Argument structures represent how individual arguments interact within a discourse. However, leveraging these structures to infer meaningful knowledge remains challenging, as it requires a formal representation of how argumentative influence propagates across the structure. We propose Neuro-symbolic Argumentation Reasoning System (NARS) for performing persuasiveness detection directly from online discussions. The neural component employs retrieval-augmented iterative LLM reasoning to extract bipolar argument structures, while the symbolic component applies gradual argumentation semantics to propagate argumentative influence over the structure. Using the Webis-CMV-20 corpus, we construct CMV2BAS, a dataset of long, multi-threaded ChangeMyView discussions annotated with LLM-derived bipolar argument structures and labeled with delta events (view change vs.\ no change). We investigate whether gradual argumentation semantics can effectively detect persuasiveness, whether augmenting them with argument quality and graph-based centrality improves performance, and whether symbolic reasoning remains necessary in the presence of modern LLMs. NARS achieves F1=0.57 and AUC=0.56 under its best configuration, outperforming zero-shot Qwen3.6:27B and GPT-oss:20B. We identify support-dominant discussions and long argumentative chains as key failure modes limiting attack influence propagation. Our work demonstrates the feasibility of operationalizing formal gradual argumentation semantics over noisy real-world discourse for scalable macro-level persuasion analysis, while highlighting key challenges and directions for future research in neuro-symbolic argumentation reasoning. We release both CMV2BAS dataset and the NARS framework for public use.
Figures & tables
Figure 1: Overview of the NARS architecture, illustrating the neural component and its steps, the symbolic component and the downstream persuasiveness detection module.
p(t)=(W+)⊤s(t),q(t)=(W−)⊤s(t)
Algorithm 2 Iterative Strength Propagation using Gradual Semantics
GPT-oss:20B (medium thinking) [ 31 ] ; batch=10 EDUs ± 2 sentences for context; self-consistency with temp={0.1, 0.2, 0.15 (tie-breaker)}; max 8,192 tokens; JSON structured output
S4 BAS Construction
Pcentral via GPT-oss:20B (medium thinking); discarded : only main component containing Pcentral ; synthetic : cosine-similarity repair via all-MiniLM-L6-v2 ; min. 3 nodes in final graph
S5 Strength Init.
UI / GI / TI / HI / Ensemble; W+ =1.0/ W− =3.0; synthetic edge weight=0.7
S6 Gradual Semantics
λ =0.5, τ =0.2, β =0.1, ε =1e-6, Tmax =1,000
Table 1: Implementation details per pipeline step, reflecting the most efficient configuration identified through empirical investigation.
Category
Method
Accuracy
F1
AUC
Naive Baseline
Random
0.5025
0.5266
0.5000
Log. Regression
0.5392
0.5635
0.5869
NARS (discarded); δ=0.3
UI
0.5417 0.4890.591
0.5165 0.4510.575
0.5620 0.5020.616
GI
0.5000 0.4510.549
0.5450 0.4860.604
0.4697 0.4130.529
TI
0.4635 0.4110.513
0.4976 0.4340.556
0.4638 0.4090.525
HI
0.4505 0.4010.505
0.4548 0.3920.517
0.4605 0.4040.516
Table 2: Evaluation of persuasiveness detection across modeling paradigms on CMV2BAS ( n=384 ). NARS results report mean with 95% confidence interval through bootstrapping ( lowerupper , B=1000 ) at δ=0.3 . LLM baselines report point estimates only. Bold denotes best per column among NARS configurations.
Figure 2: Effect of persuasiveness sensitivity threshold δ on F1 score (left) and prediction bias (right) across initialization strategies in synthetic mode ( n=384 ).
Large language models (LLMs) can generate apparently highly persuasive text, but does sounding persuasive mean arguing well? We introduce Persuasio, a multi-agent dialogue platform grounded in a formal argumentation-based theory of persuasion dialogues that adjudicates logical winners during free-text debates. Using this system, we generated 192 debates on a UK political topic between humans and LLMs, and evaluated 22 interlocutors through both automated adjudication and 9,702 crowdsourced pairwise judgements across 1{,}386 annotation instances. We observed a consistent decoupling between subjective and formal persuasiveness: LLMs dominated the subjective ranking yet performed substantially worse under argumentation-theoretic adjudication, where humans remained competitive. Multi-agent and retrieval-augmented variants further widened this divergence. These findings reveal a systematic gap between rhetorical fluency and formal argumentative strength in LLM-based persuasive dialogues.
Jordan Robinson, Angus R. Williams, Katie Atkinson +1
University of Liverpool · 2The Alan Turing Institute · University of Leeds
Can narratives make arguments more persuasive? And to this end, which narrative features matter most? Although stories are often seen as powerful tools for persuasion, their specific role in online, unstructured argumentation remains underexplored. To address this gap, we present ARGUS, a framework for studying the impact of narration on persuasion in argumentative discourse. ARGUS introduces a new ChangeMyView corpus annotated for story presence and six key narrative features, integrating insights from two established theoretical frameworks that capture both textual narrative features and their effects on recipients. Leveraging both encoder-based classifiers and zero-shot large language models (LLMs), ARGUS identifies stories and narrative features and applies them at scale to examine how different narrative dimensions influence persuasion success in online argumentation.
Sara Nabhani, Federico Pianzola, Khalid Al-Khatib +1
Philosophical accounts of persuasion often assume that shared evidence and rational argumentation should lead to a convergence of views between peers, yet everyday discourse often suggests otherwise. In this study, we use large language models to analyze a corpus of debates on Reddit's r/ChangeMyView, where belief revision is publicly signaled. Large language models were asked, halfway through each discussion, to forecast whether such an acknowledgement would arise; their probabilistic estimates serve as a conversational baseline. Each reply was then coded, through a hybrid machine-assisted procedure, for ten familiar rhetorical strategies -- concession, empathy, logical challenge, credibility appeals, and so forth. Adding these strategic features markedly improves predictive power and yields a consistent pattern: moves that express concession or empathetic alignment substantially increase the prospect of belief change, whereas frontal refutation, credibility attacks, and topic deflection diminish it. The findings indicate that effective public reasoning depends as much on relational framing as on evidential content, and they invite a refinement of normative accounts of rational dialogue.