kwang (2017) examine the content of
The Guardian media resource. K. Medvedeva
(2020), Yu. Nagorna and N. Poplavska (2022)
consider methods of visualizing television and
print content using the example of local and
national mass media. M. Knight (2015),
analyzing news content, claims that journalists
often use infographics and maps for
visualization. P. Boczkowski (2004),
S. McMillan (2006), A. Rudchenko (2017), and
M. Engebretsen (2006) are supporters of
interactive visualization and consider it a unique
aspect of online communication and an essential
component of digital journalism, as they see it the
potential for active user involvement. At the
same time, other researchers analyzing media
practice followed the trend of decreasing
interactivity (Appelgren, 2017; Stalph, 2017;
Young et al., 2018; Domingo, 2008; Burmester
et al., 2010; Ojo & Heravi, 2018; Tandoc &
Soo-Kwang, 2017).
Despite a large number of studies on information
visualization in general, the visualization of
objects in data journalism is devoted to a small
number of scientific works. The visualization
studies in the data journalism system presented in
the scientific media discourse relate to analyzing
the winners and prize-winners of the Data
Journalism Awards. Such intelligence shows that
winners will likely use static graphics, maps, and
images (Loosen et al., 2020; Ojo & y Heravi,
2018). F. Stalph (2017) suggests that bar charts,
line graphs, and maps are appropriate for daily
news, but award-winning journalism differs from
daily news with interactivity and animation.
A. Córdoba-Cabús and M. García-Borrego
(2020), analyzing the finalists and winners of the
Data Journalism Awards 2019, found that the
most popular visualization method among the
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winners was infographics, with non-interactive
and non-animated visual objects winning.
Therefore, although the field of data journalism
research is growing, and the study of visual
objects in journalistic materials remains a trend,
there is a lack of research in the scientific
discourse that analyzes in detail the media
practices of visualization in data media. This
study aims to fill the existing gap. This research
is significant in the context of comparing media
practices of Ukrainian and European data media,
which will make it possible to determine trends
in information visualization of data journalism.
Methodology
The proposed research is comparative.
Comparative analysis as a method is used to
obtain new information about the similar and
different features and properties of the studied
objects; besides, as noted by M. Dogan and
D. Pelassi (Klass, 1985), a comparative analysis
is one of the most fruitful directions of thinking,
because it allows revealing implicit relationships
and general trends of phenomena. It is worth
noting that this method is actively used in
journalism. For example, to identify
"tabloidization" trends in the USA, Germany,
and Great Britain (Esser, 1999); search for
practical journalistic innovations based on media
practices of five European countries (Meier et al.,
2022); analysis of educational strategies in data
journalism based on a comparison of educational
programs of six European countries (Splendore
et al., 2016), etc.
Also, content analysis is one of the main methods
used in the research. Content analysis is actively
used as a methodological toolkit in various areas
of journalism, in particular in data journalism. If
the previous content analysis was considered a
purely computational technique or a secondary
method based only on computational operations,
today it is regarded as an independent qualitative-
quantitative method, which can be used to carry
out the entire cycle of document research from
the selection of research units to the
interpretation of results (Kostenko & Ivanov,
2003). Thus, content analysis was applied by
scientists to analyze the visualization objects of
the winners and nominees of the Data Journalism
Awards (Córdoba-Cabús & García-Borrego,
2020, Ojo & Heravi, 2018, Córdoba-Cabús,
2020). The authors note that this method is
appropriate for researching trends in data
journalism.
Two Internet media that position themselves as
news portals of data journalism were chosen for
analysis. Texty.org.ua is an independent
Ukrainian online journal founded in 2010 as a
data journalism project that works in traditional
journalistic genres - from lengthy reports to short
messages. This media is Ukraine's most
significant data journalism project and has
nominations and awards in the Data Journalism
Awards-2017, 2016, and 2012. The European
journal chosen for analysis is the European Data
Journalism Network (created in 2017). It is a
network of independent media organizations and
data newsrooms that generate and promote data-
driven coverage of European topics in multiple
languages. This online media covers the entire
news media network in data journalism in
Europe, so it is a worthy representative for the
analysis.
The representative sample consisted of 100
journalistic materials, 50 belonging to Ukrainian
and 50 to European media. A total of 200 visual
objects in 100 publications were analyzed.
