Talking Point based Ideological Discourse Analysis in News Events
This work addresses the problem of understanding abstract ideological views in news for researchers and analysts, offering a novel method but with incremental improvements over existing discourse analysis techniques.
The paper tackles the challenge of analyzing ideological discourse in news events by proposing a framework that uses talking points to capture interactions between entities, roles, and media frames, resulting in improved performance on ideology and partisan classification tasks with automated and human validation.
Analyzing ideological discourse even in the age of LLMs remains a challenge, as these models often struggle to capture the key elements that shape real-world narratives. Specifically, LLMs fail to focus on characteristic elements driving dominant discourses and lack the ability to integrate contextual information required for understanding abstract ideological views. To address these limitations, we propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events. Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion. It then constructs a vocabulary of repeating themes - prominent talking points, that are used to generate ideology-specific viewpoints (or partisan perspectives). We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation. Additionally, we demonstrate straightforward applicability of our framework in creating event snapshots, a visual way of interpreting event discourse. We release resulting dataset and model to the community to support further research.