# A Structured Clustering Approach for Inducing Media Narratives

arXiv: https://arxiv.org/abs/2604.10368 · HTML: https://mlsanity.com/paper/2604.10368/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 91 references that are in the map.
- First posted on arXiv 11 Apr 2026.

## Builds on (most cited first, 23 of 23)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- ["Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding](https://mlsanity.com/paper/2109.05438/) (2021)
- [Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies](https://mlsanity.com/paper/1808.09386/) (2018)
- [Modeling Protagonist Emotions for Emotion-Aware Storytelling](https://mlsanity.com/paper/2010.06822/) (2020)
- [Modeling Framing in Immigration Discourse on Social Media](https://mlsanity.com/paper/2104.06443/) (2021)
- [Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data](https://mlsanity.com/paper/2205.07557/) (2022)
- [Neural Discourse Structure for Text Categorization](https://mlsanity.com/paper/1702.01829/) (2017)
- [Corpus-based Open-Domain Event Type Induction](https://mlsanity.com/paper/2109.03322/) (2021)
- [Where Do People Tell Stories Online? Story Detection Across Online Communities](https://mlsanity.com/paper/2311.09675/) (2023)
- [Conflicts, Villains, Resolutions: Towards models of Narrative Media Framing](https://mlsanity.com/paper/2306.02052/) (2023)
- [Using Sentence-Level LSTM Language Models for Script Inference](https://mlsanity.com/paper/1604.02993/) (2016)
- [CHIRON: Rich Character Representations in Long-Form Narratives](https://mlsanity.com/paper/2406.10190/) (2024)
- [A Survey of Event Causality Identification: Taxonomy, Challenges, Assessment, and Prospects](https://mlsanity.com/paper/2411.10371/) (2024)
- [DAPrompt: Deterministic Assumption Prompt Learning for Event Causality Identification](https://mlsanity.com/paper/2307.09813/) (2023)
- [Media Framing through the Lens of Event-Centric Narratives](https://mlsanity.com/paper/2410.03151/) (2024)
- [Narrative Media Framing in Political Discourse](https://mlsanity.com/paper/2506.00737/) (2025)
- [Understanding Actors and Evaluating Personae with Gaussian Embeddings](https://mlsanity.com/paper/1804.04164/) (2018)
- [Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News](https://mlsanity.com/paper/2402.14224/) (2024)

## Built on by (most cited first, 1 of 1)

- [Discovering Conceptual Metaphors Across Topics and Media Types](https://mlsanity.com/paper/2608.06652/) (2026)

## Abstract (arXiv)

Media narratives wield tremendous power in shaping public opinion, yet computational approaches struggle to capture the nuanced storytelling structures that communication theory emphasizes as central to how meaning is constructed. Existing approaches either miss subtle narrative patterns through coarse-grained analysis or require domain-specific taxonomies that limit scalability. To bridge this gap, we present a framework for inducing rich narrative schemas by jointly modeling events and characters via structured clustering. Our approach produces explainable narrative schemas that align with established framing theory while scaling to large corpora without exhaustive manual annotation.