Applying social network analysis to Parliamentary ProceedingsAutomatic discovery of meaningful cliques

Author:Justin van Wees

Supervisors:Dr. Maarten MarxDr. Johan van Doornik

June 23, 2011

Why?Motivation and research question

Can we discover communities of politicians that debate on a speci!c policy area?

Research question

• 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

Motivation

How?Background and methodology

<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>

A simple graph

A directed graph

A weighted directed graph

10

100

8

15

12

2132

84

42

A single debate represented in a graph

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Debates during Cabinet Kok II

A group of nodes that are relatively densely connected to each other but sparsely connected to

other dense groups in the network

A community

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

• Retrieve all ‘community text’

• Tokenized at word level

• Lemmatize

• Use parsimonious language models to !nd most ‘descriptive’ terms

Finding issues that a community is discussing

What?Results and conclusion

General network statistics of Kok II

No distinction between MP/MG

roles

With distinction between MP/MG

roles

Nodes 211 218

Edges 3594 3615

Density 0,081 0,076

• 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

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

Finding k-clique communties

Discussion... and future research

• 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?

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