The following characteristics were taken for
analysis: the number, typology, and functions of
visual objects, and the visualization ratio. It
should be noted that the article proposes an
updated and modified methodology of
A. Córdoba-Cabús, M. García-Borrego (2020)
and F. Stalph (2017). The main stages of the
research are presented in Figure 1.
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Fig. 1. Visualization research process in Ukrainian and European data media
Source: compiled by the author
Types of visualizations.
C. Salvatierra (2008), A. Córdoba-Cabús and
M. García-Borrego (2020) classifying visual
objects, propose to distinguish tables and lists,
static graphs, interactive graphs, maps, visual
resources (photos, videos, illustrations),
animations, infographics, and others. In our
opinion, this classification deserves attention.
However, we believe that it is inappropriate to
distinguish static and interactive graphs as
separate types. Suppose we differentiate between
interactive and static graphs; a similar division
must be made in lists, maps, and infographics.
We also disagree that illustrations should be
classified as visual resources; for example,
according to the classification of V. Shevchenko
(2014), illustrations, cartoons, and comics are
combined into one type, and photos and video
resources into another type of visual objects.
We offer a comparative analysis of the following
visualization types:
− Tables or lists. This type covers visual
objects presented as a table or lists - both
interactive and static. It should be noted that
in the presence of other elements, apart from
the components of the table or list (text not
included in the table, icon, pictures, etc.),
such a visual object is identified as an
infographic.
− Graphs. Graphs can be interactive and static,
with legend and axis names. Graphs include
dots, pie, bars, and other charts (we do not
identify the type of graph during the
research).
− Maps. Such objects should provide a
geographical representation of information;
they can be interactive and static. The maps
may have a legend and name.
− Visual resources. This type of visualization
includes photos and videos. We also suggest
considering stock images as a visual
resource but not identify them as an
illustration.
− Animation. This type of visualization offers
the change of the object without the reader's
participation; that is, the image changes
automatically, distinguishing animation
from interactivity.
− Infographics. This type of visualization
offers a combination of several types of
visual objects, for example, a variety of a
graph, an illustration, and a table.
− Illustration. Images have an artistic
component and the vision of the author of
the news or event. Such objects are
developed directly for the story, unlike
visual assets.
− There are no visualizations.
− Other.
Functions of visualizations.
We distinguish whether the visualization was
part of the journalistic material, whether it
performed a complementary and accompanying
function, or whether the material was included in
the visual object (the visualization is structured
as a story). We also pay attention to the ratio of
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text and visual objects: what prevails and
whether there are publications with only visual
objects or only text in the analyzed journalism
materials (Stalph, 2017; Córdoba-Cabús &
García-Borrego, 2020).
Interactivity of visualizations.
Interactivity means the ability of the reader to
interact with the content. Among the functions of
interactivity, based on the analysis of previous
studies (Segel & Heer, 2010; Córdoba-Cabús &
García-Borrego, 2020), we singled out the
following: exploring (possibility to obtain
details); selecting (the ability to select the
required year, region or hyperlink to obtain
information); filtering (helps to find the
necessary information to the reader among the
entire array of data); narrative (by clicking in the
required place, you can continue viewing the
publication materials); interaction with games
(the reader is invited to play a game, calculate
something, guess or remember, take a test or test
his memory); personalization (to receive specific
information, you are asked to enter your data, for
example, height, weight, age); others that cannot
be attributed to the previous ones.
Results and Discussion
A total of 100 data journalism materials covering
200 visualization objects were analyzed.
After studying them, we calculated the
visualization index - how the number of visual
objects correlates with the total number of
journalistic materials. It was found that the
publications contained an average of two visual
objects of different types (M=2), and for
Ukrainian media, this indicator was lower - M1
= 1.66 than for European media - M2 = 2.34. So,
Texty.org.ua mainly uses one type of
visualization for one material, while the
European Data Journalism network prefers
several types of visualization. We found only one
project that does not contain visualization; the
news is presented in text format (this material
belongs to the editors of Texty.org.ua).
Types of visualizations.
To determine the most common types of visual
objects in Ukrainian and European online
publications that present data journalism and to
identify general trends, we suggest considering
the percentage ratio of visualization on their data
portals and total statistics for both media. The
structural analysis of visualization is presented in
Table 1.
Therefore, the most common type of
visualization for both data media is visual
resources, i.e., photos, stock images, and videos.
They occupy 37.35% and 37.61% in Ukrainian
and European online media, respectively.
