Narratives are the cognitive and emotional scaffolds of propaganda. They organize isolated persuasive techniques into coherent stories that justify actions, attribute blame, and evoke identification with ideological camps. In this paper, we propose a novel fine-grained narrative classification in biased news articles. We also explore article-bias classification as the precursor task to narrative classification and fine-grained persuasive technique identification. We develop INDI-PROP, the first ideologically grounded fine-grained narrative dataset with multi-level annotation for analyzing propaganda in Indian news media. Our dataset INDI-PROP comprises 1,266 articles focusing on two polarizing socio-political events in recent times: CAA and the Farmers' protest. Each article is annotated at three hierarchical levels: (i) ideological article-bias (pro-government, pro-opposition, neutral), (ii) event-specific fine-grained narrative frames anchored in ideological polarity and communicative intent, and (iii) persuasive techniques. We propose FANTA and TPTC, two GPT-4o-mini guided multi-hop prompt-based reasoning frameworks for the bias, narrative, and persuasive technique classification. FANTA leverages multi-layered communicative phenomena by integrating information extraction and contextual framing for hierarchical reasoning. On the other hand, TPTC adopts systematic decomposition of persuasive cues via a two-stage approach. Our evaluation suggests substantial improvement over underlying baselines in each case.

翻译：叙事是宣传的认知与情感框架，它将孤立的说服技巧组织成连贯的故事，以合理化行动、归咎责任并唤起对意识形态阵营的认同。本文提出了一种针对偏见新闻文章的新型细粒度叙事分类方法。我们同时探索了文章偏见分类作为叙事分类及细粒度说服技巧识别的前置任务。我们开发了INDI-PROP——首个基于意识形态、具有多层次标注的细粒度叙事数据集，用于分析印度新闻媒体中的宣传内容。INDI-PROP数据集包含1,266篇聚焦于近期两个极化社会政治事件的文章：《公民身份修正法案》（CAA）与农民抗议运动。每篇文章均在三个层次进行标注：（i）意识形态文章偏见（亲政府、亲反对派、中立），（ii）基于意识形态极性与传播意图的事件特定细粒度叙事框架，以及（iii）说服技巧。我们提出了FANTA与TPTC两种基于GPT-4o-mini引导的多跳提示推理框架，用于偏见、叙事及说服技巧分类。FANTA通过整合信息抽取与语境框架实现分层推理，利用多层级的传播现象；而TPTC则采用两阶段方法系统解构说服线索。评估结果表明，两种框架在各分类任务中均较基线模型取得显著提升。