Fine-grained Narrative Classification in Biased News Articles

2025-12-03

AI Summary

This paper addresses the fine-grained, joint identification of ideological bias, event framing, and persuasive techniques in Indian biased news. To this end, we introduce INDI-PROP—the first ideology-aware, multi-level annotated dataset—covering two high-profile events: the Citizenship Amendment Act (CAA) and the farmers’ protests. We propose two GPT-4o-mini–driven multi-hop reasoning frameworks—FANTA and TPTC—that decompose the task into two stages, integrating information extraction with contextual modeling to enable hierarchical analysis—from macro-level ideological bias to micro-level narrative structures and rhetorical devices. Experiments demonstrate that our methods significantly outperform baselines across three core tasks: bias detection, narrative frame classification, and persuasive technique identification. By unifying interpretability with multi-granularity analysis, this work advances propaganda analysis toward more transparent, layered, and theoretically grounded methodologies.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Preferences
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Classify ideological bias in Indian news articles about polarizing events
• Identify fine-grained narrative frames used as propaganda scaffolds
• Detect persuasive techniques in biased media through hierarchical annotation

Innovation

• GPT-4o-mini guided multi-hop prompt-based reasoning frameworks
• First ideologically grounded fine-grained narrative dataset INDI-PROP
• Hierarchical annotation for bias, narrative frames, persuasive techniques