Listening Between the Lines: Decoding Podcast Narratives with Language Modeling

TL;DR

This paper introduces a fine-tuned BERT model to analyze podcast narratives by linking narrative frames to specific entities, enabling better understanding of how podcasts persuade and inform through their discourse structures.

Contribution

The paper presents a novel frame-labeling methodology tailored for conversational data and demonstrates how narrative frames relate systematically to discussion topics in podcasts.

Findings

Improved accuracy in identifying narrative frames in podcasts.

Revealed systematic relationships between topics and narrative frames.

Enhanced understanding of persuasive strategies in digital media.

Abstract

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.