What ‘Work’ Can Dataviz Do in Popular Science Communication?
Martin Engebretsen
a
Department of Nordic and Media Studies, University of Agder, Universitetsveien 25, Kristiansand, Norway
Keywords: Data Visualization, Popular Science Communication, Social Semiotics, Discourse Analysis, Design Practices
Abstract: Data visualizations have proliferated on public arenas for information and communication in journalism,
PR and governmental information as well as in popular science communication (PSC). In the existing
literature on PSC, simplicity, relevance and trust are identified as critical factors for the communication to
succeed. This position paper argue that data visualizations represent a semiotic resource with unique potentials
regarding all of these criteria. The paper aims at presenting a theoretical and methodological framework for
studying data visualizations applied in popular science discourses. The main goals are a) to introduce social
semiotics as an advanced analytical tool for the scrutiny of data visualizations, b) to introduce PUS (Public
understanding of science) as a field relevant for empirical studies of data visualization, and c) to present a
method of analysis combining a small scale corpus analysis with multimodal close reading of selected
visualizations.
1 INTRODUCTION
Data visualization (DV) represents a form of visual
communication well integrated in scientific
discourses. In disciplines where quantitative data play
a central role particularly in the natural and social
sciences graphs, charts and maps represent an
irreplaceable semiotic resource for revealing patterns
and relations in large amount of data (Tufte, 2001;
Few, 2012). In the professional scientific discourses,
the codes and conventions related to the production
and interpretation of DVs are strong, and normally
shared by the producer and the reader. In public
dissemination of science, on the other hand, both
verbal and visual forms of communication need to be
adapted to the needs of non-scientists, who share
neither verbal terminologies with the scientists, nor
their forms of visual-numeric literacy. In this paper
we ask: What role do DVs play in popular science
communication (PSC)? What are their semiotic and
social functions in the discourse? And how do they
apply to the norms and expectations of the genre in
which they are embedded?
a
https://orcid.org/0000-0001-8002-1423
1
See: https://www.malofiejgraphics.com/;
https://datajournalismawards.org/ and
https://www.informationisbeautifulawards.com/
During the latest decades, data visualizations
have proliferated on public arenas for information and
communication in journalism, PR, governmental
information etc. (cf. Cairo, 2013; Kennedy et al.,
2016a and b; Engebretsen, 2017). This development
is driven by, among other factors, a growing access to
data from a variety of sources and a rapid
development of cheap and easy-to-use visualization
tools (Engebretsen et al., 2018). However, on these
arenas, DVs meet a heterogenous group of readers,
representing big variations concerning visual and
numeric literacy (D’Ignazio & Klein, 2016; Pinney,
Forthcoming). Advanced DVs, with complex visual
codes and high density of variables and values, face a
high risk of being misunderstood or neglected by
members of the general public. On the other hand, a
creative, surprising and visually attractive DV may
receive a lot of attention and stimulate the motivation
for further reading (cf. Cairo, 2016; Allen, 2018).
What is regarded as best practices of data
visualization in the public, is celebrated in awards like
the Malofiej Award, the Data Journalism Award, and
the Kantar Information is Beautiful Award
1
.
260
Engebretsen, M.
What ‘Work’ Can Dataviz Do in Popular Science Communication?.
DOI: 10.5220/0009094002600264
In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 3: IVAPP, pages
260-264
ISBN: 978-989-758-402-2; ISSN: 2184-4321
Copyright
c
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
In the existing literature on public understanding
of science (PUS), certain critical factors are identified
regarding popular science communication. These
values relate to simplicity, relevance and trust (cf.
Bauer, 2009). Complex scientific problems and
findings need to be simplified, in order to be
understood by non-scientists. Further, these findings
need to be contextualized and related to issues
important to the general reader, in order to gain
attention and emotional engagement. And, finally, the
chosen forms of communication need to evoke trust,
both in the communicator – be it a news organization
or a professional mediator – and in the scientific
source behind the popularized presentation. The
argument underlying this paper, is that data
visualizations represent a text type with particular
affordances regarding both simplicity, relevance and
trust. Simplification of complexity follows with the
reductional nature of DVs, being the result of a
number of choices made by the producer through the
pipeline of production. Relevance and emotional
engagement can be evoked by the message carried by
the data itself, for instance when a chart shows the
development of criminal incidents in a specific urban
area. It can also be evoked by the contextualization
provided by the verbal co-text, or by the interactive
options offered in certain digital contexts. Finally;
trust can be built through transparency regarding the
production process; opening the “black box”. Such
transparency can be achieved by informing broadly
about the scientific sources and the data sources
behind the DV, the methods used in the production of
it, as well as the institution and the persons behind the
popular version (Engebretsen, 2017).
This position paper aims at presenting a
theoretical and methodological framework for
studying DVs in popular science discourses. The
main goals are a) to introduce social semiotics as an
advanced analytical tool for the scrutiny of data
visualization as a multimodal text type situated in
particular social practices, b) to introduce PUS
(public understanding of science) as a field relevant
for empirical, discourse oriented studies of data
visualization, and c) to present a method of analysis
combining a small scale corpus analysis with
multimodal close reading of selected DVs. The
results of the study will be particularly relevant for
science journalists and others working with science
communication, as well as students and researchers in
the field of information visualization and the field of
Science and Technology in Society (STS).
2 THEORETICAL FRAMEWORK
The main framework for the planned study is social
semiotic discourse analysis (SSDA). This approach to
texts and other meaningful cultural artefacts can be
traced back to the Australian linguist Michael
Halliday (Halliday, 1978). His theories of meaning-
making through verbal utterances have been adapted
to multimodal and mediated contexts, e.g. by Gunther
Kress and Theo van Leeuwen (Kress and van
Leeuwen, 2006; van Leeuwen, 2005). The study is
also inspired by Norman Fairclough's model of
critical discourse analysis (Fairclough, 2012), aiming
at revealing the social and political impact of specific
textual choices. This theoretical framework invites an
analytical approach first focusing on semiotic,
meaning-making structures in the visualization itself,
and then on the role that the DV has in the textual
whole in which the DV is embedded. Finally, the
findings on these semiotic-technological levels are
discussed with regards to the norms and conventions
of the specific genre and to the socio-cultural practice
in which the DV is an integrated element (see more
about methodology below).
A core concept in social semiotic theory, is that of
the three metafunctions. The metafunctions refer to
the ways in which an expression relates to different
aspects of the context: to the experienced world, to
the participants in the discourse and to the textual and
technological resources applied in the production of
the message. These metafunctions correspond to three
types of meaning. Experiental meaning concerns
what the text can say about (an aspect of) the world.
Inter-personal meaning concerns the construction of
social relations between the participants in the
discourse e.g. between the producer and the reader
of a DV as well as between the reader and the
persons/groups represented by the DV. Finally,
compositional meaning concerns the ways in which
semiotic and media-technological resources are used
to create wholeness and coherence in the textual
output (Halliday, 1978; Kress & van Leeuwen, 2006).
All of these three dimensions of meaning are realized
as semiotic potentials (although not necessarily
identified by all readers), in any instance of
meaningful expression – be it a 5-word e-message or
a 50-minute documentary film but they are
obviously realized in very different ways. A relevant
question is thus; how are the three metafunctions
realized in DVs applied in public science
communication, and how do they contribute to
understanding, engagement and trust?
In a similar study conducted in 2017, the
framework of SSDA was applied in an analysis of 17
What ‘Work’ Can Dataviz Do in Popular Science Communication?
261
journalistic data visualizations, collected from four
major Norwegian news sites (Engebretsen, 2017).
The study revealed that many of the DVs were
designed in a way that made complex patterns in the
data material easy to perceive, and thus supported the
processing of experiental meaning. Interpersonal
meaning potentials were, on the other hand, less
focused. Only eight of the 17 DVs provided any
information about the producers behind the
visualizations, and only four offered substantial
information regarding the methods used in the
process of production. 10 of the DVs offered elements
of interactivity, inviting the readers to explore the
underlying data by themselves. This study provides a
relevant model for analysis, although the DVs were
collected from a different domain of public discourse
than the PSC-discourse described in this position
paper.
1
The other part of the theoretical framework for the
study, is that of public understanding of science
(PUS). PUS is a field of activity as well as an area of
social research, closely related to the wider field of
Science, Technology and Society (STS). PUS-related
studies include the building of theory and models,
qualitative case studies as well as surveys and other
quantitative studies of science communication taking
place in a range of public genres; science blogs,
science journalism, popular science magazines,
museum exhibitions etc. The PUS-discourse has
historically, according to Bauer (2009), focused on
three different problems. In the 60s and 70s, the focus
was on a deficit of knowledge in the general audience.
In the 80s, the dominating concern was a deficit of
attention, interest and support of science in the
general public. Since the 90s, much attention in the
PUS-discourse is given to the lack of trust to science
as well as to the media. The discourse of fake news
(having grown in intensity in the Trump-period), is a
symptom (or a driver) of the deficit of trust to the
media. To illustrate the issue of low trust in science,
one can look to Norway, a nation with a high
educational attainment,
2
yet, with a very high density
of climate skeptics. In the period 2013-2018, between
22 and 27 per cent of the adult Norwegian population
were skeptical to the idea that climate changes are
related to human activities, in spite of what is
massively communicated by scientists.
3
Modelling the interaction between the “esoteric”
scientific discourses going on among scientists, and
the “exoteric” discourses of science on the public and
2
According to National Statistics Institute of Norway, 34
per cent of the Norwegian population have education on
a bachelor’s level or higher. https://www.ssb.
no/en/utdanning/statistikker/utniv
private arenas, Bauer referring to Flecks core-
periphery-model from 1937 (see Fleck, 1979) – states
that the further away from the esoteric core, the
exoteric discourses are characterized by a growing
gradient of simplification, concreteness and certainty
of judgement. In other words; in a highly popularized
presentation of a scientific result, one must expect to
find a higher degree of simplification, of visual
illustration and of certainty (i.e. a lack of reservations
and modifications) than what is expected in e.g. a
textbook in higher education. Bauer calls for more
discourse-oriented studies as a complement to the
far more frequent quantitative studies – in future
investigations of the PUS dynamics, where the
relationships between the esoteric and the (different
levels of) exoteric discourses of science ought to be
closely investigated.
Some commentators are less concerned about
public deficits regarding knowledge, interest and trust
in their approach to science communication, and
more concerned about dialogue and active
participation. They call for a less top-down and more
interactive, mutual and dialogical view on the
interaction between scientists, science
communicators and the public audience (Riise, 2008;
Santerre, 2008). Davies & Horst (2016) model
science communication as a non-hierarchic eco-
system, and point to its large complexity of actors,
epistemologies and discursive elements. In a
dialogical, non-hierarchic approach to science
communication, the style of expression and the inter-
personal dimension of meaning making play a
substantial role in the construction of the participants’
identities and their discursive roles and power.
In the intersection between these two
frameworks, where the analytical tools of social
semiotic theory are focused by core issues in the field
of PUS, we can extract a more nuanced set of research
questions in our study of data visualization in science
communication, building on the broad and general
questions formulated in the initial paragraph. We now
ask:
What characterizes the visual codes applied in
DVs in successful PSC to inform about aspects of
the world?
4
o What DV types are used? What visual codes,
metaphors, forms and colors are applied?
What is the level of information density?
3
https://www.bt.no/btmeninger/debatt/i/LALy5V/slik-er-
de-norske-klimaskeptikerne
4
In this paper, «successful PSC» refers to award winning
instances of Popular Science Communication.
