Narrative Classification Frameworks

Updated 29 January 2026

Narrative classification frameworks are formal methodologies that partition narrative data into interpretable, operationally defined categories using multi-layered taxonomies.
They integrate advanced computational techniques—such as event-chain clustering, clause decomposition, and hybrid LLM pipelines—to extract and label narrative structures with clarity and precision.
Evaluation employs rigorous metrics (F1, precision, recall, information-theoretic scores) and transparent interpretability, addressing challenges in domain adaptation and scalability.

A narrative classification framework provides a formal methodology for partitioning narratives into interpretable, operationally-defined categories, typically for the purposes of computational analysis, media studies, social science, or machine learning. Such frameworks rigorously specify labels, hierarchical relationships, feature extraction pipelines, and evaluation metrics for assigning structural or functional types to narrative data. Key developments have emerged, spanning event-centric clustering, clause-type decomposition, framing-role taxonomies, causal micro-narrative extraction, information-theoretic methods, and expert-aligned pipelines.

1. Formal Foundations: Definitions, Taxonomies, and Hierarchies

Narrative classification frameworks instantiate formal taxonomies grounded in theoretical or application-centric definitions. In "Media Framing through the Lens of Event-Centric Narratives," a narrative is formalized as a sequence of relational event chains within a document corpus $D$ (Das et al., 2024). Events $E$ are verb–object pairs $(v,o)$ , organized into single-hop triplets $(e_1, r, e_2)$ where $r \in \{ \text{Temporal}, \text{Causal} \}$ , subsequently clustered into $K$ high-level narrative themes $N = \{N_1, ..., N_K\}$ .

Alternative frameworks adopt multi-level taxonomies. The INDI-PROP schema annotates articles at three nested levels: article-level bias, fine-grained event-centric narrative frame (anchored in ideology and communicative intent), and span-level persuasive technique, yielding multi-layered semantic typing (Afroz et al., 3 Dec 2025).

Narrative clause-based frameworks leverage Labov’s model, decomposing personal narratives into orthogonal action, orientation, and evaluation clause types—each with distinctive operational roles in representing structure, context, and meaning (Saldias et al., 2020). High-level story arc frameworks (e.g., Freytag, Labov/Waletzky consolidation) assign sentence-level labels for segmentation: Abstract, Orientation, Complicating Action, Most Reportable Event, Resolution, Evaluation, etc., enabling fine granularity and temporal ordering (Li et al., 2017).

2. Feature Extraction, Representation, and Algorithmic Pipelines

Narrative classification systems integrate specialized extraction processes:

Event-centric approaches: Use syntactic pattern extraction (dependency parsing, Semantic Role Labeling) to identify verb-object events; relation classification via fine-tuned RoBERTa models identifies temporal and causal links; narrative chains are generated and embedded using SBERT then clustered with k-means++ (Das et al., 2024).
Clause-type classification: Segment narratives into clauses using Penn Treebank parses; labels assigned via neural CNNs leveraging GloVe and POS features, achieving high agreement and F1 (Saldias et al., 2020).
Graph-based frameworks: Model narrative as generative mixtures of actant (entity) networks with multitype edges; context-specific relationship distributions induce latent structure; subgraphs are extracted and clustered via Louvain or k-means, followed by KL-based verb scoring (Tangherlini et al., 2020).
Model-ensemble/hybrid pipelines: Leverage iterative LLM summarization and concept generation (chain-of-thought, Likert fit scoring), validated with human-expert refinement for reliability and interpretability (Kubli, 7 Feb 2025). Hierarchical prompt-based models (H3Prompt) employ a three-step LLM strategy to assign domain, main, and sub-narrative labels, with pivot translation for multilingual adaptation (Singh et al., 28 May 2025).

3. Classification, Labeling, and Decision Functions

Formally, input narratives (documents, clauses, or segments) are transformed into feature vectors reflecting event-chain frequencies, semantic embeddings, or clause-type proportions. Classification is typically achieved via:

Linear/multiclass logistic regression on cluster frequency vectors (after standardization) (Das et al., 2024).
Feed-forward neural network classifiers concatenating RoBERTa [CLS] embeddings with narrative features (cluster frequencies) as document vectors (Das et al., 2024).
Multi-label binary classification for narrative frames (BERT, Longformer), optimized with binary cross-entropy loss per frame label (Frermann et al., 2023).
Hierarchical prompt-based decoding via frozen/adapted LLM weights, with decision rules maximizing conditional probability for domain, main, and sub-narrative assignment (Singh et al., 28 May 2025).
Focal loss and adaptive threshold tuning to emphasize recall in multinarrative detection (Tyagi et al., 4 Sep 2025).
Chain-of-thought fit scoring for best narrative/theme selection (Kubli, 7 Feb 2025).

