Overview of findings presented at #GOR19 in Cologne on March, 8th, 2019. Networks, clusters, influencers - What can we learn from applying social network analysis (SNA), content analysis and visual analysis to a list of Twitter handles? This presentation is based on the research project "Mapping Political Networks" at the Social Media Research Foundation.

1. Insights from mapping the Twitter network
of the German Bundestag
by Harald Meier and Arber Ceni

2. 19. BUNDESTAG: TWITTER USEAGE
2
Party Color Seats
Twitter
users
Twitter users
per seat
CDU/CSU 246 131 53 %
SPD 153 123 80 %
AfD 92 85 92 %
FDP 80 72 90 %
Die Linke 69 60 87 %
B90/Die Grünen 67 64 96 %
no affiliation 2 2 100 %
All 709 537 76 %
https://www.bundestag.de/parlament/plenum/sitzverteilung_19wp

3. METHODOLOGY AND DATASETS
Methodology
1. Create a list with Twitter accounts
2. Download network data (Twitter Search API)
3. Social network, content and visual analysis
4. Create several network subsets
Network Datasets
▪ Dataset 1: Oct 12th, 2018
▪ Dataset 2: Dec 19th, 2018
▪ Dataset 3: Feb 20th, 2019
▪ More datasets in NodeXL Graph Gallery:
3https://nodexlgraphgallery.org/Pages/Default.aspx?search=%23nxlbundestag

4. TWEET FREQUENCY – FEB-20-2019
4

5. EDGE RELATIONSHIPS – FEB-20-2019
5

6. NETWORK MAPS CREATED FROM ONE DATASET
6
1. Full Network Map
2. Internal Network Map
3. Party Network Map
4. Influencer Map
5. Party Interaction Map

7. 7
Dataset 1:
Oct-12-2018
Internal Network

8. Dataset 1:
Oct-12-2018
Full Network
8

9. 9
Dataset 2:
Dec-19-2018
Internal Network

10. 10
Dataset 2:
Dec-19-2018
Full Network

11. 11
Dataset 3:
Feb-20-2019
Internal Network

12. 12
Dataset 3:
Feb-20-2019
Full Network

13. 13
Search term AfD
Feb-05-2019
Full Network

14. 14
Search terms
219a OR Abtreibung
Dec-19-2019
Full Network

15. PARTY INTERACTION MAPS
15
Dataset 1: Oct-12-2018 Dataset 2: Dec-19-2018 Dataset 3: Feb-20-2019

16. GROUP NETWORK METRICS
16
GROUP SENTIMENT
Reciprocated Vertex Pair Ratio Graph Density
Party Oct-12-2018 Dec-19-2018 Feb-20-2019 Oct-12-2018 Dec-19-2018 Feb-20-2019
AfD 14.7 % 14.1 % 8.5 % 5.8 % 5.8 % 5.7 %
B90/Die Grünen 23.8 % 21.7 % 21.0 % 8.1 % 9.6 % 8.7 %
CDU/CSU 5.8 % 11.1 % 13.0 % 2.0 % 2.6 % 2.5 %
Die Linke 14.8 % 9.9 % 15.8 % 7.5 % 4.6 % 6.0 %
FDP 18.8 % 14.2 % 15.8 % 4.3 % 4.8 % 5.1 %
SPD 11.4 % 8.8 % 10.8 % 2.7 % 2.9 % 3.2 %
Positive Word Percentage (%) Negative Word Percentage (%)
Party Oct-12-2018 Dec-19-2018 Feb-20-2019 Oct-12-2018 Dec-19-2018 Feb-20-2019
AfD 5.1 % 4.5 % 5.0 % 2.9 % 3.0 % 2.7 %
B90/Die Grünen 5.4 % 6.2 % 5.9 % 2.4 % 2.4 % 2.4 %
CDU/CSU 6.6 % 5.8 % 6.6 % 1.7 % 2.0 % 2.0 %
Die Linke 5.2 % 4.8 % 4.9 % 2.7 % 3.2 % 3.0 %
FDP 5.1 % 5.0 % 6.2 % 2.6 % 2.7 % 2.9 %
SPD 6.4 % 6.5 % 6.8 % 1.9 % 2.4 % 2.1 %

