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Quantitative methods to measure the participation to parliamentary debate anddiscourse of elected Members of Parliament (MPs) and the parties they belong toare lacking. This is an exploratory study in which we propose the developmentof a new approach for a quantitative analysis of such participation. We utilizethe New Zealand government's digital Hansard database to construct a topicmodel of parliamentary speeches consisting of nearly 40 million words in theperiod 2003-2016. A Latent Dirichlet Allocation topic model is implemented inorder to reveal the thematic structure of our set of documents. This generativestatistical model enables the detection of major themes or topics that arepublicly discussed in the New Zealand parliament, as well as permitting theirclassification by MP. Information on topic proportions is subsequently analyzedusing a combination of statistical methods. We observe patterns arising fromtime-series analysis of topic frequencies which can be related to specificsocial, economic and legislative events. We then construct a bipartite networkrepresentation, linking MPs to topics, for each of four parliamentary terms inthis time frame. We build projected networks (onto the set of nodes representedby MPs) and proceed to the study of the dynamical changes of their topology,including community structure. By performing this longitudinal networkanalysis, we can observe the evolution of the New Zealand parliamentary topicnetwork and its main parties in the period studied.

Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches