PersuasionTrace Analysis

• PersuasionTrace is a process-level persuasion analysis method that quantifies belief trajectories via numeric reports collected during multi-turn dialogues.
• It systematically annotates persuader messages with logos, pathos, and ethos, employing models like Bayesian networks, CRFs, and Transformers to capture strategy dynamics.
• Empirical findings indicate differentiated belief shifts with significant effects from pathos and baseline beliefs, highlighting its application in dialogue understanding and agentic execution.

PERSUASIONTRACE is a process-level formulation of persuasion analysis in which the primary object is a trajectory rather than a single endpoint label. In its explicit human–LLM instantiation, it instruments multi-turn persuasion conversations, records turn-by-turn numeric belief reports, annotates persuader messages with logos, pathos, and ethos, and evaluates simulated targets by fidelity to human belief dynamics (Moore et al., 3 Jun 2026). Closely related work uses adjacent trace formalisms—utterance-level persuasive-strategy sequences, Belief–Desire–Intention state logs, debate-state representations, resistance labels, and downstream action traces—suggesting a broader family of persuasion-tracing methods across dialogue understanding, online discourse, debate, agentic execution, and persuasion-effect estimation (Chen et al., 2020, Ma et al., 21 May 2026, Jeong et al., 31 Jan 2026, Jun et al., 30 Sep 2025).

1. Core definition and analytical scope

In the human–LLM framework, targets answer the same question before the dialogue, after each persuader message, and after the dialogue: “How much do you agree with the proposition shown?” on a 0–100 scale. This yields a belief trajectory $(b_{pre}, b_1, b_2, \ldots, b_t, b_{post})$, where $b_t$ is the belief after persuader turn $t$. The persuader’s stance $s$ is assigned from the target’s pre belief $b_{pre}$, and the round-level endpoint metric is

$\Delta = (b_{post} - b_{pre}) \cdot s,$

with positive values indicating movement in the persuader’s assigned direction (Moore et al., 3 Jun 2026).

This process-level view differs from endpoint-only formulations in which persuasion is observed only through final success. In Change My View, successful persuasion is defined by the presence of a $\Delta$ award on a reply, yielding a binary classification problem over replies; in the Truth Wins setting, belief update is directly rated on a continuous scale from $-100$ to $100$ as $\Delta belief = belief_{post} - belief_{pre}$ (Hoang et al., 27 Nov 2025). In regression discontinuity designs, persuasion is localized at a threshold through the RD persuasion rate

$b_t$0

which measures the probability that threshold units would act under exposure given that they would not act without exposure (Jun et al., 30 Sep 2025). These formulations use different observational interfaces, but all treat persuasion as state change under an intervention.

A further expansion appears in agentic systems, where persuasion is defined behaviorally rather than only propositionally. There, persuasion propagation denotes the phenomenon whereby a task-irrelevant persuasive intervention changes an agent’s belief state and this belief persists to influence downstream execution behavior in later, unrelated, long-horizon tasks (Jeong et al., 31 Jan 2026). This suggests that PERSUASIONTRACE is not restricted to belief reports; it can also denote the tracing of exploration breadth, revision dynamics, and stopping behavior.

2. Representational forms of the trace

Across the literature, a persuasion trace is encoded through several recurrent state representations.

Trace representation	State variables	Setting
Belief trajectory	$b_t$1	Human–LLM persuasion (Moore et al., 3 Jun 2026)
Utterance strategy sequence	ER/EE strategy labels plus tactic history	Persuasive dialogue understanding (Chen et al., 2020)
ToM trace	$b_t$2	ToM-based persuasive dialogue (Ma et al., 21 May 2026)
Resistance trace	Target-side resisting strategies per utterance	Persuasion and negotiation (Dutt et al., 2021)
Debate State Representation	$b_t$3 and taxonomic vector $b_t$4	Multi-step debate (Narayana et al., 23 Jun 2026)
Agentic action trace	$b_t$5	Persuasion propagation in agents (Jeong et al., 31 Jan 2026)

In persuasive dialogue understanding, the trace is a sequence-labeling object over utterances in a dyadic dialogue. Given a dialogue $b_t$6, the task is to predict, for each utterance, the persuasive strategy employed. Strategies are speaker-specific: the persuader has 10 categories, the persuadee has 12 categories, and both share a non-strategy dialogue-acts class. The representation explicitly distinguishes inter-speaker contextual relations, intra-speaker contextual relations, and tactic history, where tactic history denotes the sequence of previously used strategy labels in the dialogue (Chen et al., 2020).

