Presentation by Wiebke Loosen, Julius Reimer & Fenja Schmidt held at The Future of Journalism Conference: Risks, Threats and Opportunities, Sep 10th 2015, Cardiff

1. When
Data
Become
News
A
content
analysis
of
data
journalism
pieces
Wiebke
Loosen,
Julius
Reimer
&
Fenja
Schmidt
@wloosen
@julius_reimer
@Fen_Ja
The
Future
of
Journalism
Conference:
Risks,
Threats
and
OpportuniAes|
Cardiff
|
2015

2. Introduc4on:
‘Big
Data’
and
the
Data-­‐Driven
Society
• Double
relevance
of
‘big
data’
and
the
data-­‐driven
society
for
journalism:
-­‐ Topic
worth
covering:
show
related
developments
and
their
consequences
to
make
them
understandable
and
publicly
debatable
-­‐ The
‘computaAonal
turn’
affects
pracAces
of
news
producAon
à
Emergence
of
a
new
journalisAc
sub-­‐field
‘computaAonal/
data(-­‐driven)
journalism’
(cf.
Coddington,
2015;
Fink/Anderson,
2015;
Lewis,
2015)
Loosen/Reimer/Schmidt
2

3. Literature
Review:
Research
on
Data
Journalism
(#ddj)
A
“rapidly
growing
body”
(Lewis,
2015:
322)
of
studies
focusing
on:
1. Defining
what
#ddj
is
(e.g.,
Anderson,
2013;
Appelgren/Nygren,
2014;
Coddington,
2015;
Fink
&
Anderson,
2015;
Gray
et
al.,
2012)
Presumed
key
characterisAcs:
-­‐ (Usually
large)
sets
of
quanAtaAve
(digital)
data
-­‐ VisualisaAon
(maps,
bar
charts,
etc.)
-­‐ ParAcipaAon
and
crowdsourcing
-­‐ Open
data
and
open
source
2. Researching
what
actors
in
the
field
do
and
think
(Appelgren/Nygren,
2014;
De
Maeyer
et
al.,
2015;
Fink
/Anderson,
2015;
Parasie,
2014;
Parasie/Dagiral,
2013;
Karlsen/Stavelin,
2014;
Weinacht/Spiller,
2014)
à
No
systemaAcally
gathered
insights
regarding
data
journalism
as
“an
emerging
form
of
storytelling”
(Appelgren/Nygren,
2014:
394)
Loosen/Reimer/Schmidt
3

4. Research
Objec4ves
Focus
on
the
output
of
#ddj
to
beher
understand
its
reporAng
styles
and
data
sources:
à
Map
actual
occurrence
and
classify
different
types
of
presumed
key
characterisAcs
in
data-­‐driven
pieces:
-­‐ Data
sets
and
data
processing
-­‐ VisualisaAon
elements
-­‐ InteracAve
features
à
Determine
topics
covered
à
IdenAfy
media
organisaAons
which
are
parAcularly
acAve
in
the
field
Loosen/Reimer/Schmidt
4

5. Methodology:
Sample
• Nominees
for
the
Data
Journalism
Award
(issued
annually
by
the
Global
Editors‘
Network)
2013
and
2014
(following
Lanosga,
2014;
Wahl-­‐Jorgensen,
2013a,
2013b)
• ParAcular
sample
with
a
‘double
bias’
(special
group,
self-­‐selected)
and
a
‘double
advantage’
(defined
as
#ddj
by
experts
in
the
field,
seen
as
‘gold
standard’
that
could
influence
further
development)
Loosen/Reimer/Schmidt
Submissions
Nominated
projects
Projects
suited
for
analysis
Award-­‐winning
projects
(%
of
analysed
projects)
2013
>300
72
56
6
(10.7)
2014
520
75
64
9
(14.1)
Total
>820
147
120
15
(12.5)
5

6. Methodology:
Codebook
• Standardised
‘hand-­‐made’
content
analysis
(e.g.,
Krippendorff,
2013;
Lombard
et
al.,
2002)
Loosen/Reimer/Schmidt
Dimensions
V
No.
Categories
of
analysis
Formal
characterisAcs
V
1-­‐13
Medium,
topic,
language,
length
&
no.
of
related
arAcle(s),
no.
of
people
involved,
external
partners,
…
Dataset
V
14-­‐22
Type
of
data
source,
access
to
data,
kind
of
data,
geographical
&
temporal
reference,
changeability
of
dataset,
unit
of
analysis,
addiAonal
info
Analysis
and
journalisAc
ediAng
of
content
V
23-­‐26
Personalized
case
example,
criAcism,
visualisaAon,
purpose
of
analysis
Context
of
use
V
27-­‐29
InteracAve
funcAons,
online
access
to
the
database,
opportuniAes
of
further
interacAon/communicaAon
6

7. Results:
Organisa4ons
and
Staff
Involved
• Dominance
of
newspapers:
42.5
%
(of
all
cases)
• Rise
of
magazines
(7.1
%
à
17.2
%)
and
of
invesAgaAve
journalisAc
organisaAons
(14.3
%
à
25
%)
• Data
journalism
is
mostly
a
collaboraAve
effort:
-­‐
On
average
five
authors/contributors
-­‐
Increase
from
2013
to
2014
-­‐
External
partners
menAoned
in
35
%
of
all
cases
Loosen/Reimer/Schmidt
7

8. Results:
Topics
Covered
and
Formal
Elements
• Most
important
topic:
poliAcs
(48.3
%),
osen
in
combinaAon
with
financial
aspects
• Societal
issues:
33.3
%;
health
&
science:
21.7
%;
business
&
economy:
20
%
• Mostly
combinaAon
of
visualisaAon(s)
with
one
(48.3
%)
or
more
(34.2
%)
accompanying
texts
• Personalised
case
example
as
a
way
to
counter
abstractness
of
quanAtaAve
data
-­‐
In
total
40.8
%
of
the
pieces
-­‐
Lower
rates
for
economic
and
educaAon
topics
(20.8
%
and
22.2
%)
Loosen/Reimer/Schmidt
8

9. Results:
Kinds
of
Data
2013
(n
=
55)
2014
(n
=
64)
Awarded
(2013
+
2014)
(n
=
15)
Total
(n
=
119)
Freq
%
Freq
%
Freq
%
Freq
%
Financial
data
25
45.5
29
45.3
8
53.5
54
45.4
Geo
data
26
47.3
25
39.1
6
40.0
51
42.9
Measured
values
19
34.5
28
43.8
4
26.7
47
39.5
Sociodemographic
data
21
38.2
16
25.0
4
26.7
37
31.1
Personal
data
12
21.8
21
32.8
5
33.3
33
27.7
Metadata
7
12.7
13
20.3
1
6.7
20
16.8
Poll
raAngs
/
survey
data
8
14.5
7
10.9
1
6.7
15
12.6
Other
data
-­‐
-­‐
-­‐
-­‐
1
6.7
2
1.7
Loosen/Reimer/Schmidt
9

10. Example:
Sociodemographic
Data
Loosen/Reimer/Schmidt
10
Mapping
Australia’s
Census
(2013):
hhp://www.smh.com.au/data-­‐point/census-­‐2012
(9.9.15)

11. Results:
Kinds
of
Data
2013
(n
=
55)
2014
(n
=
64)
Awarded
(2013
+
2014)
(n
=
15)
Total
(n
=
119)
Freq
%
Freq
%
Freq
%
Freq
%
Financial
data
25
45.5
29
45.3
8
53.5
54
45.4
Geo
data
26
47.3
25
39.1
6
40.0
51
42.9
Measured
values
19
34.5
28
43.8
4
26.7
47
39.5
Sociodemographic
data
21
38.2
16
25.0
4
26.7
37
31.1
Personal
data
12
21.8
21
32.8
5
33.3
33
27.7
Metadata
7
12.7
13
20.3
1
6.7
20
16.8
Poll
raAngs
/
survey
data
8
14.5
7
10.9
1
6.7
15
12.6
Other
data
-­‐
-­‐
-­‐
-­‐
1
6.7
2
1.7
Loosen/Reimer/Schmidt
11

12. Example:
Personal
Data
Loosen/Reimer/Schmidt
12
Your
Olympic
Athlete
Body
Match
(2013):
hhp://www.bbc.co.uk/news/uk-­‐19050139
(9.9.15)

13. Results:
Sources
and
Access
to
Data
Loosen/Reimer/Schmidt
13
• Sources:
official
insAtuAons
(67.5
%),
other
non-­‐commercial
organisaAons
(44.2
%),
own
sources
(18.3
%)
• Mostly
data
that
is
publicly
available
(41.7
%),
access
to
data
osen
not
indicated
(40
%)

14.
2013
(n
=
56)
2014
(n
=
64)
Awarded
(2013
+
2014)
(n
=
15)
Total
(n
=
120)
Freq
%
Freq
%
Freq
%
Freq
%
Compare
values
46
82.1
56
87.5
15
100.0
102
85.0
Show
changes
over
Ame
26
46.4
30
46.9
8
53.3
56
46.7
Show
connecAons
and
flows
18
32.1
23
35.9
4
26.7
41
34.2
Show
hierarchy
8
14.3
6
9.4
1
6.7
14
11.7
Results:
Purpose
of
Analysis
Loosen/Reimer/Schmidt
14

15. Example:
Connec4ons
and
Flows
Loosen/Reimer/Schmidt
15
Rede
de
Escândalos
(2013):
hhp://veja.abril.com.br/infograficos/painel_rede_escandalos/
network_of_scandals.html
(9.9.15)

16. Results:
Visualisa4ons
&
Interac4ve
features
• Mainly
pictures
(60.0
%),
simple
staAc
charts
(54.2
%),
and
maps
(49.2
%)
• Rarely
animated
visualisaAons
(15.8
%),
no
case
without
visualisaAon
• CombinaAon
of
more
than
two
different
kinds
of
visualisaAons
(74.2
%),
osen
simple
staAc
charts
with
pictures
(31.7
%)
or
a
map
(27.5
%)
• InteracAve
funcAons:
mostly
zoom
and
details
on
demand
(55.8
%),
filtering
(51.7
%)
-­‐
18.3
%
of
cases
have
no
interacAve
funcAons
at
all
-­‐
The
average
piece
contains
1.55
different
interacAve
features
Loosen/Reimer/Schmidt
16

17. Conclusion:
The
‘Typical’
#ddj
Piece
The
‘typical’
data-­‐driven
piece…
• is
published
by
a
newspaper,
• covers
a
poliAcal
topic,
• relies
on
public
data
from
official
sources,
• builds
its
story
on
financial
and/or
geodata
–
preferably
collected
on
a
naAonal
scale,
• is
based
on
a
simple
unit
of
analysis
such
as
single
persons,
• compares
values
in
order
to
show
differences
and
similariAes
between
different
objects
of
study
(e.g.,
people
of
different
gender,
neighbourhoods)
• combines
two
types
of
visualisaAons
–
preferably
pictures
with
maps
or
simple
charts,
• allows
the
user
to
zoom
into
a
map,
request
details
and/or
to
filter
data.
Loosen/Reimer/Schmidt
17

18. Conclusion:
Tendencies
of
Development
• Data
journalism
is
increasingly
personnel
intensive
–
at
least
as
far
as
our
parAcular
sample
is
concerned
• Significant
increase
of
stories
building
on
data
from
non-­‐commercial
organisaAons
(e.g.
universiAes,
NGOs,
research
insAtutes)
between
2013
and
2014
à
#ddj
increasingly
discovers
new
data
sources
• Awarded
stories
are
more
likely
to
refer
to
data
on
a
naAonal
level;
stories
from
2014
are
less
likely
to
draw
on
regional
data
than
those
from
2013
à
news
value
of
data
• Awarded
stories
are
less
likely
to
contain
no
interacAve
funcAons
• Results
for
DJA
2015
will
show
if
we
can
idenAfy
any
clearer
lines
of
developments
Loosen/Reimer/Schmidt
18

19. Thank
you!
Wiebke
Loosen
/
Julius
Reimer
/
Fenja
Schmidt
@wloosen
@julius_reimer
@Fen_Ja

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