DOI: 10.5281/zenodo.15566143
title: Supplementary Materials and Dataset for: "LLM-Inferred Narrative Frames in Geopolitical Conflict Reporting"

description:  



This supplementary material contains both tabular data and visualizations derived from a computational media framing analysis conducted using zero-shot large language models (LLMs). The files support the analyses presented in the corresponding study and are intended for transparency, exploratory review, and reproducibility. All frame and entity associations were generated via automated inference and do not reflect human-coded labels or verified facts.


 



framing_predictions.csv




 

Description:




This file contains article-level narrative frame predictions, produced through a two-stage classification pipeline using facebook/bart-large-mnli followed by google/flan-t5-large. BART provided initial zero-shot frame scores, and FLAN served as a semantic filter to retain only prominent or well-justified frames.

 




Fields:







Column Name
Description




source
Name of the news outlet or source


published_date
Date the article was published


content
Full main text of the article


predicted_frames
Comma-separated list of top BART-predicted frames


frame_scores
Dictionary of model-predicted frame scores ({frame: score}) from BART


validated_frames
Final list of frames retained after semantic validation by FLAN


validation_flags
Raw responses from FLAN model for each evaluated frame





 



entity_level_framing_predictions.csv




 

Description:




This file contains sentence-level associations between named entities and narrative frames, filtered through a multi-step semantic validation pipeline. Each entry represents a model-inferred connection between an entity and a frame, based on the sentence context and validated through five FLAN-T5 prompts.

 




Fields:







Column Name
Description




article_id
Identifier linking to the original article


entity
Named entity mentioned in the sentence


sentence
Sentence in which the entity appears


frame
Narrative frame assigned to the sentence containing the entity


score
Model-assigned frame confidence score (from BART)





 



Supplementary Figure S1




Filename: Supplementary Figure S1.png




 

Description:




Entity–frame network showing model-inferred associations between a broader set of regionally or politically significant actors (e.g., UNRWA, Palestinian Authority, IDF, Amnesty International) and narrative frames. Nodes represent named entities; edges reflect sentence-level co-occurrences with specific frames. Only entities matching a predefined geopolitical keyword set were included. This graph supports a deeper view of how large language models detect thematic emphases around institutional actors.


 



Supplementary Figure S2




Filename: Supplementary Figure S2.png




 

Description:


Detailed entity–frame association graph displaying validated entity–frame pairs extracted from the dataset. Due to the high volume of unique named entities, this figure serves as a comprehensive visualization of LLM-inferred sentence-level framing across all actors. It reflects raw interpretive patterns detected by the model and is intended for transparency and exploratory reference, rather than close reading.

 



Interpretation and Responsible Use




All annotations and inferences in this dataset reflect the interpretation of large language models applied to surface-level textual content. These outputs should not be treated as verified facts or editorial claims. No manual coding or human judgment was involved in the labeling process, except for the construction of prompt templates.




The materials are provided to support transparency and reproducibility in automated framing analysis, especially within the context of politically sensitive topics. They are not recommended for downstream supervised learning tasks or for use as benchmark ground truth in other framing-related studies.


 



Licensing and Use




This supplementary dataset and accompanying visualizations are released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). They are intended solely for academic, non-commercial use.




All article content remains the copyright of the original publishers. No private or personally identifiable information has been included, and journalist bylines were collected only as part of publicly accessible metadata.