=Paper=
{{Paper
|id=Vol-1734/fmt-proceedings-2016-paper8
|storemode=property
|title=Exploring Media Transparency With Multiple Views
|pdfUrl=https://ceur-ws.org/Vol-1734/fmt-proceedings-2016-paper8.pdf
|volume=Vol-1734
|authors=Alexander Rind,David Pfahler,Christina Niederer,Wolfgang Aigner
|dblpUrl=https://dblp.org/rec/conf/fmt/RindPNA16
}}
==Exploring Media Transparency With Multiple Views==
https://ceur-ws.org/Vol-1734/fmt-proceedings-2016-paper8.pdf
Exploring Media Transparency With Multiple Views
Alexander Rind,1,2 David Pfahler,1 Christina Niederer,2 and Wolfgang Aigner1,2
1
TU Wien, Austria
2
St. Poelten University of Applied Sciences, Austria
This so-called media transparency (MT) dataset is
a valuable resource for politically concerned citizens
Abstract as well as for data journalists [Aus15, Lor10]. They
are interested in exploring the available data indepen-
Politically concerned citizens and data jour- dently looking for stories beyond prearranged sum-
nalists want to investigate money flows from mary statistics. However, the MT dataset is much
government to media, which are documented too large to be browsed line by line. Neither is it suffi-
as open government data on ‘media trans- cient to look only at the largest flows of money because
parency’. This dataset can be characterized many possible questions of interest focus on changes
as a dynamic bipartite network with quantita- over time and the many-to-many relationship between
tive flows and a large number of vertices. Cur- legal entities and media [NRA+ 16]. For this purpose
rently, there is no adequate visualization ap- it is useful to conceptualize the MT dataset’s money
proach for data of this structure. We designed flows as time-dependent attributes on the edges of a
a visualization providing coordinated multiple bipartite network. Simple data analysis tools such as
views of aggregated attribute values as well as spreadsheets do not adequately support such a data
short tables of top sorted vertices that can be structure.
explored in detail by linked selection across Interactive visual representations of data [CMS99,
multiple views. A derived attribute ‘trend’ Mun14] are a well-suited approach to explore com-
allows selection of flows with increasing or de- plex datasets. Many visualization techniques have
creasing volume. The design study concludes demonstrated their value in exploring time-oriented
with directions for future work. data [AMST11] and network data [BBDW16, HSS15].
However, the combination of time with quantitative
1 Introduction flows in a bipartite networks is still an open challenge
for visualization research [NAR15].
Independent news and media are a cornerstone of mod-
This paper contributes a visualization design study
ern democracy – often called the fourth power. How-
[SMM12] for time-oriented quantitative flows in a bi-
ever, governmental advertisement and sponsorships
partite network. It uses the MT dataset as example
could influence news coverage limiting the media’s in-
and non-expert users such as citizens and journalists
dependence. In Austria, the federal law on Trans-
as target audience. After surveying related work in
parency in Media Cooperation and Funding [Med15]
Section 2 and characterizing the domain problem in
makes it mandatory to disclose such flows of money
Section 3, we present the justified visualization design
from legal entities (e.g., federal ministries, cities, eco-
in Section 4. Next, a usage scenario demonstrates the
nomic chambers, government-owned companies) to
design’s utility in Section 5. The paper concludes with
media institutions (e.g., newspaper, TV, radio, on-
reflections for future development.
line). The Austrian Regulatory Authority for Broad-
casting and Telecommunications [RTR] collects these
data and makes them publicly available via the Aus- 2 Related Work
trian open government data portal [RTR16]. The design space of network visualization has been
mapped in some recent state-of-the-art reports: Had-
Copyright c by the paper’s authors. Copying permitted for
private and academic purposes.
