# Listening Between the Lines: Decoding Podcast Narratives with Language Modeling

arXiv: https://arxiv.org/abs/2511.05310 · HTML: https://mlsanity.com/paper/2511.05310/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 7 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [TREC 2020 Podcasts Track Overview](https://mlsanity.com/paper/2103.15953/) (2021)
- [Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech](https://mlsanity.com/paper/2406.03953/) (2024)
- [A Reality Check on Context Utilisation for Retrieval-Augmented Generation](https://mlsanity.com/paper/2412.17031/) (2024)
- [Current Challenges and Future Directions in Podcast Information Access](https://mlsanity.com/paper/2106.09227/) (2021)
- [GPTopic: Dynamic and Interactive Topic Representations](https://mlsanity.com/paper/2403.03628/) (2024)
- [Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection](https://mlsanity.com/paper/2402.11621/) (2024)
- [Mapping the Podcast Ecosystem with the Structured Podcast Research Corpus](https://mlsanity.com/paper/2411.07892/) (2024)
- [Everything We Hear: Towards Tackling Misinformation in Podcasts](https://mlsanity.com/paper/2408.00292/) (2024)
- [Human Interest or Conflict? Leveraging LLMs for Automated Framing Analysis in TV Shows](https://mlsanity.com/paper/2409.12561/) (2024)
- [Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcasts](https://mlsanity.com/paper/2501.12640/) (2025)
- [An End-to-End Workflow using Topic Segmentation and Text Summarisation Methods for Improved Podcast Comprehension](https://mlsanity.com/paper/2307.13394/) (2023)
- [Finding frames with BERT: A transformer-based approach to generic news frame detection](https://mlsanity.com/paper/2409.00272/) (2024)

## Abstract (arXiv)

Podcasts have become a central arena for shaping public opinion, making them a vital source for understanding contemporary discourse. Their typically unscripted, multi-themed, and conversational style offers a rich but complex form of data. To analyze how podcasts persuade and inform, we must examine their narrative structures -- specifically, the narrative frames they employ. The fluid and conversational nature of podcasts presents a significant challenge for automated analysis. We show that existing large language models, typically trained on more structured text such as news articles, struggle to capture the subtle cues that human listeners rely on to identify narrative frames. As a result, current approaches fall short of accurately analyzing podcast narratives at scale. To solve this, we develop and evaluate a fine-tuned BERT model that explicitly links narrative frames to specific entities mentioned in the conversation, effectively grounding the abstract frame in concrete details. Our approach then uses these granular frame labels and correlates them with high-level topics to reveal broader discourse trends. The primary contributions of this paper are: (i) a novel frame-labeling methodology that more closely aligns with human judgment for messy, conversational data, and (ii) a new analysis that uncovers the systematic relationship between what is being discussed (the topic) and how it is being presented (the frame), offering a more robust framework for studying influence in digital media.