Enterprise AI Analysis
Extracting narrative signals from public discourse: a network-based approach
Author(s): Armin Pournaki & Tom Willaert
Publication Date: 2025-11-19
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 conceptualize these political narratives as a type of ontological narratives: stories by which actors position themselves as political beings, and which are akin to political worldviews in which actors present their normative vision of the world, or aspects thereof. 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.
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Methodology
This paper introduces a novel graph-based formalism and a machine-guided method for extracting, representing, and analyzing narrative signals from digital textual corpora. Leveraging Abstract Meaning Representation (AMR), the method abstracts away syntactic specificities to focus on semantic meaning, enabling robust extraction of actors, events, and their relationships. This approach offers a flexible and less restrictive alternative to rule-based parsers, allowing for a more inductive discovery of narrative structures.
Applications
The formalism is specifically tailored for the study of political narratives in digital media, including speeches, social media, and manifestos. A case study on State of the European Union addresses (2010-2023) demonstrates its ability to inductively surface signals of political narratives. The extracted signals, such as actor constellations and their goals, can be re-assembled into networks for 'guided close reading', enabling researchers to reconstruct underlying narratives.
Theoretical Foundations
Drawing on structuralist and cognitive narratology, the paper defines political narratives as representations of reality through causally or temporally connected events involving actors with ascribed goals. It positions political narratives as ontological narratives that shape collective identities and worldviews. The work bridges narratological theory with computational methods, operationalizing concepts like actantial networks for empirical analysis of large corpora.
Enterprise Process Flow
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State of the European Union Addresses (2010-2023)
The case study demonstrates how the AMR-based method can surface meaningful traces of political narratives in a corpus of 12 annual SOtEU speeches. By analyzing the most frequent agents, patients, and predicates, the study reveals shifts in emphasis across different presidencies, such as Barroso's focus on economic union and Juncker's emphasis on job markets and solidarity. The method enables the identification of allusions to established narratives like the 'neoliberal' or 'leftist' narratives of European integration, facilitating a 'guided close reading' approach for deeper qualitative interpretation.
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Your Enterprise AI Roadmap
A strategic phased approach for seamless integration and maximum impact.
Phase 1: Discovery & Strategy
Initial consultation and needs assessment to define narrative analysis goals and scope. Data preparation and cleansing for target textual corpora. Development of custom AMR parsing and graph filtering rules tailored to specific domains.
Phase 2: Core System Deployment
Deployment of the AMR-based narrative extraction pipeline. Integration with existing data infrastructure. Initial training and calibration of the system with sample data to ensure optimal performance and accuracy.
Phase 3: Network Analysis & Insight Generation
Execution of network-based narrative analysis on full corpora. Generation of narrative trace tables, actantial networks, and goal/motive analyses. Iterative refinement of extraction heuristics based on initial insights and expert feedback.
Phase 4: 'Guided Close Reading' & Reporting
Development of interactive visualization tools for 'guided close reading'. Training of research teams on platform usage and interpretation of narrative signals. Comprehensive reporting and synthesis of macro-level narrative trends and micro-level textual evidence.
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