Interestingly, the values are quite close; that is,
there is a general trend toward using visual
resources by editorial offices as the simplest,
fastest, and least expensive in terms of the
workforce of visual objects. It should be noted
that when we are talking about a photo, we do not
mean a single photo, it can be a series or a
carousel of images, but the type of visualization
is the same.
Table 1.
Structural analysis of visualization types
Type of visualization
Texty.org.ua
Europeandatajournalism.eu
TOTAL
Tables or lists
2.41%
3.42%
3%
Graphics
19.27%
32.47%
27%
Maps
14.46%
12.82%
13.5%
Visual resources
37.35%
37.61%
37.5%
Animation
3.62%
1.71%
2.5%
Infographics
13.25%
10.26%
11.5%
Illustration
4.82%
1.71%
3%
There are no visualizations
1.21%
-
0.5%
Other
3.61%
-
1.5%
Source: Calculated by the author
Also, editorial offices often use graphs.
Texty.org.ua graphs account for 19.27% of the
total set of visual objects; for the European Data
Journalism Network, this share is 32.47% (table
1). Most often, the graphs trace the change of the
phenomenon over time or the transformation of
the indicator depending on the geographical
location (city, country, continent). As you can
see, European data media use graphs more often
than Ukrainian media, and the frequency of their
use is close to visual resources. However, it is
worth noting that the charts from Texty.org.ua
are more complex, visually more creative. In
figure 2 a, b, reviewing the graphs created by the
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Ukrainian and European editors, respectively, is
suggested. As we can see, for the European data
space, the trend is to make quick and simple
graphs, while for the Ukrainian media, the
quality of the graph's presentation is essential.
Graphics also take second place in the general
structure of types of visual objects - 27%.
а) Texty.org.ua
b) European Data Journalism network
Fig. 2. Examples of graphs in journalistic materials of analyzed data media
Source: Drozdova et al., 2022a; Morphonios, 2022.
Interactive and static maps occupy third place in
the structure of visual objects in analyzed media
(Table 1). This type of visualization is more
common in Ukrainian data media (14.46%),
which can be explained by the specificity of the
content. Since there is a war in the country, the
maps often depict the territory of Ukraine, where
the centers of hostilities and the direction of the
front line are marked. An example is the
publication "Under attack. What and when
Russia shelled in Ukraine", "Open data at war.
How many are published in cities and why is it
important", "From where and how is Russia
shelling Mykolaiv", etc.
About the same share as maps are occupied by
infographics (Table 1). This type of visualization
is more complicated than the previous ones, as it
requires a journalist to analyze carefully, to be
able to interpret information effectively, and to
have abstract thinking. It should be noted that
Texty.org.ua uses infographics more often
(13.25%) than the European Data Journalism
network (10.26%).
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Other visualization types occupy less than 5% of
the total structure. However, the use of
illustrations is more typical for the Ukrainian
media. In contrast, tables and lists are more
typical for the European media.
Functions of visual elements.
Visualization is a characteristic, but optional, of
data journalism; its role depends on the type of
information and emphasis the journalist wants to
draw the reader's attention to.
In the analyzed sample, only one journalistic
piece consisted only of visualization, and only
one consisted exclusively of text. Both projects
belong to the editors of Texty.org.ua. If
everything is evident with the text, we suggest
you review how the media created a story
exclusively from visual objects. By clicking on
the link with the title of the publication "War
video. Chronology", we get to a page presented
as a calendar, in which, by selecting the
appropriate region and day, you can view videos
shot by eyewitnesses and military personnel of
that day (Fig. 3).
Fig. 3. Texty.org.ua material that uses only visualization
Source: Oksymets et al., 2022.
In general, the publications of the European Data
Journalism network are dominated by text
(46%); in the second place – are publications in
which the visual and text parts are balanced
(36%); in the third place – journalistic materials
in which the visualization predominate (18%).
The editors focus more on working with the text
than designing and visualizing. In Texty.org.ua
articles, the situation is different; the distribution
according to the analyzed characteristics is
uniform (visualization predominates - 32%, text
dominates - 30%, balance - 34%) (Fig. 4).
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Fig. 4. Structure of the analyzed journalistic materials in the European and Ukrainian data media
Source: Calculated by the author
Figure 5 presents the functions performed by
visualization in the journalistic materials of the
analyzed media – whether the visualization
complements and expands or whether the
visualization is built as a separate material. As we
can see, for both media, the most frequent is the
use of visual objects to supplement the material,
and the method in which the visualization is built
as a story is not so popular and makes up 14% of
the total structure for Texty.org.ua and 2% for
European Data Journalism network.
