在字里行间倾听：利用语言模型解码播客叙事
Listening Between the Lines: Decoding Podcast Narratives with Language Modeling
AI总结：
本文提出一种基于 BERT 的方法，通过标注叙事框架与对话实体的关系，揭示播客中主题与框架的系统关联，提升对数字媒体影响的分析能力。
摘要：
播客已成为塑造公众意见的重要场所，因此成为理解当代 discourse 的关键来源。其通常非剧本、多主题、对话式的风格提供了丰富但复杂的数据形式。为了分析播客如何说服和传达信息，我们必须研究其叙事结构——特别是它们所使用的叙事框架。播客的流体和对话性质给自动化分析带来了重大挑战。我们证明现有大型语言模型，通常在更结构化的文本如新闻文章上训练，难以捕捉人类听众依赖的细微线索来识别叙事框架。因此，当前方法在大规模准确分析播客叙事方面显得不足。为了解决这一问题，我们开发并评估了一个经过微调的 BERT 模型，该模型明确将叙事框架与对话中提到的特定实体联系起来，有效地将抽象框架扎根于具体细节。我们的方法随后使用这些细粒度框架标签，并将其与高级主题相关联，以揭示更广泛的 discourse 趋势。本文的主要贡献是：（i）一种新的框架标注方法，更符合人类对杂乱对话数据的判断；（ii）一种新的分析，揭示了讨论内容（主题）与呈现方式（框架）之间的系统关系，提供了一个更稳健的框架来研究数字媒体中的影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