Retrieval-augmented classification can provide sentence-level evidence: SentenceBERT encoding and relevance scores select top-k supporting sentences for transparent and interpretable frame assignment (Frermann et al., 2023).

4. Evaluation Metrics, Validation Schemes, and Benchmarking

Frameworks employ rigorous evaluation metrics:

Micro/macro-averaged F1, precision, recall. Across label sets, scores are reported at clause, document, and dataset level (e.g., macro-F1 = 0.60 for relation classification, best document-level F1 for cluster-only features at $K=150$ clusters) (Das et al., 2024).
Agreement metrics. Krippendorff’s $\alpha$ (intrusion tests), Cohen’s $\kappa$ (expert vs. model), Jaccard index (multi-label human annotation), consensus scoring for perspectival outputs (Bhagat et al., 17 Apr 2025, Mire et al., 17 Dec 2025).
Information-theoretic scores. Entropy, Jensen–Shannon divergence, mutual information, and plot-twist surprise as segment-level markers for dynamic labeling (“cliffhanger,” “high-complexity arc”) (Schulz et al., 2024).
Benchmark coverage analysis. NarraBench computes taxonomy alignment via edit-distance functions and evaluates coverage ( $\approx$ 27%) and distributional agreement using JS divergence; highlights gaps in events, style, perspective, and revelation (Hamilton et al., 10 Oct 2025).

5. Interpretability, Transparency, and Theoretical Grounding

A defining strength of advanced frameworks is intrinsic interpretability:

LLM-guided cluster expansions yield human-readable exemplars for each narrative theme, aiding analyst transparency (Das et al., 2024).
Retrieval-based pipelines expose explicit evidence sentences supporting frame assignments, enabling post hoc justification and critical evaluation (Frermann et al., 2023).
Multi-hop reasoning chains (FANTA) integrate entity-relation extraction, context framing, and staged classification, elucidating how low-level cues collectively drive high-level narrative judgments (Afroz et al., 3 Dec 2025).
ReACT explanation frameworks combine evidence retrieval with taxonomy-informed reasoning steps for concise and grounded narrative justifications (Tyagi et al., 4 Sep 2025).

Framework architectures are theoretically anchored in policy framing (policy frames, narrative roles), narrative theory (Labov, Freytag, Genette, Piper), information theory (Schulz–Patrício–Odijk), and social psychology (intent and reader response taxonomy) (Das et al., 2024, Hamilton et al., 10 Oct 2025, Schulz et al., 2024, Mire et al., 17 Dec 2025).

6. Domain Adaptation, Multilinguality, and Generalization

Many frameworks are designed for cross-domain application, but domain specificity remains crucial:

Fine-grained ontologies (e.g., causal micro-narratives for “inflation”) derive expert-specific cause/effect categories and definitions to guide annotation and classifier prompts; porting requires new ontologies (Heddaya et al., 2024).
Hierarchical prompt-based frameworks support multilingual data by translation to a pivot language, enabling unified LLM-inference pipelines (Singh et al., 28 May 2025).
Evaluation has focused on polarizing domains (immigration, gun control, policy, propaganda, conspiracy theories, personal stories); however, generalization to novel domains and languages remains an open challenge.

7. Limitations and Future Directions

Reported limitations include noisiness in relation extraction (especially causal relations), domain-dependent clustering quality, LLM-generated expansions prone to hallucination, intensive manual taxonomy construction, and high inference cost with large LLMs. Extensions involve joint end-to-end optimization of event, relation, and cluster extraction, alternative graph-based representations, improved multi-annotator/LLM consensus protocols, integration of multimodal evidence, and explicit modeling of perspectival reader response (Das et al., 2024, Hamilton et al., 10 Oct 2025, Mire et al., 17 Dec 2025).

Ongoing expansion of benchmark registries and design of fine-grained, multilingual, and multimodal narrative classification tasks are recommended for comprehensive progress in the field (Hamilton et al., 10 Oct 2025).

In summary, narrative classification frameworks formalize the intricate structural, functional, and semantic dimensions of narrative data, leveraging multi-layered taxonomies, state-of-the-art extraction algorithms, robust evaluation schemas, and transparent interpretability mechanisms. The diversity of approaches—event-chain clustering, clause decomposition, LLM-driven prompt pipelines, information-theoretic segmentation, and expert-aligned scoring—reflects a rapidly maturing field addressing crucial challenges in media analysis, policy framing, social discourse tracking, and explainable machine learning.