Fig. 5. Functions performed by visualization in materials
Source: Calculated by the author
Interactivity.
199 visual objects were examined for
interactivity (one of the analyzed publications
does not contain visual objects). The results
proved the superiority of static visual objects
over interactive ones. For Texty.org.ua, the ratio
of static and interactive objects is 66.2% to
33.8%, i.e., static ones prevail almost twice. The
indicators for the European Data Journalism
network are similar - 62.4% to 37.6%. Therefore,
the results of the analysis of interactivity confirm
the conclusions of E. Appelgren (2017), F. Stalph
(2017), and M. Young et al. (2018) regarding the
general trend of decreasing interactivity in
journalistic materials.
In Figure 6, it is proposed to consider the
functions that perform interactivity. Thus, 44
interactive European data-media objects and 28
Ukrainian ones were analyzed. As can be seen
from Figure 6, Texty.org.ua uses a broader range
30%
32%
34%
2%
2%
46%
18%
36%
0
0
The text predominates
Visualisations predominate
Balanced
Only text
Only visualisations
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
europeandatajournalism.eu texty.org.ua
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
texty.org.ua europeandatajournalism.eu
The visualisation is part of the story The visualisation is structured as a story
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of interactivity functions than the European Data
Journalism network. Ukrainian media almost
does not use filtering; the editorial office often
uses selection to demonstrate important
information. The selection is similarly the most
common feature for the European Data
Journalism Network, while narrating,
personalization, and interaction with games are
not used at all.
Fig. 6. Interactivity functions
Source: Calculated by the author
Consider innovative features such as interaction
with games and personalization for a qualitative
analysis of interactivity features. Texty.org.ua
actively implements new technologies and
trends, using games and personalization. In
Figure 7, we offer an example of an interactive
game. In the story "Rising prices: The State
Statistics Service does not lie. But check how
your expenses have increased," the reader needs
to remember or guess the cost of a particular
product a year ago to check the level of inflation
in the country. Figure 8 demonstrates the use of
personalization by the Ukrainian media; the
reader can compile his consumer basket and
calculate its cost depending on the time change.
For example, in a similar story of the European
Data Journalism network about inflation, "How
are EU countries doing against inflation?" graphs
with an interactive exploring function are
exclusively used (Aude, 2022).
Fig. 7. A fragment of Texty.org.ua material that uses interaction with games
Source: Drozdova et al., 2022b.
0,00%
10,00%
20,00%
30,00%
40,00%
50,00%
60,00%
70,00%
80,00%
90,00%
texty.org.ua europeandatajournalism.eu
Exploring Selecting Filtering Narrating Interacting with games Personalising Other
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Fig. 8. Fragment of Texty.org.ua material that uses personalization
Source: Drozdova et al., 2022b.
So, the quantitative and qualitative comparative
analysis of interactivity functions demonstrates
that the European data media is focused on a
clear, rather "dry" presentation of information
and does not fully apply the tools of attracting
consumers but uses interactivity only to expand
or supplement data. Instead, Ukrainian online
media demonstrates a creative approach, offering
the consumer to actively interact with the content
and stay on the page as long as possible.
Conclusions
The active development of digital technologies,
the saturation of information containing large
volumes of data, and the openness of data
characterize the modern world. Mass media play
a significant role in the dissemination of large
data flows. It is especially true for media
specializing in data journalism.
Visualization is a component and the most
crucial element in the system of texts belonging
to data journalism. Visualization helps to
transmit large amounts of data in a compressed
form to interest the recipient. Although the media
use the same tools to create visual content, the
spectrum of visual elements differs.
Convincing in this regard is the experience of
using visual language in the media, presenting
the media culture of different countries.
Therefore, the study's main goal was to
determine the visualization features of data
journalism in Ukrainian and European media
resources to identify common and distinctive
features in this segment. The news portals of
journalism Texty.org.ua and the European Data
Journalism Network were taken for the analysis.
A literature review revealed that the problem
raised needs to be studied more. Most of the
works focus on analyzing the visual content of
well-known newsrooms or exclusively Data
Journalism Awards nominees.
The leading research methods were content
analysis and comparative analysis.
A review of the above publications showed that
Ukrainian and European data journalism is
developing following global trends.
