Identifying Narrative Content in Podcast Transcripts (2024)

Abstract: As one of the oldest forms of human communication, narratives appear across a variety of genres and media. Computational methods have been applied to study narrativity in novels, social media, and patient records, leading to new approaches and insights. However, other types of media are growing in popularity, like podcasts. Podcasts contain a multitude of spoken narratives that can provide a meaningful glimpse into how people share stories with one another.In this paper, we outline and apply methods to process English-language podcast transcripts and extract narrative content from conversations within each episode. We provide an initial analysis of the types of narrative content that exists within a wide range of podcasts, and compare our results to other established narrative analysis tools.Our annotations for narrativity and pretrained models can help to enable future research into narrativity within a large corpus of approximately 100,000 podcast episodes.

## introduction storytelling is an intricate and culturally rich psychological phenomenon. when storytellers share a narrative with an audience, they are doing more than just telling a story @xcite . they are taking their audience on a shared journey, navigating through emotions, insights, and cultural reflections. our understanding of the complex psychological framework underpinning narrative structures is still in its early stages @xcite . as per @xcite , a narrative can be described as a communication that recounts the journey of particular characters through a sequence of interconnected events within a specified timeframe. this concept fundamentally revolves around conveying someone's personal experience or perspective on a subject. previous work in natural language processing has examined narratives in novels @xcite , social media sites such as reddit @xcite , twitter @xcite and facebook @xcite , and medical records @xcite . narrative analysis in these studies has explored aspects such as feature analysis in online health communities @xcite or the spread of health misinformation on twitter @xcite , contributing to a deeper understanding of how narratives are constructed and communicated in diverse textual sources. in recent years, podcasts have emerged as a significant medium, rich in linguistic variety and style. for example, the share of americans who listen to podcasts has substantially increased over the last decade @xcite . their diverse topics, ranging from formal news journalism to conversational chats and spanning both fiction and nonfiction, allow researchers to delve into language use across various emotional and thematic contexts. once transcribed, podcast datasets can bridge the gap between formal and informal language, serving as a crucial resource for uncovering various insights and patterns from modern language. one important feature of podcasts that has received little attention, however, is narrativity, which is important because of its contribution to engagement and emotional connection. many podcast episodes contain examples of people sharing stories, either in the form of personal experiences or storytelling involving external characters and events. at the same time, unlike novels, transcribed podcasts are not always coherent texts and possess a conversational nature, allowing transitions between narrative and non-narrative content within a single episode and this dynamic flexibility is creating the unicity of narratives within podcasts. given the large number of often lengthy podcast episodes, automatically extracting and analyzing this narrative content from podcasts may help to explore the potential for new avenues in research, content creation, recommen-dation systems, and other applications. in this study, we build upon the previous work in this domain and introduce a novel model for extracting narratives from podcasts. the extraction of narratives from podcast data poses several unique challenges. unlike written texts, podcasts rely on oral communication, which follows a different style and structure @xcite , and additional noise may be introduced due to imperfect transcription tools. podcasts span a broad range of topics and formats, which makes it more difficult to apply narrative analysis and detection tools that are tailored to particular genres or media. podcasts often follow a conversational format with multiple speakers, making the identification and separation of narrative threads more intricate. often, the main content of the podcast is interspersed with extraneous content such as advertisements, which should be ignored when identifying narratives. in this work, we make the following contributions: (1) we develop a podcast transcript processing workflow to remove non-english and extraneous content; (2) we annotate a set of podcast episodes for sentence-level narrativity and fine-tune language models for the task of narrative detection; ## narrative extraction methodology in this section, we describe the baseline method from the linguistic inquiry and word count (liwc), which we used to assign narrativity scores to podcast transcripts, and our approach to building text classification models that we evaluate and use later for the extraction of narrative sentences from podcast transcripts. ## podcast narrative analysis in this section, we use our narrative extraction models to estimate the overall narrativity of each podcast to explore the topics that are most associated with narrativity. we compare our results with an-other popular method for automatically quantifying narrativity and find that in the domain of podcasts, our method appears to better identify texts that have a high degree of narrativity. ## conclusion in this work, we studied narrativity within podcasts, a medium that has grown in popularity in recent years. to clean the dataset, we implemented an extraneous content detection system and demonstrated competitive results with existing works. our classifier can work on both episode descriptions and transcripts at the same time. we then annotated a dataset and trained text classification models for the task of narrative sentence detection. we used our best performing model to annotate the entire spotify podcasts dataset for narrativity, and then compared the types of podcasts that had a high proportion of narrative sentences with those that had high narrative scores based on other tools, namely liwc narrative arc. our results show that our proposed method was able to identify high narrativity in fiction and true crime podcasts, which are expected examples of categories that should contain narrative content. we aspire for this research to serve as a starting point for future investigations into podcast narrativity, and we believe our annotated data and proposed models will facilitate future analyses in this area.