# Fine-grained Narrative Classification in Biased News Articles Source: [Fine-grained Narrative Classification in Biased News Articles]() Collection index: [llms.txt]() Data updated: 2025-12-05T05:03:13.504Z Authors: Zeba Afroz, Harsh Vardhan, Pawan Bhakuni, Aanchal Punia, Rajdeep Kumar, Md. Shad Akhtar Published: 2025-12-03T09:07:52.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Fine-Grained Narrative Classification on INDI-PROP Farmer's Protest 1.0 (test)](): Fine-Grained Narrative Classification; INDI-PROP Farmer's Protest 1.0 (test); Micro Precision: 0.757. - [Persuasive Technique Classification on INDI-PROP Farmer's Protest](): Persuasive Technique Classification; INDI-PROP Farmer's Protest; Micro Precision: 84.7. - [Article-Bias Classification on Farmer’s Protest INDI-PROP (test)](): Article-Bias Classification; Farmer’s Protest INDI-PROP (test); Micro Precision: 77.1. - [Fine-Grained Narrative Classification on INDI-PROP CAA 1.0 (test)](): Fine-Grained Narrative Classification; INDI-PROP CAA 1.0 (test); Micro Precision: 0.67. - [Persuasive Technique Classification on INDI-PROP CAA](): Persuasive Technique Classification; INDI-PROP CAA; Micro Precision: 73. ## Original sources and implementation [Original paper]() [PDF]()