lak et al. [HSS15] identified five facets of concern for
In: W. Aigner, G. Schmiedl, K. Blumenstein, M. Zeppelzauer
visualizing a network: (i) its structure comprised of
(eds.): Proceedings of the 9th Forum Media Technology 2016, nodes and edges, (ii) partitions, (iii) the attributes
St. Pölten, Austria, 24-11-2016, published at http://ceur-ws.org of nodes and edges, (iv) dynamics, i.e., change over
65
Exploring Media Transparency With Multiple Views
Table 1: Raw format of the media transparency (MT) dataset (first four entries)
legal entity time law medium money amount
Abfallwirtschaft Tirol-Mitte GesmbH Q4/2012 §2 Bezirksblätter Tirol 8,122.32 e
Agrarmarkt Austria Marketing GesmbH Q4/2012 §2 Falsta↵ 26,418.00 e
Agrarmarkt Austria Marketing GesmbH Q4/2012 §2 Connoisseur Circle 6,142.50 e
Agrarmarkt Austria Marketing GesmbH Q4/2012 §2 bz-Wiener Bezirkszeitung 7,031.16 e
..
.
time, and (v) spatialization such as geographic con- fees. Each quarter, each legal entity is obligated to
text of nodes. Beck et al. [BBDW16] addressed make a disclosure for both §2 and §4. Every media
in particular visual representations for dynamic net- cooperation involving more than 5,000 e needs to be
works such as animation and timeline. Von Landes- included with the recipient’s name and the amount of
berger et al. [vLKS+ 11] focused on large networks. money accumulated in the quarter. If a legal entity
Niederer et al. [NAR15] surveyed visualization of dy- had no such media cooperation, it still has to submit
namic, weighted and directed networks, and thus, data a nil report.
of a structure similar to the MT dataset. The MT dataset is published on an open data portal
Examples of such related visualization designs are [RTR16] each quarter of a year with data covering the
DOSA [vdEvW14], egoSlider [WPZ+ 16], egoLines preceding eight quarters. The raw data are formatted
[ZGC+ 16], Graph Comics [BKH+ 16], TimeArcTree as semicolon-separated values in a text file. Table 1
[GBD09], and Visual Adjaceny List [HBW14]. How- shows the five relevant variables: name of the legal
ever, none of these approaches explicitly considers the entity, time specified by year and quarter, category
bipartite nature of the MT dataset, i.e., that there are of legal background, name of the medium, amount of
distinct nodes for legal entities and for media. money (quantitative). Additionally, the raw data con-
We could identify only one scholarly work focusing tains a variable that flags nil reports.
on visualizing the MT dataset in particular: Niederer
et al. [NRA+ 16] investigated the visualization needs 3.1 Data Abstraction
of data journalists based on four interviews that were
anchored on the MT dataset as exemplary scenario. The MT dataset is comprised of the quarterly money
Besides that, there is some press coverage on the data transferred from legal entities to media. We can con-
and some articles are accompanied by interactive web ceptualize these data as time-dependent flows in a bi-
infographics (e.g., derStandard.at [Ham], Paroli Mag- partite network (Figure 1) [NRA+ 16]. The network’s
azin [Lan]). Yet, these infographics present a subset underlying graph is bipartite because its vertices can
of the available data that has been aggregated and fil- be divided in two disjoint sets – legal entities and
tered to support their articles’ story. Since they allow media – and each edge connects vertices of di↵erent
only minimal interactivity, further exploration is not sets. These edges are directed and weighted repre-
possible. Furthermore, since 2013 the open source soft- senting the flow of money from legal entities to media.
ware project Medientransparenz Austria [SBSV] pro- The network is dynamic both in terms of its struc-
vides an interactive online tool that shows the com- ture (vertices and edges can appear or disappear over
plete MT dataset. It integrates several visual repre- time) and its quantitative flows (weights changing over
sentations giving insight into the data, but its views time) [vLKS+ 11]. The time-oriented aspect of the data
require much scrolling and are distributed across mul- can be characterized as instants on a discrete, interval-
tiple pages. In addition, changes of money flow over
time are not explicitly represented.
3 Background
As a fundament for developing a novel visualization
design for the MT dataset, we must first understand
its background and characterize the domain problem.
The law [Med15] regulates three categories of Figure 1: Conceptualizing the MT dataset as time-
money flows that need to be disclosed: §2 covers adver- dependent flows in a bipartite network
tisement, §4 sponsorships, and §31 ORF programme Figure by [NRA+ 16] used with permission.
