@inproceedings{luo-etal-2026-extax,
title = "{E}x{T}ax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies",
author = "Luo, Shang and
Yang, Yingguang and
Sun, Zhenchen and
Liu, Yang and
Chong, Bin and
Chen, Jingru and
Chen, Yancheng and
Liang, Jiayu and
Kefu, Xu and
Peng, Hao and
Yu, Philip S.",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2573/",
pages = "58457--58483",
ISBN = "979-8-89176-459-0",
abstract = "The democratization of LLMs has accelerated the generation and circulation of highly fluent disinformation, making traditional syntax-semantic verification increasingly insufficient. Such deception often combines persuasive rhetoric, emotional manipulation, and narrative role construction to shape readers' interpretations. However, existing detectors typically emphasize isolated signals{---}such as syntax, external knowledge, persuasion, or affective cues{---}and thus struggle to capture multi-faceted manipulation or provide structured, taxonomy-grounded evidence. To address this gap, we present $\mathbf{ExTax}$, a taxonomy-aligned framework for interpretable disinformation detection. ExTax unifies persuasive rhetoric, emotional manipulation, and narrative roles into a 17-dimensional taxonomic space comprising 6 persuasive-rhetoric strategies, 5 emotional-manipulation methods, and 6 narrative-role categories. It elicits attributes from multiple frontier LLMs, reconciles their disagreements through Entropy-driven Dynamic Label Smoothing, and fuses the resulting taxonomic representations with contextual encodings via Heterogeneous Multi-Head Attention. Across five cross-domain and cross-genre benchmarks, ExTax achieves an overall Macro $F_1$ of 0.8456, outperforming strong deep learning and LLM-based baselines. It also remains robust under severe genre imbalance, where the strongest deep baseline drops from 0.9454 to 0.6194 across genres. Our code is available at https://github.com/owlsan49/ExTax."
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<abstract>The democratization of LLMs has accelerated the generation and circulation of highly fluent disinformation, making traditional syntax-semantic verification increasingly insufficient. Such deception often combines persuasive rhetoric, emotional manipulation, and narrative role construction to shape readers’ interpretations. However, existing detectors typically emphasize isolated signals—such as syntax, external knowledge, persuasion, or affective cues—and thus struggle to capture multi-faceted manipulation or provide structured, taxonomy-grounded evidence. To address this gap, we present \mathbfExTax, a taxonomy-aligned framework for interpretable disinformation detection. ExTax unifies persuasive rhetoric, emotional manipulation, and narrative roles into a 17-dimensional taxonomic space comprising 6 persuasive-rhetoric strategies, 5 emotional-manipulation methods, and 6 narrative-role categories. It elicits attributes from multiple frontier LLMs, reconciles their disagreements through Entropy-driven Dynamic Label Smoothing, and fuses the resulting taxonomic representations with contextual encodings via Heterogeneous Multi-Head Attention. Across five cross-domain and cross-genre benchmarks, ExTax achieves an overall Macro F₁ of 0.8456, outperforming strong deep learning and LLM-based baselines. It also remains robust under severe genre imbalance, where the strongest deep baseline drops from 0.9454 to 0.6194 across genres. Our code is available at https://github.com/owlsan49/ExTax.</abstract>
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%0 Conference Proceedings
%T ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies
%A Luo, Shang
%A Yang, Yingguang
%A Sun, Zhenchen
%A Liu, Yang
%A Chong, Bin
%A Chen, Jingru
%A Chen, Yancheng
%A Liang, Jiayu
%A Kefu, Xu
%A Peng, Hao
%A Yu, Philip S.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F luo-etal-2026-extax
%X The democratization of LLMs has accelerated the generation and circulation of highly fluent disinformation, making traditional syntax-semantic verification increasingly insufficient. Such deception often combines persuasive rhetoric, emotional manipulation, and narrative role construction to shape readers’ interpretations. However, existing detectors typically emphasize isolated signals—such as syntax, external knowledge, persuasion, or affective cues—and thus struggle to capture multi-faceted manipulation or provide structured, taxonomy-grounded evidence. To address this gap, we present \mathbfExTax, a taxonomy-aligned framework for interpretable disinformation detection. ExTax unifies persuasive rhetoric, emotional manipulation, and narrative roles into a 17-dimensional taxonomic space comprising 6 persuasive-rhetoric strategies, 5 emotional-manipulation methods, and 6 narrative-role categories. It elicits attributes from multiple frontier LLMs, reconciles their disagreements through Entropy-driven Dynamic Label Smoothing, and fuses the resulting taxonomic representations with contextual encodings via Heterogeneous Multi-Head Attention. Across five cross-domain and cross-genre benchmarks, ExTax achieves an overall Macro F₁ of 0.8456, outperforming strong deep learning and LLM-based baselines. It also remains robust under severe genre imbalance, where the strongest deep baseline drops from 0.9454 to 0.6194 across genres. Our code is available at https://github.com/owlsan49/ExTax.
%U https://aclanthology.org/2026.emnlp-main.2573/
%P 58457-58483
Markdown (Informal)
[ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies](https://aclanthology.org/2026.emnlp-main.2573/) (Luo et al., EMNLP 2026)
ACL
- Shang Luo, Yingguang Yang, Zhenchen Sun, Yang Liu, Bin Chong, Jingru Chen, Yancheng Chen, Jiayu Liang, Xu Kefu, Hao Peng, and Philip S. Yu. 2026. ExTax: Explainable Disinformation Detection via Persuasion, Emotion, and Narrative Role Taxonomies. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 58457–58483, Budapest, Hungary. Association for Computational Linguistics.