@inproceedings{tan-handoyo-2026-survey,
title = "A Survey of Neurosymbolic Approaches to Propaganda and Persuasion Technique Detection",
author = "Tan, Alysa Ziying and
Handoyo, Daniel Kainovan",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.138/",
pages = "2688--2699",
ISBN = "979-8-89176-444-6",
abstract = "Propaganda and persuasion technique detection, the task of identifying how misleading content persuades rather than merely what it claims, occupies a distinctive and underexplored position within computational misinformation research. This task requires interpretable and rhetorically grounded reasoning over persuasion techniques, making it particularly well aligned with neurosymbolic artificial intelligence (AI). This survey presents the first systematic review of neurosymbolic approaches for propaganda and persuasion technique detection, together with relevant structured-reasoning approaches from closely related areas that offer methodological foundations for these tasks. It synthesizes work spanning argument mining, knowledge-augmented learning, logic-based fallacy detection, structured reasoning with large language models (LLMs), and hybrid neural-symbolic architectures. We further organize existing methods through a unified taxonomy of neurosymbolic integration paradigms and review foundational rhetoric theory, benchmark datasets, and evaluation methodologies. We identify key open challenges, including subtle and implicit persuasion, cross-lingual generalization, explanation faithfulness, and the growing challenge of LLM-generated propaganda. Finally, we outline future research directions and argue that neurosymbolic AI provides a promising foundation for bridging predictive performance with human-interpretable rhetorical reasoning in next-generation misinformation detection systems."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tan-handoyo-2026-survey">
<titleInfo>
<title>A Survey of Neurosymbolic Approaches to Propaganda and Persuasion Technique Detection</title>
</titleInfo>
<name type="personal">
<namePart type="given">Alysa</namePart>
<namePart type="given">Ziying</namePart>
<namePart type="family">Tan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Daniel</namePart>
<namePart type="given">Kainovan</namePart>
<namePart type="family">Handoyo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-11</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Yue</namePart>
<namePart type="family">Zhang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ruifeng</namePart>
<namePart type="family">Xu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ayu</namePart>
<namePart type="family">Purwarianti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tong</namePart>
<namePart type="family">Rong</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jun</namePart>
<namePart type="family">Suzuki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Hengqin, China</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-444-6</identifier>
</relatedItem>
<abstract>Propaganda and persuasion technique detection, the task of identifying how misleading content persuades rather than merely what it claims, occupies a distinctive and underexplored position within computational misinformation research. This task requires interpretable and rhetorically grounded reasoning over persuasion techniques, making it particularly well aligned with neurosymbolic artificial intelligence (AI). This survey presents the first systematic review of neurosymbolic approaches for propaganda and persuasion technique detection, together with relevant structured-reasoning approaches from closely related areas that offer methodological foundations for these tasks. It synthesizes work spanning argument mining, knowledge-augmented learning, logic-based fallacy detection, structured reasoning with large language models (LLMs), and hybrid neural-symbolic architectures. We further organize existing methods through a unified taxonomy of neurosymbolic integration paradigms and review foundational rhetoric theory, benchmark datasets, and evaluation methodologies. We identify key open challenges, including subtle and implicit persuasion, cross-lingual generalization, explanation faithfulness, and the growing challenge of LLM-generated propaganda. Finally, we outline future research directions and argue that neurosymbolic AI provides a promising foundation for bridging predictive performance with human-interpretable rhetorical reasoning in next-generation misinformation detection systems.</abstract>
<identifier type="citekey">tan-handoyo-2026-survey</identifier>
<location>
<url>https://aclanthology.org/2026.findings-aacl.138/</url>
</location>
<part>
<date>2026-11</date>
<extent unit="page">
<start>2688</start>
<end>2699</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T A Survey of Neurosymbolic Approaches to Propaganda and Persuasion Technique Detection
%A Tan, Alysa Ziying
%A Handoyo, Daniel Kainovan
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F tan-handoyo-2026-survey
%X Propaganda and persuasion technique detection, the task of identifying how misleading content persuades rather than merely what it claims, occupies a distinctive and underexplored position within computational misinformation research. This task requires interpretable and rhetorically grounded reasoning over persuasion techniques, making it particularly well aligned with neurosymbolic artificial intelligence (AI). This survey presents the first systematic review of neurosymbolic approaches for propaganda and persuasion technique detection, together with relevant structured-reasoning approaches from closely related areas that offer methodological foundations for these tasks. It synthesizes work spanning argument mining, knowledge-augmented learning, logic-based fallacy detection, structured reasoning with large language models (LLMs), and hybrid neural-symbolic architectures. We further organize existing methods through a unified taxonomy of neurosymbolic integration paradigms and review foundational rhetoric theory, benchmark datasets, and evaluation methodologies. We identify key open challenges, including subtle and implicit persuasion, cross-lingual generalization, explanation faithfulness, and the growing challenge of LLM-generated propaganda. Finally, we outline future research directions and argue that neurosymbolic AI provides a promising foundation for bridging predictive performance with human-interpretable rhetorical reasoning in next-generation misinformation detection systems.
%U https://aclanthology.org/2026.findings-aacl.138/
%P 2688-2699
Markdown (Informal)
[A Survey of Neurosymbolic Approaches to Propaganda and Persuasion Technique Detection](https://aclanthology.org/2026.findings-aacl.138/) (Tan & Handoyo, Findings 2026)
ACL