66
Exploring Media Transparency With Multiple Views
based, linear time domain with the granularities quar- The quantitative values of quarterly flows vary be-
ter and year [AMST11]. tween e 5,000 (the minimum to be reported) and
This abstract data structure has some benefits over e 1.929.533 (median = e 10,931; average = e 23,444).
the raw data’s table structure: The central aspects
of the problem domain (legal entities, media, and 3.3 Design Requirements
flows) are represented directly as data items, which
can have properties from derived data such as aggre- Based on our data analysis described above and the
gated money. Network metrics such as in-degree can interviews with data journalists interested in the MT
be examined. They can also be manipulated by user dataset as reported by Niederer et al. [NRA+ 16], we
interaction. can identify five design requirements that a visualiza-
tion design for the MT dataset should fulfill:
3.2 Preprocessing and Analysis of Data Scale R1 Data scalability: The number of vertices for both
legal entities and media is relatively large. Be-
We perform some preprocessing to achieve a better sides the institutions’ names and their network
data basis for our visualization: relations, there are no further data that could be
(1) We substitute the original MT dataset with used for clustering vertices. While a majority of
data from the Medientransparenz Austria project vertices is only sparsely connected, some central
[SBSV], which have two benefits: First, they have vertices have a large number of flows. Likewise,
included data for all quarters since the start of the the weights representing amount of money can
MT dataset in Q3/2012. Second, they have pre- vary widely within the network. The time dimen-
processed the data to clean di↵erent forms of writ- sion adds additional scale.
ing the names of media and legal entities. Such R2 Development over time: The data journalists in-
inconsistencies could result either from typos or terviewed by Niederer et al. [NRA+ 16] expressed
from the organization actually being renamed. particular interest in patterns or abnormalities in
(2) Next, we discard nil reports from the data. Even the number and weight of flows over time.
though these nil reports make up about 80% of all R3 Data wrangling: For two reasons, users would
records, they cannot add any insight to our design need to refine the MT dataset by basic data wran-
as they have missing values for media name and gling functionality: First, they can add their im-
amount of money. plicit expert knowledge into the analysis. For ex-
(3) Finally, we also discard programme fees (legal cat- ample, they could group together the federal min-
egory §31) because on the one hand there are only istries run by politicians of the same party.
one or two records per quarter and on the other Second, data quality is still not sufficient for some
hand their amount is much higher than any other data entries even though data quality measures
record. The median §31 amount is about 80 times have been taken by the RTR and the dataset has
as much as the highest regular amount. been pre-cleaned by the Medientransparenz Aus-
tria project. Table 2 shows some examples based
As of summer 2016, the preprocessed MT dataset on media from this dataset containing the string
encompasses 36,261 quarterly money flows over 15 “standard”. It should be possible to combine en-
quarters (Q3/2012–Q1/2016). So that one quarter has tries with di↵erent forms of writing or di↵erent
on average circa 2,400 flows. 34,717 flows (96%) have media (print, online, app) of the same newspaper
§2 as legal background and there are 1,544 flows for and to hide entries of poor quality.
§4. (30 flows for §31 have been discarded.) R4 Ease of use: The target audience of the MT
There are 993 distinct legal entities and 3,813 dataset such as interested citizens or data jour-
distinct media. Legal entities have between 1 and nalists will most likely have no expert knowledge
1,782 outgoing flows (median = 8; average = 36.2), if of statistics or visualization. They will access the
we count each quarter as a separate flow. These flows MT visualization as a spontaneous activity where
connect them to between 1 and 618 distinct media (me- no special training can be provided. Therefore,
dian = 3; average = 12.2). 71 legal entities maintain a care should be taken that well-known visualiza-
continuous flow over all 15 quarters to between 1 and tion techniques are chosen and the user interface
24 media. Media have between 1 and 1,577 incoming is self-explaining.
flows (median = 1; average = 9.4). These flows connect R5 Interactive exploration: Some users will approach
them to between 1 and 285 distinct legal entities (me- the MT visualization trying to verify an existing
dian = 1; average = 3.2). 68 media maintain a contin- hypothesis but we expect that a majority of us-
uous flow over all 15 quarters from between 1 and 18 age session will consist of undirected exploration
legal entities. in search for patterns of interest. For this, interac-
67
Exploring Media Transparency With Multiple Views
Table 2: Media matching the query string “standard” ters best [Mun14, p. 150]. A similar aggregation is
ordered by the number of connected legal entities and visualized for sum of money transferred by legal back-
showing the aggregated sum of transferred money. ground in Figure 2.B.
