Applying social network analysisto Parliamentary ProceedingsAutomatic discovery of meaningful cliquesAuthor:Justin van Wee...
Why?Motivation and research question
Research question    Can we discover communities of politicians      that debate on a speci c policy area?                ...
How?Background and methodology
<root>  <docinfo>...</docinfo>  <meta>...</meta>  <proceedings>    <topic>       <scene type="speaker" speaker="Hamer" par...
A simple graph
A directed graph
42                  32                                21                       12        84  100             10           ...
.8&&%9":3()(;&/%3<"3=()(,-                                               8  456",,%#()(+77()(,-                           ...
Debates during Cabinet Kok II
A community   A group of nodes that are relatively denselyconnected to each other but sparsely connected to       other de...
A k-clique (k = 4)   K-clique communities (k = 4)
Finding issues that a community is discussing•   Retrieve all ‘community text’•   Tokenized at word level•   Lemmatize•   ...
What?Results and conclusion
General network statistics of Kok II              No distinction With distinction             between MP/MG between MP/MG ...
Finding k-clique communties•   By default, found groups are note ‘cohesive’•   Filter out ‘noise’ by setting a threshold o...
Finding k-clique communties•   All k-clique communities could be traced back to a single    policy area•   Except for more...
Discussion... and future research
•   Method for setting edge weight threshold•   Reviewing of k-cliques done by single person•   Used four years of data, s...
Questions?For detailed results, datasets and programs see: http://justinvanwees.nl/goto/bachelorscriptie
Applying social network analysis to Parliamentary Proceedings
Applying social network analysis to Parliamentary Proceedings
Applying social network analysis to Parliamentary Proceedings
Applying social network analysis to Parliamentary Proceedings
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Applying social network analysis to Parliamentary Proceedings

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Automatic discovery of meaningful cliques

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Applying social network analysis to Parliamentary Proceedings

  1. 1. Applying social network analysisto Parliamentary ProceedingsAutomatic discovery of meaningful cliquesAuthor:Justin van WeesSupervisors:Dr. Maarten MarxDr. Johan van DoornikJune 23, 2011
  2. 2. Why?Motivation and research question
  3. 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. 4. How?Background and methodology
  5. 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. 6. A simple graph
  7. 7. A directed graph
  8. 8. 42 32 21 12 84 100 10 8 15A weighted directed graph
  9. 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. 10. Debates during Cabinet Kok II
  11. 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. 12. A k-clique (k = 4) K-clique communities (k = 4)
  13. 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. 14. What?Results and conclusion
  15. 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. 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. 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. 18. Discussion... and future research
  19. 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. 20. Questions?For detailed results, datasets and programs see: http://justinvanwees.nl/goto/bachelorscriptie
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