Te Pūnaha Matatini, University of Auckland, Auckland, New Zealand.
Te Pūnaha Matatini, School of Chemical and Physical Sciences, Victoria University of Wellington, Wellington, New Zealand.
PLoS One. 2018 Jun 20;13(6):e0199072. doi: 10.1371/journal.pone.0199072. eCollection 2018.
Quantitative methods to describe the participation to debate of Members of Parliament and the parties they belong to are lacking. Here we propose a new approach that combines topic modeling with complex networks techniques, and use it to characterize the political discourse at the New Zealand Parliament. We implement a Latent Dirichlet Allocation model to discover the thematic structure of the government's digital database of parliamentary speeches, and construct from it two-mode networks linking Members of the Parliament to the topics they discuss. Our results show how topic popularity changes over time and allow us to relate the trends followed by political parties in their discourses with specific social, economic and legislative events. Moreover, the community analysis of the two-mode network projections reveals which parties dominate the political debate as well as how much they tend to specialize in a small or large number of topics. Our work demonstrates the benefits of performing quantitative analysis in a domain normally reserved for qualitative approaches, providing an efficient way to measure political activity.
定量方法来描述国会议员及其所属政党参与辩论的情况还很缺乏。在这里，我们提出了一种新的方法，将主题建模与复杂网络技术相结合，并将其应用于描述新西兰议会的政治话语。我们实施了一个潜在狄利克雷分配模型来发现政府的议会演讲数字数据库的主题结构，并从其中构建出将议员与他们讨论的主题联系起来的二模网络。我们的结果展示了主题的流行度如何随时间变化，并使我们能够将政党在其话语中遵循的趋势与特定的社会、经济和立法事件联系起来。此外，二模网络投影的社区分析揭示了哪些政党主导政治辩论，以及它们在少数或多数主题上倾向于专业化的程度。我们的工作证明了在通常保留给定性方法的领域进行定量分析的好处，为衡量政治活动提供了一种有效的方法。
