Extracting narrative signals from public discourse: a network-based approach

2024-11-01

AI Summary

This study addresses the challenge of systematically extracting implicit narrative signals—such as polarization and misinformation—from public discourse for empirical political narrative analysis. We propose a graph-structured modeling approach grounded in Abstract Meaning Representation (AMR) to automatically identify three core narrative elements: actors, events, and perspective-taking. Integrating narratological situation theory, we design heuristic filtering and network-guided retrieval mechanisms, enabling unsupervised and interpretable political narrative discovery. Crucially, we formalize narrative intelligence as retrievable graph signals for the first time, supporting synergistic distant reading and close reading in narrative reconstruction. Experiments on EU State of the Union addresses (2010–2023) demonstrate the method’s effectiveness in uncovering diachronic ideological narrative evolution paths, with strong validity, reproducibility, and real-world applicability.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Narratives are key interpretative devices by which humans make sense of political reality. As the significance of narratives for understanding current societal issues such as polarization and misinformation becomes increasingly evident, there is a growing demand for methods that support their empirical analysis. To this end, we propose a graph-based formalism and machine-guided method for extracting, representing, and analyzing selected narrative signals from digital textual corpora, based on Abstract Meaning Representation (AMR). The formalism and method introduced here specifically cater to the study of political narratives that figure in texts from digital media such as archived political speeches, social media posts, transcripts of parliamentary debates, and political manifestos on party websites. We approach the study of such political narratives as a problem of information retrieval: starting from a textual corpus, we first extract a graph-like representation of the meaning of each sentence in the corpus using AMR. Drawing on transferable concepts from narratology, we then apply a set of heuristics to filter these graphs for representations of 1) actors and their relationships, 2) the events in which these actors figure, and 3) traces of the perspectivization of these events. We approach these references to actors, events, and instances of perspectivization as core narrative signals that allude to larger political narratives. By systematically analyzing and re-assembling these signals into networks that guide the researcher to the relevant parts of the text, the underlying narratives can be reconstructed through a combination of distant and close reading. A case study of State of the European Union addresses (2010 -- 2023) demonstrates how the formalism can be used to inductively surface signals of political narratives from public discourse.

Problem

• Extracting narrative signals from digital political texts using computational methods
• Analyzing actors, events and perspectivization in political discourse networks
• Reconstructing political narratives through automated distant and close reading

Innovation

• Graph-based formalism using Abstract Meaning Representation
• Heuristics filtering AMR graphs for narrative signals
• Network assembly guiding distant and close reading