---
title: Inflation Narratives Dataset
url: https://www.emergentmind.com/topics/inflation-narratives-dataset
type: topic
---

# Inflation Narratives Dataset

The Inflation Narratives Dataset is a curated resource for empirical research on the extraction, classification, and quantitative study of causal economic narratives surrounding inflation in U.S. news media. It provides annotated textual data and controlled gold standards for benchmarking both traditional and large language model-based pipelines for narrative detection, with particular emphasis on explicit and implicit causal claims about inflation, its determinants, and its effects [2506.15041][2410.05252].

## 1. Definition and Scope

The task of identifying “inflation narratives” entails recognizing passages in news media that express a causal relation between inflation and other macroeconomic phenomena. Two principal resources are referenced in current literature:

- **[2506.15041]**: Focuses on explicitly formatted economic narratives—defined as causally-linked pairs of semantically and temporally distinct events—within selected newspaper articles mentioning inflation or significant price changes from 1985–2023 (WSJ/NYT).
- **[2410.05252]**: Emphasizes sentence-level *causal micro-narratives*, where sentences are categorized according to ontology-based “cause” and “effect” labels drawn from a taxonomy of inflation-related concepts, spanning U.S. news from 1960–1980 and 2012–2023 (ProQuest; NOW).

Both datasets specifically exclude mere references to inflation without explicit or implicit causal attribution, and restrict annotation to U.S. English-language coverage. The inflation narrative annotation is thus domain-, language-, and period-specific.

## 2. Corpus Construction and Sampling

### [2506.15041] Pipeline

- **Source**: Wall Street Journal and New York Times.
- **Sampling**: Filtered for “inflation” or “price (increase|hike|surge)” in content.
- **Annotated Subset**: 100 articles randomly selected.
- **Excerpting**: Each excerpt consists of one inflation-mentioning sentence plus two preceding and two following sentences, averaging ≈5 sentences/120 words per excerpt.
- **Total Annotation Set**: ≈12,000 words; 291 narrative pairs.

### [2410.05252] Pipeline

- **Sources**:
  - Contemporary: NOW Corpus (2012–2023), 118,383 filtered articles.
  - Historical: ProQuest U.S. news (1960–80), 392,475 filtered articles.
- **Segmentation**: Sentence tokenization via spaCy (NOW) and BlingFire (ProQuest).
- **Annotation Set**: ≈1,000 sentences per source for training, 1,119 (NOW test), 488 (ProQuest test).

This sampling framework yields annotation units that are either multi-sentence excerpts ([2506.15041]) or single sentences ([2410.05252]), suitable respectively for structured narrative-link extraction and multi-label classification.

## 3. Annotation Guidelines and Ontology

### Causal Link Definition ([2506.15041])

- **Event structure**: “Event A – causes – Event B” or “Event A – is caused by – Event B”.
- **Events**: Must describe discernible occurrences, activities, conditions, policies, or future plans.
- **Wording**: Verbatim recording, avoiding synonymization or tense modification.
- **Chaining/Forking**: Chained or forked causality decomposed into atomic pairs.
- **Coreference**: Pronouns resolved to antecedent entities.
- **Economic relevance**: Only narratives with explicit economic inflation relevance coded.
- **Positive causality**: Negations or “does not cause” relations excluded.

### Cause–Effect Ontology ([2410.05252])

- **Causes** (8): demand, supply, wage, monetary, fiscal, expect, international, other-cause.
- **Effects** (11): purchase, cost, uncertain, rates, redistribution, savings, trade, cost-push, social, govt, other-effect.
- **Annotation task**: Binary narrative detection (presence/absence), plus multi-label assignment from this ontology.
- **Additional tags**: Temporal frame (past/present/future/na), direction (up/down/na), U.S. context.

A key distinction is that [2506.15041] encodes explicit event–event causal pairs as structured triples, while [2410.05252] maps sentences to an ontology for multi-label statistical categorization.

## 4. Dataset Structure and Formats

| Resource           | Unit           | Annotation Fields                                      | Size                              |
|--------------------|---------------|-------------------------------------------------------|------------------------------------|
| [2506.15041]       | Article excerpt| {Event A, causal connector, Event B} (JSON triple)    | 100 excerpts, 291 narratives       |
| [2410.05252]       | Sentence      | contains-narrative (bool); “narratives”: [{cause/effect, time, dir}] | ≈2,600 sentences annotated         |

**[2506.15041] Example Entry:**
```json
{
  "Event A": "the FOMC raised the policy rate",
  "causal connector": "causes",
  "Event B": "turmoil on Wall Street"
}
```

**[2410.05252] Example Entry** *(editor’s term; see App. D for exact format)*:
```json
{
  "contains-narrative": true,
  "narratives": [
    {"cause": "monetary", "time": "present", "direction": "up"}
  ],
  "source": "NOW",
  "raw_text": "Higher central bank rates have pushed up inflation expectations."
}
```
Files are distributed in JSON, with CSV metadata (excerpt text, document ID, counts) available in [2506.15041]. Conversion to tabular format is straightforward.

