@inproceedings{afroz-etal-2026-fine,
title = "Fine-grained Narrative Classification in Biased News Articles",
author = "Afroz, Zeba and
Vardhan, Harsh and
Bhakuni, Pawan and
Punia, Aanchal and
Kumar, Rajdeep and
Akhtar, Md. Shad",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.665/",
doi = "10.63317/2ddvvr4zijyh",
pages = "8431--8445",
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 article. We also explore article-bias classification as the pre-cursor task to narrative classification and fine-grained persuassive technique identification. We develop INDI-PROP, the first ideologically grounded fine-grain 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/NRC 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 guided multi-hop prompt-based reasoning frameworks for the bias, narrative, and persuasive technique classification. FANTA leverages multi-layered communicative phenomenon 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 suggest substantial improvement over underlying baselines in each case."
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<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 article. We also explore article-bias classification as the pre-cursor task to narrative classification and fine-grained persuassive technique identification. We develop INDI-PROP, the first ideologically grounded fine-grain 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/NRC 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 guided multi-hop prompt-based reasoning frameworks for the bias, narrative, and persuasive technique classification. FANTA leverages multi-layered communicative phenomenon 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 suggest substantial improvement over underlying baselines in each case.</abstract>
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%0 Conference Proceedings
%T Fine-grained Narrative Classification in Biased News Articles
%A Afroz, Zeba
%A Vardhan, Harsh
%A Bhakuni, Pawan
%A Punia, Aanchal
%A Kumar, Rajdeep
%A Akhtar, Md. Shad
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F afroz-etal-2026-fine
%X 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 article. We also explore article-bias classification as the pre-cursor task to narrative classification and fine-grained persuassive technique identification. We develop INDI-PROP, the first ideologically grounded fine-grain 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/NRC 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 guided multi-hop prompt-based reasoning frameworks for the bias, narrative, and persuasive technique classification. FANTA leverages multi-layered communicative phenomenon 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 suggest substantial improvement over underlying baselines in each case.
%R 10.63317/2ddvvr4zijyh
%U https://aclanthology.org/2026.lrec-1.665/
%U https://doi.org/10.63317/2ddvvr4zijyh
%P 8431-8445
Markdown (Informal)
[Fine-grained Narrative Classification in Biased News Articles](https://aclanthology.org/2026.lrec-1.665/) (Afroz et al., LREC 2026)
ACL
- Zeba Afroz, Harsh Vardhan, Pawan Bhakuni, Aanchal Punia, Rajdeep Kumar, and Md. Shad Akhtar. 2026. Fine-grained Narrative Classification in Biased News Articles. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 8431–8445, Palma de Mallorca, Spain. ELRA Language Resource Association.