Data media use an average of 2 types of visual
objects in one material. As you can see, the
editors try not to overload the material with
visual objects. However, European media still
use more visual objects in one material than
Ukrainian media.
The most common form of visualization for both
editions is visual resources (photos and videos).
Since information changes extremely quickly,
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this requires rapid publication of information
from newsrooms in the online environment. That
is why the trend is to use visual resources since
they do not require additional time and resources
from the journalist, unlike infographics, maps,
and diagrams.
Despite the predominance of visual resources in
data media, maps, graphs, and infographics
occupy an important place in the structure of
used visual objects. It should be noted that
Ukrainian media uses maps and infographics
more often, and tables are scarce.
A comparative analysis of the structure of the
materials revealed that the editors of European
media pay more attention to work with text,
while in Ukrainian data, media journalists focus
on design and visualization; they try to put more
information in a visual object. Analysis of
visualization functions showed a general trend
towards using visual objects to reinforce and
supplement textual information, and the
construction of visualization as a story is instead
an exception.
As for interactive visual elements, we are
observing a decline in their use. At the same time,
the editors do not abandon this characteristic at
all but make different accents. Thus, the
European Data Journalism network uses two
functions of interactivity in journalistic materials
- supplementing and expanding information.
Texty.org.ua uses a creative approach to
interactivity, saturating and coloring its materials
with games and personalization. Considering the
analysis, predicting a further decrease in
interactivity is possible. It will concern either
essential interactive functions (choice, research)
or the creation of complex interactive projects
within the plot (games, personalization).
So, the results indicate two distinct points:
− a different structure of materials: European
media focuses on text, Ukrainian
emphasizes visualization;
− the media use interactivity differently:
Texty.org.ua tries to involve all functions,
while the European Data Journalism
network uses only 2.
Despite the changing nature of visualization,
specific trends can be useful for implementing
projects in the digital space. In particular, these
results can serve as change indicators for future
research and allow practitioners to adapt content
according to general trends. In our opinion, the
European Data Journalism network should pay
attention to the quality of visual objects because
they are often uninteresting and monotonous; as
the practice of Texty.org.ua shows, numbers can
look stylish and attractive.
For further research, it would be interesting to
transfer this study to daily materials in popular
global media to test how data visualization is
adapted in the environment of media giants.
Bibliographic references
Appelgren, E. (2017). An Illusion of
Interactivity: The Paternalistic Side of Data
Journalism. Journalism Practice, 12(3),
308-325.
https://doi.org/10.1080/17512786.2017.1299
032
Appelgren, E., Nygren, G. (2014). Data
Journalism in Sweden. Introducing new
methods and genres of journalism into ‘old’
organizations. Digital Journalism, 2(3),
394-405.
https://doi.org/10.1080/21670811.2014.8843
44
Aude, M. (2022). How are EU countries doing
against inflation? European Data Journalism
network. Retrieved from
https://www.europeandatajournalism.eu/eng/
News/Data-news/How-are-EU-countries-
doing-against-inflation
Bidzilya, Yu., & Kravets, D. (2019).
Development of open data as new
perspectives and opportunities for modern
Ukrainian journalism. Collection of scientific
works of the Research Institute of Press
Studies, 9 (27), 232-245.
https://doi.org/10.37222/2524-0331-2019-
9(27)-13.
Boczkowski, P. (2004). The Processes of
Adopting Multimedia and Interactivity in
Three Online Newsrooms. Journal of
Communication, 54(2), 197-213.
Borges-Rey, E. (2016). Unravelling Data
Journalism. Journalism Practice, 10(7),
833-843.
https://doi.org/10.1080/17512786.2016.1159
921
Borgo, R., Kehrer, J., Chung, D., Maguire, E.,
Laramee, R., Hauser, H., Ward, M., &
Chen, M. (2013). Glyph-Based Visualization:
Foundations, Design Guidelines, Techniques
and Applications. Eurographics State of the
Art Reports. Retrieved from
https://core.ac.uk/download/pdf/78856764.p
df
Burdina, E. (2016). Infographics on television:
the impact of infotainment on the
visualization of facts. State and regions, 3,
114
www.amazoniainvestiga.info ISSN 2322- 6307
75-80. Retrieved from
http://nbuv.gov.ua/UJRN/drsk_2016_3_14
Burmester, M., Mast, M., Tille, R., & Weber, W.