17. INTERNAL INFLUENCERS – OCT-12-2018
17
Rank Twitter Handle Party In-Degree
Out-
Degree
Betweenness
Centrality
Eigenvector
Centrality
1 c_lindner FDP 59 18 25740.531 0.018
2 kahrs SPD 25 32 15147.732 0.014
3 katarinabarley SPD 31 2 8493.334 0.010
4 mgrossebroemer CDU/CSU 21 16 8234.326 0.008
5 larsklingbeil SPD 25 9 8033.471 0.008
6 stbrandner AfD 10 26 7976.457 0.010
7 peteraltmaier CDU/CSU 32 1 7956.670 0.010
8 sven_kindler B90/Die Grünen 21 18 6686.655 0.011
9 udohemmelgarn AfD 7 36 6365.859 0.011
10 rbrinkhaus CDU/CSU 31 1 6311.749 0.005
11 dorisachelwilm Die Linke 12 21 5432.233 0.007
12 johannesvogel FDP 7 19 5137.178 0.005
13 rkiesewetter CDU/CSU 4 25 4855.530 0.005
14 alice_weidel AfD 26 1 4689.686 0.009
15 dietmarbartsch Die Linke 14 6 4584.795 0.005
Betweenness
Centrality
Eigenvector
Centrality

18. INTERNAL INFLUENCERS – OCT-12-2018
18
Rank Twitter Handle Party In-Degree
Out-
Degree
Betweenness
Centrality
Eigenvector
Centrality
1 kahrs SPD 25 515 3954118.61 0.009
2 eskensaskia SPD 7 240 1619942.49 0.003
3 anked Die Linke 12 176 1219345.88 0.002
4 udohemmelgarn AfD 7 296 1169617.79 0.009
5 djanecek B90/Die Grünen 13 142 827590.17 0.002
6 renatekuenast B90/Die Grünen 11 145 805045.48 0.003
7 c_lindner FDP 61 74 735614.75 0.005
8 rkiesewetter CDU/CSU 4 135 726132.40 0.002
9 schneider_afd AfD 7 234 658452.66 0.008
10 drandreasnick CDU/CSU 4 132 633946.34 0.002
11 lieblingxhain B90/Die Grünen 7 127 604051.51 0.002
12 frankschwabe SPD 8 101 578840.63 0.002
13 lisapaus B90/Die Grünen 7 128 542398.86 0.002
14 olliluksic FDP 11 130 507873.19 0.003
15 miro_spd SPD 10 80 497234.59 0.002
Betweenness
Centrality
Eigenvector
Centrality

19. EXTERNAL INFLUENCERS – OCT-12-2018
19
Rank Twitter Handle Category
Betweenness
Centrality
1 cducsubt Party 1174612.976
2 spdbt Party 939511.988
3 welt News/Media 731024.365
4 spiegelonline News/Media 471645.219
5 gruenebundestag Party 386620.708
6 die_gruenen Party 385938.244
7 tagesspiegel News/Media 312888.187
8 cdu Party 309163.666
9 csu Party 303999.802
10 dielinke Party 265409.586
11 sz News/Media 239684.103
12 spdde Party 239172.866
13 afdimbundestag Party 237197.374
14 afd Party 220071.677
15 faznet News/Media 212572.647
16 fdpbt Party 190287.677
17 arminlaschet Politician 186602.259
18 junge_union Party 162198.381
19 _a_k_k_ Politician 157753.422
20 fdp Party 151872.034
The most influential Twitter users
outside the Bundestag are related
national party accounts and large
news media outlets.

20. INTERNAL NETWORK: TOP INFLUENCERS
20
Dataset 2:
Dec-19-2018
Internal Network

21. Dataset 3:
Feb-20-2019
Full Network
21

22. Summary
▪ Clusters
▪ Changing coalition clusters
▪ Ambiguity between internal and full network data
▪ Party Interaction
▪ A lot of talk about the AfD, very little talk with the AfD
▪ B90/Die Grünen is most unified party
▪ Influencers
▪ Internal: High ranking party officials and top tweeters
▪ External: Media outlets, Party accounts, regional politicians
Further research
▪ We need more network maps!
▪ Compare to other platforms (e.g. Facebook Page Like Networks)
▪ Compare to other parliaments
22

23. Questions? Please send an email to harald@smrfoundation.org
All network maps related to the Bundestag can be found here:
https://nodexlgraphgallery.org/Pages/Default.aspx?search=%23nxlbundestag
Please visit the following website for more information:
https://www.smrfoundation.org/2018/09/14/research-project-mapping-political-networks/
This case study is part of the research project
Mapping Political Networks
at the Social Media Research Foundation

24. Social Media Network
VERTEX Twitter/Facebook user, hashtag, post
EDGE retweet, share, react, mention, reply
APPENDIX: WHAT IS A NETWORK?
Network
A network consists of VERTICES and EDGES.
An EDGE is a connection between two VERTICES.

25. [Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
APPENDIX: NETWORK SHAPES
PEW Report: Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. PEW Research Report 2014:
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/

26. 1
[Divided]
Polarized Crowds
[Unified]
Tight Crowd
[Fragmented]
Brand Clusters
[Clustered]
Community Clusters
[In-Hub & Spoke]
Broadcast Network
[Out-Hub & Spoke]
Support Network
APPENDIX: NETWORK SHAPES
PEW Report: Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters. PEW Research Report 2014:
http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/