In an era of rapid global change, parliaments face unprecedented challenges in addressing complex issues that transcend national borders. This panel invites contributions that leverage cutting-edge computational methods to explore how legislative bodies, political parties, and individual politicians are adapting their strategies, communications, and policy-making processes to these challenges.
The panel will explore the evolving nature of political discourse and decision-making in the face of transformative forces such as climate change, demographic shifts, technological advancements, and changing economic paradigms. Accepted papers will employ advanced natural language processing (NLP) techniques and machine learning algorithms to examine how these phenomena are reshaping political agendas, party manifestos, and legislative debates across diverse political systems. Importantly, we welcome work focusing on different countries and languages beyond the English-speaking world.
The panel aims to delve into the intricate relationships between electoral systems, institutional designs, and political communication strategies. By comparing different institutional contexts through sophisticated text-mining techniques, we shall uncover how varying institutional structures and party organisations shape the way politicians engage with one another and with constituents to address pressing global issues. Papers will investigate how global initiatives are integrated into domestic political narratives and legislative processes and how they affect the dynamics between governing parties and opposition forces. Importantly, we welcome papers focusing on different countries and different languages.
The panel also aims to explore the internal mechanisms of policy formation within political parties and legislative committees. This includes an examination of how intra-party deliberations and cross-party negotiations influence public debates and legislative outcomes, particularly in coalition governments and multi-party systems. Papers that apply novel machine-learning approaches to large-scale textual datasets to uncover these patterns and trends are particularly welcome.
Through a combination of state-of-the-art quantitative text analysis, machine learning techniques, and natural language processing, this panel aims to provide fresh, data-driven insights into the adaptive capacities of democratic institutions in the face of global challenges. By bringing together diverse perspectives on political communication, institutional design, and policy-making processes, underpinned by rigorous computational analysis, we seek to contribute to a broader understanding of how modern democracies are evolving to meet the complex demands of an interconnected world.