# Entity Framing and Role Portrayal in the News

arXiv: https://arxiv.org/abs/2502.14718 · HTML: https://mlsanity.com/paper/2502.14718/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 64 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods](https://mlsanity.com/paper/1610.03771/) (2016)
- [Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data](https://mlsanity.com/paper/2205.07557/) (2022)
- [Author's Sentiment Prediction](https://mlsanity.com/paper/2011.06128/) (2020)
- [Characterizing the Entities in Harmful Memes: Who is the Hero, the Villain, the Victim?](https://mlsanity.com/paper/2301.11219/) (2023)
- [Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing](https://mlsanity.com/paper/2306.02052/) (2023)
- [FinEntity: Entity-level Sentiment Classification for Financial Texts](https://mlsanity.com/paper/2310.12406/) (2023)
- [Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot Strategies](https://mlsanity.com/paper/2404.04361/) (2024)
- [YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews](https://mlsanity.com/paper/2012.14541/) (2020)
- [Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection](https://mlsanity.com/paper/2402.11621/) (2024)

## Built on by (most cited first, 6 of 6)

- [Team QUST at SemEval-2025 Task 10: Evaluating Large Language Models in Multiclass Multi-label Classification of News Entity Framing](https://mlsanity.com/paper/2506.21564/) (2025)
- [FRaN-X: FRaming and Narratives-eXplorer](https://mlsanity.com/paper/2507.06974/) (2025)
- [A Counterfactual Graph Approach to Oppositional Narrative Analysis](https://mlsanity.com/paper/2603.06135/) (2026)
- [ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies](https://mlsanity.com/paper/2605.27045/) (2026)
- [Uncovering Temporal Framing in the News](https://mlsanity.com/paper/2606.00294/) (2026)
- [Conflict or Strategy? Asymmetric Role Framing of La France insoumise and Rassemblement National in French News Headlines, 2022-2025](https://mlsanity.com/paper/2608.09936/) (2026)

## Abstract (arXiv)

We introduce a novel multilingual hierarchical corpus annotated for entity framing and role portrayal in news articles. The dataset uses a unique taxonomy inspired by storytelling elements, comprising 22 fine-grained roles, or archetypes, nested within three main categories: protagonist, antagonist, and innocent. Each archetype is carefully defined, capturing nuanced portrayals of entities such as guardian, martyr, and underdog for protagonists; tyrant, deceiver, and bigot for antagonists; and victim, scapegoat, and exploited for innocents. The dataset includes 1,378 recent news articles in five languages (Bulgarian, English, Hindi, European Portuguese, and Russian) focusing on two critical domains of global significance: the Ukraine-Russia War and Climate Change. Over 5,800 entity mentions have been annotated with role labels. This dataset serves as a valuable resource for research into role portrayal and has broader implications for news analysis. We describe the characteristics of the dataset and the annotation process, and we report evaluation results on fine-tuned state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, a paragraph, and a sentence.