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Applying social network analysis to Parliamentary Proceedings

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Automatic discovery of meaningful cliques

Automatic discovery of meaningful cliques

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  • 1. Applying social network analysisto Parliamentary ProceedingsAutomatic discovery of meaningful cliquesAuthor:Justin van WeesSupervisors:Dr. Maarten MarxDr. Johan van DoornikJune 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
  • 5. <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>
  • 6. A simple graph
  • 7. A directed graph
  • 8. 42 32 21 12 84 100 10 8 15A weighted directed graph
  • 9. .8&&%9":3()(;&/%3<"3=()(,- 8 456",,%#()(+77()(,- 8 2 4 !"#$%&()(**+()(,- 24,"2()(B1$A()(,- >":#%1%#$)456/?2%3()(@+A()(,- .//0%&1/&2()(0/1%&3,%32 A single debate represented in a graph
  • 10. Debates during Cabinet Kok II
  • 11. A community A group of nodes that are relatively denselyconnected to each other but sparsely connected to other dense groups in the network
  • 12. A k-clique (k = 4) K-clique communities (k = 4)
  • 13. 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
  • 14. What?Results and conclusion
  • 15. General network statistics of Kok II No distinction With distinction between MP/MG between MP/MG roles rolesNodes 211 218Edges 3594 3615Density 0,081 0,076
  • 16. 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
  • 17. 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
  • 18. Discussion... and future research
  • 19. • 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?
  • 20. Questions?For detailed results, datasets and programs see: http://justinvanwees.nl/goto/bachelorscriptie

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