Look Who's Talking: Bipartite Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches
Ben Curran, Kyle Higham, Elisenda Ortiz, Demival Vasques Filho

TL;DR
This study develops a bipartite network approach to analyze parliamentary speeches, revealing thematic structures and participation patterns of MPs over time using a large-scale topic model and network analysis.
Contribution
It introduces a novel bipartite network method for analyzing parliamentary debate participation and thematic evolution over multiple terms.
Findings
Identified major themes discussed in parliament.
Detected patterns related to social, economic, and legislative events.
Analyzed the evolution of party-based community structures.
Abstract
Quantitative methods to measure the participation to parliamentary debate and discourse of elected Members of Parliament (MPs) and the parties they belong to are lacking. This is an exploratory study in which we propose the development of a new approach for a quantitative analysis of such participation. We utilize the New Zealand government's digital Hansard database to construct a topic model of parliamentary speeches consisting of nearly 40 million words in the period 2003-2016. A Latent Dirichlet Allocation topic model is implemented in order to reveal the thematic structure of our set of documents. This generative statistical model enables the detection of major themes or topics that are publicly discussed in the New Zealand parliament, as well as permitting their classification by MP. Information on topic proportions is subsequently analyzed using a combination of statistical…
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