Slides for the presentation of the paper "Media Bias in German Online Newspapers" at Hypertext 2015

1. Media Bias in
German Online Newspapers
Alexander Dallmann
DMIR – University of Würzburg
Florian Lemmerich (GESIS – Leibniz Institute for the Social Sciences)
Daniel Zoller (DMIR – University of Würzburg)
Andreas Hotho (DMIR – University of Würzburg)

2. Bias in Media
• Different types of bias:
– coverage bias
• count mentions of parties or representatives
– statement bias
• analyze similarity in vocabulary between newspapers
and party manifestos
• perform sentiment analysis towards parties or
representatives
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3. Motivation
– Analyze online news sites with regards to political bias
• large number of articles available
• access to different news sites
• articles contain meta-information
– Articles contain information, they…
• mention parties (SPD, CDU, …)
• mention representatives
• may express sentiment
• may use ideological terms similar to a party
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4. The Dataset
Timeframe: 2009-10-27 - 2013-10-22
Sections: politics, economics
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Site #articles 𝐥𝐞𝐧𝐠𝐭𝐡 party rep either title meta
faz.net 40 209 513.5 30.5% 24.5% 35.1% 4.1% 20.1%
spiegel.de 51 560 472.1 31.7% 30.6% 37.5% 6.0% 10.0%
taz.de 23 610 477.5 35.9% 27.6% 40.0% 2.5% 2.9%
zeit.de 17 926 445.5 36.6% 35.7% 43.2% 6.8% 32.3%
Average: 33 326 477.2 33.7% 29.6% 39.0% 4.9% 16.3%
• ~130 000 articles
• up to 43% contain a reference to either a party or a representative
mentions

5. Coverage Bias
Streit über Schulpflicht von
Flüchtlingskindern: Gabriel bremst
Bausewein
[…] Bausewein, der auch SPD-Chef in
Thüringen ist, hatte in einem offenen
Brief an Bundeskanzlerin Angela
Merkel (CDU) und Ministerpräsident
Bodo Ramelow (Linke) gefordert […]
Flüchtlingspolitik: Merkels wahre
Bewärungsprobe
[…] Die Kanzlerin hielt sich lange raus,
überließ das Thema ihrem
Innenminister Thomas de
Maiziere(CDU). […]
Am Sonntagabend wollte Merkel mit
anderen Spiezenleuten von CDU und
CSU im Kanzleramt beraten, wie es
weitergehen soll. […]
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CDU CSU SPD LINKE GRÜNE FDP
1 0 1 1 0 0
CDU CSU SPD LINKE GRÜNE FDP
1 1 0 0 0 0
CDU CSU SPD LINKE GRÜNE FDP
2 1 1 1 0 0

6. Example
Site CDU CSU FDP Grüne Linke SPD 𝚺
faz.net 315 167 481 213 57 543 1776
spiegel.de 605 296 848 355 266 1007 3377
taz.de 104 28 120 127 99 146 624
zeit.de 224 118 381 147 90 369 1329
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Site CDU CSU FDP Grüne Linke SPD
faz.net 17.7% 9.4% 27.1% 12.0% 3.2% 30.6%
spiegel.de 17.9% 8.8% 25.1% 10.5% 7.9% 29.8%
taz.de 16.7% 4.5% 19.2% 20.4% 15.9% 23.4%
zeit.de 16.9% 8.9% 28.7% 11.1% 6.8% 27.8%
Average 17.3% 7.9% 25.0% 13.5% 8.5% 27.9%
Articles with party acronym in title:
Normalized by row:

7. Results
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Deviations from average:
Site CDU CSU FDP Grüne Linke SPD
faz.net +0.4 % +1.5 % +2.1 % -1.5 % -5.2 % +2.7 %
spiegel.de +0.6 % +0.9 % +0.1 % -3.0 % -0.6 % +1.9 %
taz.de -0.6 % -3.4 % -5.8 % +6.9 % +7.4 % -4.5 %
zeit.de -0.4 % +1.0 % +3.6 % -2.4 % -1.7 % -0.1 %
-8 0 8 16
faz.net
spiegel.de
taz.de
zeit.de
Acronyms in titles
SPD Linke Grüne FDP CSU CDU
• Observable differences in coverage when
looking at articles that focus on a party

8. Results
-8 0 8
faz.net
spiegel.de
taz.de
zeit.de
Acronyms in full text
SPD Linke Grüne FDP CSU CDU
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-8 0 8
faz.net
spiegel.de
taz.de
zeit.de
Representatives in full text
SPD Linke Grüne FDP CSU CDU

9. Statement Bias
• Vocabulary similarity
– Compare manifestos with newspapers
– Count occurences of ideological terms
• freedom, solidarity, … (Pappi, Seher and Kurella, 2011)
– Compare using cosine similarity
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10. Results
Site CDU FDP Grüne Linke SPD
faz.net 0.863 0.853 0.731 0.794 0.838
spiegel.de 0.808 0.802 0.756 0.831 0.833
taz.de 0.756 0.774 0.759 0.835 0.802
zeit.de 0.846 0.842 0.787 0.845 0.864
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• taz.de
• favours keywords from left manifestos
• faz.net
• favours keywords from more conservative and liberal manifestos

11. Summary
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• Dataset
– 4 major german news sites
– ~130.000 articles (4 year period)
• Coverage bias
– observable through counting:
• party acronyms
• representatives
– titles, text, keywords
• Statement bias
– observable similarities in usage of ideological terms
• party manifestos
• newspaper articles

12. Media Bias in
German Online Newspapers
Alexander Dallmann
DMIR – University of Würzburg
alexander.dallmann@uni-wuerzburg.de
Florian Lemmerich (GESIS – Leibniz Institute for the Social Sciences)
Daniel Zoller (DMIR – University of Würzburg)
Andreas Hotho (DMIR – University of Würzburg)

13. Coverage Bias
Articles that contain party acronym P:
𝑀 𝑎𝑐𝑟 𝑃, 𝑁 = | 𝑑 ∈ 𝑁: 𝑎𝑐𝑟 𝑃 ⊑ 𝐹(𝑑) |
Articles that contain representatives of Party P:
𝑀 𝑚𝑒𝑚 𝑃, 𝑁 = | 𝑑 ∈ 𝑁: (∃𝑚 𝑃,𝑖 ∈ 𝑀 𝑃 : 𝑚 𝑃,𝑖 ⊑ 𝐹 𝑑 ) |
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14. Summary
• Dataset:
– 4 major german news sites
– 4 year period
– ~130 000 articles
• Coverage bias
– Compare mentions of
• parties
• representatives
– in:
• title
• text
• keywords
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• Statement bias:
 count keywords
• social, economics,
…
 compare counts of
• party manifestos
• news sites