(2010). How Users Perceive and Use
Interactive Information Graphics: An
Exploratory Study [Paper]. In Banissi, Ebad
(ed.) Proceedings. 4th International
Conference Information Visualization (IV
2010), London, 26-29 July, 2010.
Coddington, M. (2015). Clarifying Journalism’s
Quantitative Turn. Digital Journalism, 3(3),
331-348.
https://doi.org/10.1080/21670811.2014.9764
00
Cohen, S., Hamilton, J., & Turner, F. (2011).
Computational Journalism. Communications
of the ACM, 54(10), 66-71.
https://doi.org/10.1145/2001269.2001288
Córdoba-Cabús, A. (2020). Estándares de
calidad en el periodismo de datos: fuentes,
narrativas y visualizaciones en los Data
Journalism Awards 2019. El Profesional de la
información, 29(3).
https://doi.org/10.3145/epi.2020.may.28
Córdoba-Cabús, A., & García-Borrego, M.
(2020). Visualisations as a critical
information source for data journalism.
Analysis of the typology, interactivity, and
functions in the 2019 Data Journalism
Awards. Doxa Comunicación, 31, 87-105.
De-Maeyer, J., Libert, M., Domingo, D.,
Heinderyckx, F., & Le-Cam, F. (2015).
Waiting for Data Journalism. Digital
Journalism, 3(3), 432-446.
https://doi.org/10.1080/21670811.2014.9764
15
Domingo, D. (2008). Interactivity in the Daily
Routines of Online Newsrooms: Dealing with
an Uncomfortable Myth. Journal of
Computer-Mediated Communication, 13(3),
680-704
Drozdova, E., Kelm, N., Gerasemchuk, K., &
Nabozhniak, O. (2022a). Fire shaft. How and
which projectiles arrive at ukrainian
positions. Texty.org.ua. Retrieved from
https://texty.org.ua/projects/107013/vohnyan
yj-val-yak-i-yaki-snaryady-prylitayut-na-
ukrayinski-pozyciyi/.
Drozdova, E., Kelm, N., Gerasimenko, V., &
Bondarenko, A. (2022b). Rising prices: The
State Statistics Service does not lie. But check
how your expenses have increased.
Texty.org.ua. Retrieved from
https://texty.org.ua/projects/105550/infliatio
n/.
Engebretsen, M. (2006). Shallow and Static or
Deep and Dynamic? Studying the State of
Online Journalism in Scandinavia. Nordicom
Review, 27(1), 3-16.
Engebretsen, M., Kennedy, H., & Weber, W.
(2018). Data visualization in Scandinavian
newsrooms: emerging trends in journalistic
visualization practices. Nordicom Review,
39(2), 1-16. https://doi.org/10.21256/zhaw-
3505
Esser, F. (1999). “Tabloidization” of News: A
Comparative Analysis of Anglo-American
and German Press Journalism. European
Journal of Communication, 14(3), 291-324.
https://doi.org/10.1177/02673231990140030
01
Fink, K., & Anderson, C. (2015). Data
Journalism in the United States. Journalism
Studies, 16(4), 467-481.
https://doi.org/10.1080/1461670X.2014.939
852
Flew, T., Spurgeon, C., Daniel, A., & Swift, A.
(2012) The Promise of Computational
Journalism. Journalism Practice, 6(2),
157-171.
https://doi.org/10.1080/17512786.2011.6166
55
Gray, J., Bounegru, L., & Chambers, L. (2012).
The Data Journalism Handbook. Sebastopol,
Ucrania: O’Reilly Media.
Hamilton, J. (2016). Democracy’s detectives:
The economics of investigative journalism.
Cambridge, MA: Harvard University Press.
Hannaford, L. (2015). Computational Journalism
in the UK Newsroom: Hybrids or Specialists?
Journalism Education, 4(1), 6-21.
Hermida, A., & Young, M. (2017). Finding the
Data Unicorn. A hierarchy of hybridity in
data and computational journalism. Digital
Journalism, 5(2), 159-176.
https://doi.org/10.1080/21670811.2016.1162
663
Howard, A. (2014). The art and science of data-
driven journalism. New York, Estados
Unidos: Tow Center for Digital Journalism.
https://doi.org/10.7916/D8Q531V1
Klass, G. (1985). How to Compare Nations:
Strategies in Comparative Politics. By Mattei
Dogan and Dominique Pelassy. (Chatham,
N.J.: Chatham House Publishers, 1984).
