From Talk to Action with Accountability: Monitoring the Public Discussion of Finnish Decision-Makers with Deep Neural Networks and Topic Modelling
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Decades of research on climate have provided a consensus that human activity has changed the climate and we are currently heading into a climate crisis. Many tools and methods, some of which utilize machine learning, have been developed to monitor, evaluate, and predict the changing climate and its effects on societies. However, the mere existence of tools and increased awareness have not led to swift action to reduce emissions and mitigate climate change. Politicians and other policy makers lack the initiative to move from talking about the climate to concrete climate action. In this work, we contribute to the efforts of holding decision makers accountable by describing a system which digests politicians' speeches and statements into a topic summary. We propose a multi-source hybrid latent Dirichlet allocation model which can process the large number of publicly available reports, social media posts, speeches, and other documents of Finnish politicians, providing transparency and accountability towards the general public.
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