IVAPP 2020 - 11th International Conference on Information Visualization Theory and Applications
262
What role do these DVs have with regards to the
overall message of the texts in which they are
embedded?
o Do they carry core information? Do they
document, illustrate, tell stories? Are
elements of uncertainty represented verbally
or visually?
What characterizes the interplay between DV and
co-text regarding the construction of identities,
trust and discursive roles?
o Are the DVs related to information
concerning sources and methods? Are there
any invitations for reader activity and/or
dialogue?
How do the findings related to the questions above
apply to the current norms and expectations of
popular science communication? Do they
contribute to simplicity, engagement and trust?
3 METHODOLOGY
The method suggested to answer the questions above,
is that of social semiotic discourse analysis (SSDA).
This form of analysis belongs to the scientific
paradigm of hermeneutics, linking descriptive,
interpretive, and critical perspectives to generate a
framework aimed at understanding how a certain
semiotic artefact ‘works’ in a certain socio-cultural
context (cf. van Leeuwen, 2005; Ledin & Machin,
2018). In SSDA, the analyst will always relate a close
reading of selected elements on the micro level (the
semiotic structures) to relevant elements on the macro
level (the socio-cultural context) in order to
understand the social functions of the semiotic
choices made by the text producer. Sometimes the
socio-cultural context is investigated directly,
through observation, interviews etc.; sometimes it is
investigated indirectly, through textual implication,
other literary sources or general knowledge. The
approach to the collected data follows an abductive
method, combining the deductive use of certain
theoretical perspectives as a starting point for the data
analysis, with an inductive openness to the unique
features of the investigated objects and their social
contexts (Richardson & Kramer, 2006).
The first stage in a SSDA is most often to gather
a selection of samples from the field under scrutiny
(Aiello, forthcoming). The samples can be described
according to their formal characteristics on a relevant
level of detail. In the next stage, a small or large
number of samples are analyzed with focus on the
realization of the three metafunctions. The number of
samples is dependent on the research questions as
well as the resources allocated to the study. When
dealing with a larger corpus, a pilot-study with close
reading of 2-3 samples can reveal what features that
are most relevant to include in a more formalized and
systematic study of the whole corpus. (cf.
Engebretsen 2017)
The final stage of the analysis includes a
discussion of the findings in relation to the socio-
cultural context that they belong to. What semiotic
and social work do the artefacts do? Do they answer
to the norms and quality criteria of the genre? Do they
represent change and innovation? Do they affect
issues related to power, democracy and equality? This
is the stage where the most important questions are
dealt with, although the answers as always in
qualitative research necessarily will involve
interpretations and perspectivations on the side of the
researcher.
3.1 Material for Analysis
In the suggested study, the analyst will follow the
three stages described above, with a focus on the
issues formulated in the four research questions. The
corpus material belongs to the category of “best
practices”. It consists of 20 DVs, found in 20 price-
winning instances of popular science communication.
The price-winners are collected from three different
awards, all relevant to the PUS-discourse.
Both static and interactive DVs will be included
in the sample. The difference between a static
(explanatory) and an interactive (exploratory) DV
may affect all three of the semiotic metafunctions
mentioned in the theory section. E.g.; a static DV may
have a stronger narrative power, while an interactive
DV may lead to a more active and emotionally
engaged reading process.
The selection of samples is obviously not
representative of all DVs applied in PSC. Thus, the
findings cannot be taken as indicative of the total
population. On the contrary, they most probably stand
out from their population, which is the reason for their
status as price-winners. However, this status also
gives them the function of being models for other DV
designers. Close reading of a ‘golden sample’ is
particularly relevant in a genre with such a rapid
development concerning visual and technological
design. What wins prices today, will most probably
affect the mainstream of tomorrow.
What ‘Work’ Can Dataviz Do in Popular Science Communication?
263
ACKNOWLEDGEMENTS
The research reported in this article was supported by
The Norwegian Research Council (NFR).
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