X-Troll: EXplainable Detection of State-Sponsored Information Operations Agents

Tian, L; Zhang, X; Kim, MMH; Biggs, J; Rizoiu, MA

X-Troll: EXplainable Detection of State-Sponsored Information Operations Agents

Tian, L Zhang, X Kim, MMH Biggs, J Rizoiu, MA

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management, 2025, pp. 2874-2884
Issue Date:: 2025-11-10

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Field	Value	Language
dc.contributor.author	Tian, L
dc.contributor.author	Zhang, X
dc.contributor.author	Kim, MMH
dc.contributor.author	Biggs, J
dc.contributor.author	Rizoiu, MA
dc.date.accessioned	2025-12-22T01:22:41Z
dc.date.available	2025-12-22T01:22:41Z
dc.date.issued	2025-11-10
dc.identifier.citation	Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management, 2025, pp. 2874-2884
dc.identifier.uri	http://hdl.handle.net/10453/191068
dc.description.abstract	State-sponsored trolls, malicious actors who deploy sophisticated linguistic manipulation in coordinated information campaigns, posing threats to online discourse integrity. While Large Language Models (LLMs) achieve strong performance on general natural language processing (NLP) tasks, they struggle with subtle propaganda detection and operate as ''black boxes'', providing no interpretable insights into manipulation strategies. This paper introduces X-Troll, a novel framework that bridges this gap by integrating explainable adapter-based LLMs with expert-derived linguistic knowledge to detect state-sponsored trolls and provide human-readable explanations for its decisions. X-Troll incorporates appraisal theory and propaganda analysis through specialized LoRA adapters, using dynamic gating to capture campaign-specific discourse patterns in coordinated information operations. Experiments on real-world data demonstrate that our linguistically-informed approach shows strong performance compared with both general LLM baselines and existing troll detection models in accuracy while providing enhanced transparency through expert-grounded explanations that reveal the specific linguistic strategies used by state-sponsored actors. X-Troll source code is available at: https://github.com/ltian678/xtroll_source/.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	Cikm 2025 Proceedings of the 34th ACM International Conference on Information and Knowledge Management
dc.relation.ispartof	Proceedings of the 34th ACM International Conference on Information and Knowledge Management
dc.relation.isbasedon	10.1145/3746252.3761028
dc.rights	info:eu-repo/semantics/openAccess
dc.title	X-Troll: EXplainable Detection of State-Sponsored Information Operations Agents
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-12-22T01:22:35Z
pubs.publication-status	Published

Abstract:

State-sponsored trolls, malicious actors who deploy sophisticated linguistic manipulation in coordinated information campaigns, posing threats to online discourse integrity. While Large Language Models (LLMs) achieve strong performance on general natural language processing (NLP) tasks, they struggle with subtle propaganda detection and operate as ''black boxes'', providing no interpretable insights into manipulation strategies. This paper introduces X-Troll, a novel framework that bridges this gap by integrating explainable adapter-based LLMs with expert-derived linguistic knowledge to detect state-sponsored trolls and provide human-readable explanations for its decisions. X-Troll incorporates appraisal theory and propaganda analysis through specialized LoRA adapters, using dynamic gating to capture campaign-specific discourse patterns in coordinated information operations. Experiments on real-world data demonstrate that our linguistically-informed approach shows strong performance compared with both general LLM baselines and existing troll detection models in accuracy while providing enhanced transparency through expert-grounded explanations that reveal the specific linguistic strategies used by state-sponsored actors. X-Troll source code is available at: https://github.com/ltian678/xtroll_source/.

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http://hdl.handle.net/10453/191068