Three entries are di↵erent forms of writing the same To visualize the distribution of a single quantitative
website. The fifth entry contains the names of six sep- attribute a histogram can be used [Mun14, p. 306].
arate newspapers and stands as example of inconsis- Figure 2.C shows a histogram of the data attribute
tent data collection when he MT dataset was started “money amount”.
in 2012. Figure 2.D is a histogram of the data attribute
medium #rel. summed flows “trend”. This attribute is derived from all amounts
of money ei flowing from a legal entity to a medium
Der Standard 189 18,905,741 e
over time i. The trend T quantifies the relative di↵er-
derstandard.at 64 2,768,875 e
ence of money transferred between the first half of the
www.derstandard.at 19 312,242 e
quarters |Qm | and the second half.
Der Standard KOMPAKT 2 44,745 e
Standard Verlagsge- 1 3,099,082 e
sellschaft m.b.H. j k
|Q|
Krone, Kurier, Presse, 1 90,874 e |Qm | = 2 (1)
Salzburger Nachrichten, P|Q| P|Qm | 1
ei ei
Standard, Kleine Zeitung T = P|Q|
i=|Qm | i=1
(2)
ei
derstandard.at App 1 11,510 e i=1
ES Evening Standard 1 10,884 e
Magazine The categorical data attributes legal entity and
http://www.derstandard.at 1 9,938 e medium both have a large number of categories (see
Subsection 3.2), which are too many to visualize them
in a bar chart. Neither is it possible to aggregate the
tive features are needed that are usable and help categories in a reasonable way. But the entries can
users maintain overview. be sorted by another aggregated quantitative data at-
tribute so that only the most relevant ones are dis-
4 Visualization Design played For example, it is possible to sort legal entities
by the sum of money transferred from them to media.
Based on these design requirements, we developed a vi-
Figure 2.E shows the details for the first 10 sorted legal
sualization design for the MT dataset (Figure 2). This
entities as a table with 4 columns. The first column
section describes the design and explains its underly-
shows the name of the legal entity. The second column
ing rationale. In Subsection 4.1 the individual diagram
shows the sum of the transferred money to various me-
views of the design are presented. How the user is
dia over time. Additionally a sparkline sized bar chart
able to interact with them is described in Subsection
represents the distribution of the transferred money
4.2 and how the views are linked with each other is
over time. This enables the user to see the trend over
delineated in Subsection 4.3.
time [Tuf06, AMST11]. The third column displays the
number of relations, i.e. the count of media receiving
4.1 Attribute Visualization money from the legal entity. The forth column dis-
The MT dataset contains 5 data attributes. The plays the “trend” as calculated by Equation 1. This
columns of Table 1 display these data attributes. It is data table visualization enables the user to receive de-
not possible to visualize every single data record of this tailed aggregated information for a few entities. Fig-
table in the dashboard, therefore the records are ag- ure 2.F applies the same visual representation to the
gregated and the aggregated information is displayed. first 10 sorted media.
[Mun14, p. 305] The last visualization in Figure 2.G shows the flow
For example Figure 2.A shows aggregated data of money from legal entities to media using a chord
of money transferred over time. For this, the data diagram [KSB+ 09]. The aggregated amount of money
attribute “money amount” is summed for all data is encoded with the length of an arc of a circle seg-
records with the same value in the data attribute ment of the diagram. This allows the user to see from
“time”. This reduces the data to only 2 data attributes which legal entity how much money is transferred to
and only 1 data record per quarter. The sum is a quan- which medium. Because there are too many di↵erent
titative attribute and the quarters can be handled as categories, placeholder segments are generated for le-
ordinal attribute. A bar chart suites the task of look- gal entities and media, which contain all not displayed
ing up and comparing the values of the di↵erent quar- entries and aggregate the money for all of them.
68
Exploring Media Transparency With Multiple Views
A B C D
H H
I I
E G F
Figure 2: The visualization design for the media transparency (MT) dataset is comprised of seven views: (A) bar
chart of aggregated money by time, (B) second bar chart for money by legal category, (C) histogram of money
by flow, (D) histogram of increasing/decreasing trend, (E) table of 10 legal entities with total money, sparkline
of money, number of connected media, and trend, (F) second table of top 10 media, and (G) chord diagram of
flows. Both tables can be (H) searched and (I) sorted.
