Post on 20-Jun-2015
description
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
2
Twitter and Politics
2
Twitter and Politics
The 2009 German General Election started with the impression of Obama’s online campaign fresh in mind
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
Twitter Overview
3
Twitter Overview»Micro-publishing« — publish short messages
3
Twitter Overview»Micro-publishing« — publish short messages
Re-Tweet — quote message including attribution
3
Twitter Overview»Micro-publishing« — publish short messages
Re-Tweet — quote message including attribution
Directed (@-)message — explicitly sent to a recipient
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
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)
3
Data Collection
4
Data CollectionBootstrap: List of political twitter users(Assembled from several websites compiling lists of politicians on twitter)
4
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)
4
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)
4
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
4
Dataset
5
Dataset
Three months prior to General Election
5
Dataset
Three months prior to General Election
A sample of Germany’s politically active twitter users — 33 048 individuals
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
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
5
Election Day
Twitter Outage
6
Message Volume
Election Day
TV debate
Poster remixes
7
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
9
Defining Links
Followers are a questionable indicator of influence (Cha et al: “The Million Follower Fallacy”)
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)
9
Network Structure
10
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
12
«The Rich get Richer»
Follower gain over sample timespan correlates with intensity of interaction(Spearman’s Rho rs = .54, two-tailed p ≃ 0)
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 growthNew participants attach (follow) to most visible nodes
12
«The Rich get Richer»
Content Analysis
13
Content Analysis
Hand-coded a sample from each of the 50 most prominent users
13
Content Analysis
Hand-coded a sample from each of the 50 most prominent users
Prominence: Volume of meaningful communication (direct messages + re-tweets)
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
13
Top 50 users
14
35
66
3
Politicians / Parties Media Media-like Blogs Personal Accounts
Top 50 users
14
35
66
3
Politicians / Parties Media Media-like Blogs Personal Accounts
The Pirate PartyThe Green Party
Jörg Tauss
Content Typology
15
personal intellectual work-related automatic
Mindcasting, Lifecasting, Workcasting
Results
16
Results
Reach on twitter is very dependent on a small group of users (new gatekeepers)
16
Results
Reach on twitter is very dependent on a small group of users (new gatekeepers)
Preferential attachement makes entry into twitter ecosystem difficult
16
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)
16
Results II
17
Results II
Dedicated “political” communities do not play a significant role.
17
Results II
Dedicated “political” communities do not play a significant role.
Dedicated “political” users do (mostly) not play a significant role.
17
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.
17
18
Topical Interaction
18
Topical Interaction
Politics as One Among Many Topics
19
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
Thank you
21
List of hashtags identifying political topicscducsu spd fdp gruene grüne piraten npd linke zensursula bundestagswahl petition politik cduremix09 btw09 wahl sst linkspartei union tvduell
22
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:
22
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:
22
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:
22
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:
22
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:
22
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)
22
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
23
0
100
200
300
400
political national politics regional politicslink to party website re-tweet directed message