American Political Science Review, 79(1),
243-244. doi:
https://doi.org/10.2307/1956182
Knight, M. (2015). Data journalism in the UK: a
preliminary analysis of form and content.
Journal of Media Practice, 16(1), 55-72.
https://doi.org/10.1080/14682753.2015.1015
801
Kostenko, N., & Ivanov, V. (2003). Content
analysis experience: Models and practices:
Monograph. Free Press Center, 200 p. (In
Ukranian)
Volume 12 - Issue 64
/ April 2023
115
http:// www.amazoniainvestiga.info ISSN 2322- 6307
Lichenko, A. (2018). Interpretation of the term
"data journalism" in foreign media studies: a
comparative analysis. Media space: problems
and challenges of today: materials of the All-
Ukrainian scientific and practical conference
of students and young scientists (Kyiv, April
25, 2018). Institute of Journalism, 75-79.
Retrieved from
https://er.nau.edu.ua/bitstream/NAU/41209/
1/Lichenko_tezy.pdf.
Lichenko, A. (2019). Interactive visualization as
a way of representing a large amount of data
in the media (on the example of the
Guardian). Visnyk of the Lviv University.
Series Journalism, 45, 230-237.
http://dx.doi.org/10.30970/vjo.2019.45.1000
5
Loosen, W., Reimer, J., & De Silva-Schmidt, F.
(2020). Data-driven reporting: An on-going
(r)evolution? An analysis of projects
nominated for the Data Journalism Awards
2013–2016. Journalism, 21(9), 1246–1263.
https://doi.org/10.1177/1464884917735691
McMillan, S. (2006). Exploring Models of
Interactivity from Multiple Research
Traditions: Users, Documents and Systems.
The Handbook of New Media. London,
Thousand Oaks, New Dehli: SAGE, 205-229.
Medvedeva, K. (2020). Data journalism: the role
of visualization in the recipient's perception
of data. National Aviation University, Kyiv,
77 p. (In Ukranian)
Meier, K., Schützeneder, J., García Avilés, J. A.,
Valero-Pastor, J. M., Kaltenbrunner, A.,
Lugschitz, R., ... & Saner, M. (2022).
Examining the Most Relevant Journalism
Innovations: A Comparative Analysis of Five
European Countries from 2010 to 2020.
Journalism and Media, 3(4), 698-714.
Retrieved from
http://dx.doi.org/10.3390/journalmedia3040
046
Morphonios, N. (2022). Greek courts abuse pre-
trial detention. European Data Journalism
network. Retrieved from
https://www.europeandatajournalism.eu/eng/
News/Data-news/Greek-courts-abuse-pre-
trial-detention.
Nagorna, Yu., & Poplavska, N. (2022). Data
visualization in the news shows on the
Ternopil tv channels. Grail of science, 11,
309-313. https://doi.org/10.36074/grail-of-
science.24.12.2021.050.
Nguyen, A. (2017). News, numbers and public
opinion in a data-driven world. New York,
NY: Bloomsbury Academic.
Ojo, A., & Heravi, B. (2018). Patterns in Award-
Winning Data Storytelling. Digital
Journalism, 6(6), 693-718. https://doi.org
/10.1080/21670811.2017.1403291
Oksymets, O., Gerasimenko, V., & Drozdova, E.
(2022). War video. Chronology.
Texty.org.ua. Retrieved from
https://texty.org.ua/d/2022/war_video/en/ind
ex.html?
Open Data Inventory. (2021). Open Data Watch.
Retrieved from
https://odin.opendatawatch.com/
Paraise, S., & Darigal, E. (2012). Data-driven
journalism and the public good: “Computer
assisted-reporters” and “programmer-
journalists” in Chicago. New media &
society, 15(6), 853-871.
https://doi.org/10.1177/1461444812463345
Polyuga, V. (2019). Big data technology (Big
Data): main characteristics and prospects for
use in journalism. Dialogue: Media studios,
25, 144-154. https://doi.org/10.18524/2308-
3255.2019.25.195599
Rinsdorf, L., & Boers, R. (2016). The need to
reflect. Data journalism as an aspect of
disrupted practice in digital journalism and in
journalism education. In Proceedings of the
Roundtable Conference of the International
Association of Statistics Education (IASE),
Berlin, July 2016. Retrieved from http://iase-
web.org/documents/papers/rt2016/Rinsdorf.
pdf
Royal, C. (2010). The journalist as programmer:
A case study of The New York Times
interactive news technology department.