4.2 Interaction Components for every visualization. By clicking onto a data
table row the entity is selected. The histograms
The interaction with the diagrams is essential for the
(Figure 2.C&D) do not support a simple click op-
user to explore the data and to verify or refute an
eration, but a click and drag operation to select
initial hypothesis (R5).
a one dimensional range of the attribute in the
In Figure 2 the data is visualized without any ma- histogram [Mun14, ch. 11.4].
nipulations by the user. The first three diagrams (Fig- Highlight Elements To visualize which visual ele-
ure 2.A,B,C&G) give the user an overview of the un- ments are selected, the color saturation of the vi-
derlying data. To analyze the data further the user is sual element is increased and for the filtered ele-
able to manipulate the view of the data. ments the saturation is decreased. Figure 3 shows
Details on Demand The visualization design en- this di↵erence in saturation in contrast to the not
ables the user to receive details of a visual en- selected visual elements in Figure 2.A&B. The col-
coding of an aggregation of a data attribute of a oring of the small-multiple bar charts in the data
chart. The visual encoding of a number, for ex- table is also linked with the highlighting of the
ample the height of a bar in a bar chart or the time bar chart. The used colors are selected using
length of an arc in a flow visualization, supports the ColorBrewer2 tool, which is based on evalu-
the user to compare the encoding with the same ation of “385 unique colour schemes [...] across
encoded data attributes. To receive exact num- di↵erent computer platforms and monitors, [...]
bers the user is able to hover over each visual en- for possible colour-blind confusions, as well as in
coded element and receive in place information printed formats.” [HB03]
with a tool-tip [Dix09]. Sort To explore detail information for the trend over
Select Elements To receive even more detailed in- time, money, and the number of relations from one
formation about the highlighted visual element, entity to another, the user is able to sort the data
the user is able to select it. The data is then fil- table along the data attribute of her/his interest
tered by the selection and all other visualizations (see Figure 2.I).
are updated with the newly filtered data. This in- Search To support users’ who want to analyze the
teraction method is implemented in the visualiza- data for a specific entity, full-text search is inte-
tion design as simple left-mouse-click and works
69
Exploring Media Transparency With Multiple Views
grated. In our visual design this is implemented 6 Usage Scenario
as a simple form text fields for the legal entities
This sections presents a usage scenario to understand
and media (see Figure 2.H).
how the visualization of the MT dataset enables users
Combine and Remove Like already mentioned in
to obtain a deeper insight into the data. The steps
Section 3.1 the data quality might not be optimal.
of the scenario can be followed in a video located at
As modifying the underlying data cannot be ex-
https://vimeo.com/188278798.
pected by the target user group, interactive visual
In most cases a user that is interested in a data
editing should be possible (R3). In our prototype,
set has some a-priori knowledge and a hypothesis that
users may remove entries and combine multiple
she/he wants to verify or falsify. In this scenario the
entries into a single entry. With a click onto the
user is interested in which legal entities spend money
labels above a data table the selected rows of that
on online advertisement with Google.
table are combined or removed.
Entering “google” into the full text search, the
list returns 57 entries (e.g.: google, google.at,
www.google.at, ...) due to data quality issues. By
4.3 Coordinating Multiple Views
interactively manipulating the data the user is able to
The designed interface connects the di↵erent visual- obtain a deeper insight. For example by combining
izations and widgets and organizes them. The views the 57 categorical entries of the data attribute media
are arranged on fixed positions, but the user is able to one entry named “Google”. The flow visualization
to filter the data [EB11, Rob07]. Because all visual- is now easier to read because the number of visual el-
izations of the media transparency database use the ements was reduced.
same data set it is possible to link the selection be- The user is able to filter data which she/he is not
tween all views and thus use each view for dynamic interested in to obtain new information. For example
query [AS94, ST98]. Additionally the color of the vi- by selecting only the entries of universities. This re-
sual elements indicate which aggregation is used. This sults in 3 legal entities, which the user is now able to
helps the user to see the connection between the vi- compare in more detail (see Figure 4).
