Breaking the Barrier: Interactive Election Campaign Communication on Twitter
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Breaking the Barrier Interactive Election Campaign Communication on Twitter during the German General Election 2009 Pascal Jürgens U of Mainz, Germany [email protected] Andreas Jungherr U of Bamberg, Germany [email protected]
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Presentation from the General Online Research Conference 2010 in Pforzheim, Germany.
Transcript of Breaking the Barrier: Interactive Election Campaign Communication on Twitter
Breaking the Barrier
Interactive Election Campaign Communication on Twitterduring the German General Election 2009
Pascal JürgensU of Mainz, [email protected]
Andreas JungherrU of Bamberg, [email protected]
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Twitter and Politics
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Twitter and Politics
The 2009 German General Election started with the impression of Obama’s online campaign fresh in mind
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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
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Twitter Overview»Micro-publishing« — publish short messages
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Twitter Overview»Micro-publishing« — publish short messages
Re-Tweet — quote message including attribution
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Twitter Overview»Micro-publishing« — publish short messages
Re-Tweet — quote message including attribution
Directed (@-)message — explicitly sent to a recipient
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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
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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 Collection
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Data CollectionBootstrap: List of political twitter users(Assembled from several websites compiling lists of politicians on twitter)
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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)
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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)
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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
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Dataset
Three months prior to General Election
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Dataset
Three months prior to General Election
A sample of Germany’s politically active twitter users — 33 048 individuals
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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
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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
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Defining Links
Followers are a questionable indicator of influence (Cha et al: “The Million Follower Fallacy”)
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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
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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
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«The Rich get Richer»
Follower gain over sample timespan correlates with intensity of interaction(Spearman’s Rho rs = .54, two-tailed p ≃ 0)
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«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 growthNew participants attach (follow) to most visible nodes
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«The Rich get Richer»
Content Analysis
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Content Analysis
Hand-coded a sample from each of the 50 most prominent users
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Content Analysis
Hand-coded a sample from each of the 50 most prominent users
Prominence: Volume of meaningful communication (direct messages + re-tweets)
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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
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
Results
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Results
Reach on twitter is very dependent on a small group of users (new gatekeepers)
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Results
Reach on twitter is very dependent on a small group of users (new gatekeepers)
Preferential attachement makes entry into twitter ecosystem difficult
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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
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Results II
Dedicated “political” communities do not play a significant role.
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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.
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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
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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
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List of hashtags identifying political topicscducsu spd fdp gruene grüne piraten npd linke zensursula bundestagswahl petition politik cduremix09 btw09 wahl sst linkspartei union tvduell
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No Clear-Cut Style
Loadings: Factor1 Factor2 Factor3 Factor4 life -0.902 -0.316 -0.105 -0.267 mind 0.973 -0.199 work 0.996 auto 0.993 polit 0.485 0.188 0.800 0.110 level 0.370 0.167 0.678 ext 0.156 0.152 diag -0.118 -0.154 self -0.131
Explorative factor analysis not fruitful:
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No Clear-Cut Style
Loadings: Factor1 Factor2 Factor3 Factor4 life -0.902 -0.316 -0.105 -0.267 mind 0.973 -0.199 work 0.996 auto 0.993 polit 0.485 0.188 0.800 0.110 level 0.370 0.167 0.678 ext 0.156 0.152 diag -0.118 -0.154 self -0.131
Explorative factor analysis not fruitful:
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No Clear-Cut Style
Loadings: Factor1 Factor2 Factor3 Factor4 life -0.902 -0.316 -0.105 -0.267 mind 0.973 -0.199 work 0.996 auto 0.993 polit 0.485 0.188 0.800 0.110 level 0.370 0.167 0.678 ext 0.156 0.152 diag -0.118 -0.154 self -0.131
Explorative factor analysis not fruitful:
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No Clear-Cut Style
Loadings: Factor1 Factor2 Factor3 Factor4 life -0.902 -0.316 -0.105 -0.267 mind 0.973 -0.199 work 0.996 auto 0.993 polit 0.485 0.188 0.800 0.110 level 0.370 0.167 0.678 ext 0.156 0.152 diag -0.118 -0.154 self -0.131
Explorative factor analysis not fruitful:
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No Clear-Cut Style
Loadings: Factor1 Factor2 Factor3 Factor4 life -0.902 -0.316 -0.105 -0.267 mind 0.973 -0.199 work 0.996 auto 0.993 polit 0.485 0.188 0.800 0.110 level 0.370 0.167 0.678 ext 0.156 0.152 diag -0.118 -0.154 self -0.131
Explorative factor analysis not fruitful:
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No Clear-Cut Style
Loadings: Factor1 Factor2 Factor3 Factor4 life -0.902 -0.316 -0.105 -0.267 mind 0.973 -0.199 work 0.996 auto 0.993 polit 0.485 0.188 0.800 0.110 level 0.370 0.167 0.678 ext 0.156 0.152 diag -0.118 -0.154 self -0.131
Explorative factor analysis not fruitful:
(p-value 8.870000×10^-154, Chi square of 727.04 on 6 degrees of freedom)
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No Clear-Cut Style
Loadings: Factor1 Factor2 Factor3 Factor4 life -0.902 -0.316 -0.105 -0.267 mind 0.973 -0.199 work 0.996 auto 0.993 polit 0.485 0.188 0.800 0.110 level 0.370 0.167 0.678 ext 0.156 0.152 diag -0.118 -0.154 self -0.131
Content Typology
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political national politics regional politicslink to party website re-tweet directed message
