This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set covering all speeches and debates in the UK House of Commons between 1975 and 2014. By the use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show that both members and parties feature specific roles within the system, consistent over time, and extract global patterns indicating levels of political cohesion. Our results provide a wide array of novel hypotheses about the complex dynamics of political systems, with valuable policy applications.
['Stefano Gurciullo', 'Michael J. Smallegan', 'Maria Pereda', 'Federico Battiston', 'Alice Patania', 'Sebastian Poledna', 'Daniel Hedblom', 'Bahattin Tolga Oztan', 'Alexander Herzog', 'Peter John', 'Slava Mikhaylov']
Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates