1. Breaking the Barrier
Interactive Election Campaign
Communication on Twitter
during the German General Election 2009
Pascal Jürgens Andreas Jungherr
U of Mainz, Germany U of Bamberg, Germany
pascal.juergens@gmail.com andreas.jungherr@gmail.com

2. Twitter and Politics
The 2009 German General Election started
with the impression of Obama’s online
campaign fresh in mind
Due to several high-profile incidents, German
media and politics focussed on Twitter at
least as much as on other social networks
2

3. Twitter Overview
»Micro-publishing« — publish short messages
Re-Tweet — quote message including attribution
Directed (@-)message — explicitly sent to a
recipient
Topic tags (#) — defines the topic of the message
High degree of mobile usage
(~15% in our dataset)
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4. Data Collection
Bootstrap: List of political twitter users
(Assembled from several websites compiling lists of politicians on
twitter)
Growth: perpetual search for #tags
(Add new users to sample)
Capture: Collect all new tweets
(Also crawling archives for coverage over entire time range)
Graphs: Nightly snapshot of friend/followers
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5. Dataset
Three months prior to General Election
A sample of Germany’s politically active
twitter users — 33 048 individuals
A complete archive of their communication
(public tweets) — 10 109 894 messages
A temporal map of their connections
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6. Message Volume
Election Day
Twitter Outage
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7. Political Message Volume
Election Day
TV debate
Poster remixes
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8. “We are strong enough
for security and
freedom”
“We know:
War means (is) peace”
www.flickr.com/photos/41501796@N06/3823362599/
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9. Defining Links
Followers are a questionable indicator of
influence (Cha et al: “The Million Follower Fallacy”)
Intentional, meaningful interaction as a link:
@-messages and quotes (re-tweets)
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10. Network Structure
Distribution of Incoming Links (cumulative)
Degree Distribution Degree Distribution
1e+00
little connected
1.0
users make up
the majority
1e-01
0.8
cumulative frequency
0.6
1e-02
0.4
highly connected
1e-03
users are
infrequent
0.2
1e-04
0.0
1 10 100 1000 10000 0 2000 4000 6000 8000
in-degree in-degree
(Clustering Coefficient C = .045 Random Erdös-Rényi Graph C = .001)
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11. The Visible Core
∑ interactive messages per user / ranked
30000
@-Messages Retweets
22500
15000
7500
0
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12. «The Rich get Richer»
Follower gain over sample timespan
correlates with intensity of interaction
(Spearman’s Rho rs = .54, two-tailed p ≃ 0)
Corresponds to “preferential attachement”
theory on network growth
New participants attach (follow) to most visible nodes
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13. Content Analysis
Hand-coded a sample from each of the 50
most prominent users
Prominence: Volume of meaningful
communication (direct messages + re-tweets)
Coding for content topic, references, links
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14. Top 50 users
6
6
The Pirate Party
The Green Party
3
Jörg Tauss
35
Politicians / Parties Media Media-like Blogs Personal Accounts 14

15. Content Typology
Mindcasting, Lifecasting, Workcasting
personal intellectual work-related automatic
15

16. Content Typology
400
300
200
100
0
political national politics regional politics
link to party website re-tweet directed message
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17. Results
Reach on twitter is very dependent on a small
group of users (new gatekeepers)
Preferential attachement makes entry into
twitter ecosystem difficult
Politicians are only successful if they attach
to existing topics/trends/conventions? (e.g.
Jörg Tauss, Piratenpartei)
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18. Results II
Dedicated “political” communities do not
play a significant role.
Dedicated “political” users do (mostly) not
play a significant role.
Political communication happens ad-hoc in
issue-driven topics among non-political
tweets and topics.
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19. Topical Interaction
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20. Politics as One Among Many Topics
Translated:
“I’ll be glad once the election campaigns
are over and we can all like each other
again. Especially once the dull discussions
come to an end.” 20

21. Thank you
21