## 5. Annotation Workflow and Agreement

### [2506.15041] Gold Standard

- **Three experts:** Independent full-corpus coding.
- **Codebook refinement:** Via iterative calibration meetings.
- **Adjudication:** Inclusion of any narrative unanimously judged correct.
- **Agreement metrics:** Pairwise Jaccard similarity (J≈0.59–0.60), expert-vs-gold accuracy (0.67–0.74).

### [2410.05252] Annotator Protocol

- **Interface:** All 19 labels visible with definitions; only explicit/implicit causal claims coded.
- **Agreement:** Krippendorff’s α (MASI-weighted): binary detection, 0.80 (ProQuest), 0.67 (NOW); multi-label, 0.66 (ProQuest), 0.59 (NOW).
- **Observed confusion:** Disagreement often on presence/absence of any narrative, especially for “social”, rather than on label choice.

Both studies demonstrate substantial but imperfect agreement; subjectivity and ambiguity remain substantial constraints.

## 6. Extraction Methods and Model Performance

### LLM and Fine-tuned Model Pipelines

- **[2506.15041]:** 
  - Model: GPT-4o (snapshot gpt-4o-2024-11-20, T=0.2).
  - Prompting: Few-shot Chain-of-Thought with 7 curated exemplars.
  - Extraction sequence: Discard extracorpus, isolate first two-event chain, restate causal link in JSON, resolve coreference, finalize event phrasing.
  - Post-processing: Fork decomposition, LLM-based valence/topic clustering, normalization.
  - Metrics: Accuracy (0.44, vs. 0.67–0.74 for experts); Jaccard similarity (0.40 vs. experts’ 0.59–0.60); model over-extracts in narrative-sparse, under-extracts in narrative-dense texts.

- **[2410.05252]:** 
  - Best-performing model: LLaMA 3.1 8B, fine-tuned.
  - F₁: 0.87 (NOW, binary), 0.71 (NOW, multi-label); 0.78/0.62 (ProQuest).
  - Comparators: Phi-2 (fine-tuned) and GPT-4o (few-shot), lower F₁.
  - Cross-domain F₁ drop: 3–4% absolute loss for multitask transfer; up to 11% gain on detection.
  - Major sources of error: Linguistic ambiguity, model confusion paralleling annotator disagreement, false chains.

A plausible implication is that controlled fine-tuning with a rich ontology and clear interface yields higher detection/classification fidelity than zero/few-shot LLM prompting.

## 7. Applications, Access, and Limitations

### Use Cases

- Tracking diachronic or cross-outlet shifts in inflation narratives.
- Construction of narrative-intensity indices for macroeconomic modeling.
- Empirical studies of media framing and economic discourse.
- Benchmarking LLMs and model pipelines for interpretability in social science contexts.

### Access

- [2506.15041]: https://doi.org/10.5281/zenodo.15622517 with accompanying code and metadata (GitHub mirror available).
- [2410.05252]: https://mheddaya.com/research/narratives; users should consult project webpage for licensing.

### Limitations

- Domain-, language-, and corpus-specificity restricts generalizability.
- [2506.15041]: Small annotated sample (N=100 excerpts, 291 narratives); non-public LLM (GPT-4o) model pipeline limits replicability.
- [2410.05252]: Out-of-domain and cross-period transfer validity only partially explored.
- Both: Annotator subjectivity, high inter-coder variability, imperfect model-human alignment.

## 8. Conclusion

The Inflation Narratives Dataset, as defined by [2506.15041] and [2410.05252], constitutes a rigorously annotated, publicly available resource for studying causal claims about inflation in U.S. media corpora. It provides gold-standard annotation protocols, detailed schema for both structured and multi-label classification, and benchmarks for both expert and LLM-driven extraction processes. The resource underpins current methodological advances in computational narrative analysis and supports a spectrum of applications in empirical economics, computational social science, and AI-driven media analytics.

Source: https://www.emergentmind.com/topics/inflation-narratives-dataset