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
<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>
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
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
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
• 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