Talking Point based Ideological Discourse Analysis in News Events

• URL: http://arxiv.org/abs/2504.07400v1
• Date: Thu, 10 Apr 2025 02:52:34 GMT
• Title: Talking Point based Ideological Discourse Analysis in News Events
• Authors: Nishanth Nakshatri, Nikhil Mehta, Siyi Liu, Sihao Chen, Daniel J. Hopkins, Dan Roth, Dan Goldwasser,
• Abstract summary: We propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.<n>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.<n>We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation.
• Score: 62.18747509565779
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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