Breaking the Barrier

Interactive Election Campaign Communication on Twitterduring the German General Election 2009

Pascal JürgensU of Mainz, Germanypascal.juergens@gmail.com

Andreas JungherrU of Bamberg, Germanyandreas.jungherr@gmail.com

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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

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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Data CollectionBootstrap: 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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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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Election Day

Twitter Outage

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Message Volume

Election Day

TV debate

Poster remixes

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Political Message Volume

8www.flickr.com/photos/41501796@N06/3823362599/

“We know:War means (is) peace”

“We are strong enough for security and

freedom”

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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Network Structure

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0 2000 4000 6000 8000

0.0

0.2

0.4

0.6

0.8

1.0

Degree Distribution

in-degree

cum

ulat

ive

frequ

ency

1 10 100 1000 10000

1e-04

1e-03

1e-02

1e-01

1e+00

Degree Distribution

in-degree

cum

ulat

ive

frequ

ency

highly connectedusers are

infrequent

little connected users make up the majority

(Clustering Coefficient C = .045 Random Erdös-Rényi Graph C = .001)

Distribution of Incoming Links (cumulative)

The Visible Core

110

7500

15000

22500

30000

∑ interactive messages per user / ranked

@-Messages Retweets

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 growthNew participants attach (follow) to most visible nodes

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«The Rich get Richer»

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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Top 50 users

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35

66

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Politicians / Parties Media Media-like Blogs Personal Accounts

The Pirate PartyThe Green Party

Jörg Tauss

Content Typology

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personal intellectual work-related automatic

Mindcasting, Lifecasting, Workcasting

Content Typology

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0

100

200

300

400

political national politics regional politicslink to party website re-tweet directed message

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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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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Topical Interaction

Politics as One Among Many Topics

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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.”

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Thank you