This document summarizes a study applying social network analysis techniques to discover communities of politicians debating specific policy areas in Dutch parliamentary proceedings. The study represents debates as weighted, directed graphs and uses k-clique community detection and language modeling to identify groups discussing single policy topics. The results show most communities could be traced to a single policy area, demonstrating the potential of these methods for automatic discovery of issue-based communities in political networks. The study also discusses avenues for future related research.

1. Applying social network analysis
to Parliamentary Proceedings
Automatic discovery of meaningful cliques
Author:
Justin van Wees
Supervisors:
Dr. Maarten Marx
Dr. Johan van Doornik
June 23, 2011

2. Why?
Motivation and research question

3. Research question
Can we discover communities of politicians
that debate on a speci c policy area?
Motivation
• It’s unknown which member is responsible for a certain
policy area
• Discover what issues are discussed within a policy area
• Serve as example application of social network analysis
techniques

4. How?
Background and methodology

7. <root>
<docinfo>...</docinfo>
<meta>...</meta>
<proceedings>
<topic>
<scene type="speaker" speaker="Hamer" party="PvdA" function="Mevrouw"
role="mp" title="Mevrouw Hamer (PvdA)" MPid="02221">
<speech party="PvdA" speaker="Hamer" function="Mevrouw"
role="mp" MPid="02221">
<p>Dat is helemaal niet waar. U bewijst nu voor de derde keer
dat u niet ...</p>
</speech>
<speech type="interruption" party="Verdonk" speaker="Verdonk"
function="Mevrouw" role="mp" MPid="02995">
<p>Mag ik even uitpraten? Dank u. Zo werkt dat, gewoon fatsoen.
Dank u wel. [...]</p>
</speech>
</scence>
</topic>
</proceedings>
</root>

8. A simple graph

9. A directed graph

10. 42
32
21
12
84
100
10
8
15
A weighted directed graph

11. .8&&%9":3()(;&/%3<"3='()(,-
8
456",,%#()(+77()(,-
8
2
4
!"#$%&'()(**+()(,-
2
4,"2'()(B1$A()(,-
>":#%1%#$)456/?2%3()(@+A()(,-
.//0%&1/&'2()(0/1%&3,%32
A single debate represented in a graph

12. Debates during Cabinet Kok II

13. A community
A group of nodes that are relatively densely
connected to each other but sparsely connected to
other dense groups in the network

14. A k-clique (k = 4) K-clique communities (k = 4)

15. Finding issues that a community is discussing
• Retrieve all ‘community text’
• Tokenized at word level
• Lemmatize
• Use parsimonious language models to nd most
‘descriptive’ terms

16. What?
Results and conclusion

17. General network statistics of Kok II
No distinction With distinction
between MP/MG between MP/MG
roles roles
Nodes 211 218
Edges 3594 3615
Density 0,081 0,076

18. Finding k-clique communties
• By default, found groups are note ‘cohesive’
• Filter out ‘noise’ by setting a threshold on edge weights
• At 15 interruptions: 197 nodes, 741 edges, 31 k-clique
communities

21. Finding k-clique communties
• All k-clique communities could be traced back to a single
policy area
• Except for more ‘general’ policy areas
• 92% of the community members directly related to the policy
area covered by the community
• 85% of top 20 ‘issue terms’ relevant to policy area
• K-clique community detection and parsimonious language
models are successful methods for automatic discovery of
communities within debate networks

22. Discussion
... and future research

23. • Method for setting edge weight threshold
• Reviewing of k-cliques done by single person
• Used four years of data, shorter time-window possible?
• Focused on Cabinet Kok II, what about other (earlier)
cabinets?
• Completely different data?

24. Questions?
For detailed results, datasets and programs see:
http://justinvanwees.nl/goto/bachelorscriptie