Paper presented at the International
Symposium on Online Journalism, Austin,
Texas. Retrieved from
https://online.journalism.utexas.edu/2010/pa
pers/Royal10-1.pdf
Royal, C., & Blasingame, D. (2015). Data
journalism: An explication. International
Symposium on online journalism, 5(1), 24-
46. Retrieved from https://bit.ly/34lyrIS
Rudchenko, A. (2017). Infographics and
dynamic visualization as a trend of modern
media. Crossmedia: content, technologies,
perspectives: monography. Kyiv: Institute of
Journalism of Taras Shevchenko Kyiv
National University, 75-83. Retrieved from
https://r.donnu.edu.ua/handle/123456789/15
59
Salvatierra, C. (2008). Análisis de la
composición gráfica de infografías
periodísticas impresas, de las secciones
‘Internacionales’ y ‘Policiales’ del diario
Clarín en el periodo de tiempo comprendido
entre 1960 y 2008 (Trabajo Fin de Grado).
Universidad Abierta Interamericana, Buenos
Aires. Retrieved from https://bit.ly/3cKy8uG
116
www.amazoniainvestiga.info ISSN 2322- 6307
Segel, E., & Heer, J. (2010). Narrative
visualization: Telling Stories with Data.
IEEE Transactions on Visualization and
Computer Graphics, 16(6), 1139-1148.
https://doi.org/10.1109/TVCG.2010.179
Shevchenko, V. (2014). Visual content as a trend
of modern journalism. Mediascope, 4.
Retrieved from
http://www.mediascope.ru/1654
Splendore, S., Di Salvo, P., Eberwein, T.,
Groenhart, H., Kus, M., & Porlezza, C.
(2016). Educational strategies in data
journalism: A comparative study of six
European countries. Journalism, 17(1),
138-152.
https://doi.org/10.1177/1464884915612683
Stalph, F. (2017). Classifying Data Journalism.
Journalism Practice, 12(1), 1332-1350.
https://doi.org/10.1080/17512786.2017.1386
583
Sunne, S. (2016). How data journalism is
different from what we’ve always done.
American press Institute. Retrieved from
https://www.americanpressinstitute.org/publi
cations/reports/strategy-studies/how-data-
journalism-is-different/
Tabary, C., Provost, A., & Trottier, A. (2016).
Data journalism’s actors, practices, and
skills: A case study from Quebec. Journalism,
17(1), 66-84.
https://doi.org/10.1177/1464884915593245
Tandoc, E., & Soo-Kwang, O. (2017). Small
Departures, Big Continuities? Journalism
Studies, 18(8), 997-1015.
https://doi.org/10.1080/1461670X.2015.110
4260
Uskali, T., & Kuutti, H. (2015). Models and
Stream of Data Journalism. The Journal of
Media Innovations, 2(1), 77-88.
http://dx.doi.org/10.5617/jmi.v2i1.882
Weber, W., & Rall, H.-M. (2016). Design and
journalism. Challenges and opportunities: A
dialogue between two cultures. In W.W.
Wong, Y. Kikuchi, & T.S. Lin (Eds.), Making
Trans / National Contemporary Design
History. 10th Conference of the International
Committee for Design History & Design
Studies (ICDHS 2016) (pp. 313-318). Sao
Paulo: Blucher.
Weber, W., & Rall, H.-M. (2012). Data
visualization in online journalism and its
implications for the production process. In E.
Banissi et al. (Eds.,) Information
Visualisation. Proceedings of the 16th
International Conference on Information
Visualisation (pp. 349–356). Montpellier,
France: IEEE Computer Society. DOI
10.1109/IV.2012.65
Young, M., Hermida, A., & Fulda, J. (2017).
What Makes for Great Journalism? A Content
Analysis of Data Journalism Awards Finalists
2012-2015. Journalism Practice, 12(1),
115-135.
https://doi.org/10.1080/17512786.2016.1270
171
Zayko, L. (2021). Training of specialists in
specialty 061 "Journalism": modern
challenges. Scientifiс and pedagogic
internship Innovative methods of the
organization of the education process for the
specialties "Journalism" and "Management of
Socio-Cultural Activity" in Ukraine and EU
countries": Internship proceedings,
Wloclawek, 17–20.