From Talk to Action with Accountability: Monitoring the Public Discussion of Policy Makers with Deep Neural Networks and Topic Modelling

TL;DR

This paper introduces MuSTAS, a multi-source topic aggregation system using hybrid LDA to summarize policymakers' discussions on climate change, enhancing transparency and accountability.

Contribution

It presents a novel multi-source hybrid LDA approach for aggregating and summarizing policy discussions from various sources on climate change.

Findings

Effective summarization of multi-source policy discussions.

Enhanced transparency in climate policy discourse.

Facilitates public accountability for policymakers.

Abstract

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. While public discussion and research efforts on climate change mitigation have increased, potential solutions need to not only be discussed but also effectively deployed. For preventing mismanagement and holding policy makers accountable, transparency and degree of information about government processes have been shown to be crucial. However, currently the quantity of information about climate change discussions and the range of sources make it increasingly difficult for the public and civil society to maintain an overview to hold politicians accountable. In response, we propose a multi-source topic aggregation system (MuSTAS) which processes policy makers speech and rhetoric from several publicly available sources into an easily digestible topic summary. MuSTAS uses novel multi-source hybrid latent Dirichlet allocation to model topics from a variety of documents. This topic digest will serve the general public and civil society in assessing where, how, and when politicians talk about climate and climate policies, enabling them to hold politicians accountable for their actions to mitigate climate change and lack thereof.