When Data Become News. A Content Analysis of Data Journalism Pieces.

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

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

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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)


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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


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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)


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)

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Methodology: Codebook

•  Standardised ‘hand-‐made’ content analysis (e.g., Krippendorff, 2013; Lombard et al., 2002)


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

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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

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 %)


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


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Example: Sociodemographic Data


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Mapping Australia’s Census (2013): hhp://www.smh.com.au/data-‐point/census-‐2012 (9.9.15)

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


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Example: Personal Data


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Your Olympic Athlete Body Match (2013): hhp://www.bbc.co.uk/news/uk-‐19050139 (9.9.15)

Results: Sources and Access to Data


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•  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 %)

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


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Example: Connec4ons and Flows


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Rede de Escândalos (2013): hhp://veja.abril.com.br/infograficos/painel_rede_escandalos/ network_of_scandals.html (9.9.15)

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


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.


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


Thank you!

Wiebke Loosen / Julius Reimer / Fenja Schmidt @wloosen @julius_reimer @Fen_Ja

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When Data Become News. A Content Analysis of Data Journalism Pieces.

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