sualizations and it enables the user to understand the
connection of a data attribute in one to the distribu- 7 Conclusions
tion of a data attribute in another diagram [Mun14, This paper presented a visualization design to explore
ch. 12]. the MT dataset, a large open government data asset
reporting on the flows of money from government to
media. We implemented the design as a web-based
5 Implementation prototype, made it publicly available, and showcased it
on science communications events like Lange Nacht der
The visualization design has been implemented as a Forschung. Based upon these experiences and informal
web-based software using JavaScript with the libraries feedback we received, we can now reflect how well the
D3.js [BOH11], Crossfilter [cro], and dc.js Dimensional visualization design addresses its design requirements
Charting [dc]. and provide directions for future research:
The implementation is available from https: R1 Data scalability: The various views of aggre-
//github.com/VALIDproject/mtdb2 as free and gated attributes are useful to provide a big-picture
open source software under a BSD-2-clause license overview of the dataset. Subsequently, the inter-
and can be tested at http://medientransparenz. action concept of linked selection, sorting tables,
validproject.at/dashboard/. For iterative refine- and showing the first results works to learn about
ment an informal usability test with two subjects was the details. Some users criticized the chord dia-
conducted. gram as being too cluttered and hard to read. A
Sankey diagram is being considered as alternative.
In future work, two additional proposals from
the preceding problem characterization study by
Niederer et al. [NRA+ 16] can be adopted: The
large number of legal entities and media could be
automatically clustered into hierarchical groups
using text or network analytics. Alternatively,
supplementary data could be loaded to provide
Figure 3: The elements of the visualizations adapt additional properties such as geographic area for
color saturation upon selection changes. legal entities and/or media. These properties
70
Exploring Media Transparency With Multiple Views
Figure 4: Snapshot of the visualization after following the steps of the usage scenario.
would subsequently be used for filtering and ag- should be provided so that these features are not
gregation. visible by default.
R2 Development over time: Both the bar chart view R4 Ease of use: The visualization design is built us-
showing aggregated money flow over time, and ing simple visual representation techniques that
the sparkline sized bar chart for each legal en- are well known to the general public. Still, the
tity/medium work well to show distribution, ab- multiple views in composition were described as
normalities and other temporal patterns for the slightly overwhelming at first impression. In ad-
currently selected respectively visible items. The dition, novice users were not aware of direct ma-
derived attribute “trend” was added to allow nipulation so they did not expect they could filter
overview and direct manipulation of one concrete the data e.g. by clicking on a bar.
temporal pattern. While being a powerful fea- R5 Interactive exploration: As demonstrated in the
ture, it is hard to grasp for novice users of the MT usage scenario, the visualization design allows free
dataset visualization. Further design experiments exploration of the MT dataset. While doing so,
are necessary to provide user-friendly exploration users can maintain overview of system state, i.e.
of temporal dynamic flows in bipartite networks. which selections are active and also reset selec-
R3 Data wrangling: The interactions to combine legal tions.
entities and/or media o↵ers some benefits. The As further support for exploration, the data jour-
views are less cluttered by di↵erent entries for nalists interviewed by Niederer et al. [NRA+ 16]
related institutions. In some cases data wran- suggested documentation of the research path in
gling can eliminate a perceived false patterns such order to provide analytic provenance [NCE+ 11].
as abrupt end of flow to one medium that is in Thus, our design study yielded not only a possible
fact continued to a medium of a slightly di↵erent visualization design but also a range of directions for
name. future work on exploring flows in dynamic bipartite
Further work on data wrangling is indicated: On networks.
the one hand, we found the current functionality
too limiting in several exploration sessions and de- Acknowledgements
sired more flexibility such as hierarchical groups
and/or multi-group assignment like tags. On the This work was supported by the Austrian Ministry
other hand, the two functions ‘combine’ and ‘re- for Transport, Innovation and Technology (BMVIT)
move’ introduced more confusion for first time under the ICT of the future program via the VALiD
users. Possibly, a dedicated data wrangling mode project (no. 845598) and by the Austrian Science Fund
(FWF) via the KAVA-Time project (no. P25489).
71
Exploring Media Transparency With Multiple Views
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