In ToM-based persuasive dialogue, the trace is richer and includes latent mental states. The per-turn object is $b_t$7, where $b_t$8 is intention, $b_t$9 is desire, $t$0 is a set of belief statements, and $t$1 is the persuader’s selected strategy. The associated backward inference chain is

$t$2

and the annotation protocol enforces carry-over of unresolved negative beliefs into the next turn (Ma et al., 21 May 2026).

In debate-oriented tracing, the Debate State Representation compresses the live state into $t$3, where $t$4 is the Committed Claims Trajectory and $t$5 is the Active Trigger Flaw Extraction. This state is mapped to a $t$6-dimensional probability vector $t$7, creating a discrete categorical bottleneck over structural vulnerabilities rather than topical content (Narayana et al., 23 Jun 2026). By contrast, resistance-oriented tracing labels the target’s utterances with one of seven resisting strategies—Source Derogation, Counter Argument, Information Inquiry, Personal Choice, Self Pity, Hesitance, and Self-Assertion—yielding a parallel target-side dynamics that complements persuader strategy traces (Dutt et al., 2021).

3. Data collection and annotation protocols

The explicit PERSUASIONTRACE framework is implemented as a web-based experimental platform for AI persuasion experiments with text and audio I/O, participant-chosen personalized propositions, and multi-turn belief elicitation. Participants were recruited on Prolific and interacted with an LLM persuader. Across all analyses, $t$8 completed rounds were collected. Unless otherwise noted, human cohorts used a text-based interface, fixed four-turn dialogues, a 10-minute cap per round, multi-turn belief elicitation, and DebateGPT topic propositions; quality filters excluded rounds with average human message length below 10 characters or average human reply time below 5 seconds (Moore et al., 3 Jun 2026).

The rhetorical annotation pipeline in that framework assigns logos, pathos, and ethos scores to persuader messages on an ordinal scale with 0 = absent, 1 = somewhat present, and 2 = dominant. The guidelines emphasize conservatism and context-aware scoring. The same paper also introduces a related-belief survey arm enabling forced-initialization replay, stance-bias diagnostics, and naive-responsiveness diagnostics for simulated targets (Moore et al., 3 Jun 2026).

Several adjacent datasets instantiate alternative trace granularities. PersuasionForGood contains 1,017 dialogues in a charity-donation setting, of which 300 dialogues are annotated with persuasive strategies; the dialogues average 10.43 turns and 19.36 words per utterance, are split roughly 80/20 into train/test, and are evaluated with macro F1 under five-fold cross-validation because of label imbalance (Chen et al., 2020). ToM-BPD contains 504 dialogues and 3,926 utterances, with average 7.79 turns per dialogue, average persuader utterance length 38.62 tokens, and average persuadee utterance length 19.67 tokens; its annotations include desire labels, belief statements with polarity and reason, and one of nine persuasive strategies, and its quality control includes an annotator qualification test with a 25-turn exam and a 90% threshold (Ma et al., 21 May 2026).

Resistance tracing is instantiated on 530 Persuasion4Good conversations and 800 CraigslistBargain conversations. Agreement before full annotation reached Fleiss’ $t$9 for Persuasion4Good and Fleiss’ $s$0 for CraigslistBargain, after iterative calibration rounds. Multi-label utterances are rare—1.2% in Persuasion4Good and 3.85% in CraigslistBargain—and are reduced to a single label for modeling by random selection (Dutt et al., 2021).

In large-scale online persuasion detection, the primary target corpus is the Winning Arguments Change My View dataset, with 3,456 posts for training and 807 for testing. Each post provides the original post, one positive reply, and one negative reply, with pairs chosen using highest Jaccard similarity. A second dataset, Truth Wins, supplies theory-grounded feature annotations, including belief update on a $s$1 to $s$2 scale and eight feature ratings on a 1–5 Likert scale averaged across 10 raters (Hoang et al., 27 Nov 2025).

4. Modeling paradigms

One major line treats persuasion tracing as contextual sequence labeling. In persuasive dialogue understanding, RoBERTa Large is fine-tuned for utterance-level strategy classification, the final four layer [CLS] activations are averaged into a 1024-dimensional utterance vector, and context is modeled through inter-speaker and speaker-specific encoders. The proposed Transformer-ExtCRF uses a Transformer over the whole dialogue, then speaker-specific Transformers for ER and EE subsequences, followed by an extended linear-chain CRF with four transition matrices for ER$s$3ER, ER$s$4EE, EE$s$5ER, and EE$s$6EE transitions, plus auxiliary losses for speaker-specific CRFs and persuasion outcome prediction (Chen et al., 2020).

A second line models latent cognitive state explicitly. In ToM-PD, TTBYS decomposes reasoning into three stages: desire inference, belief inference, and strategy prediction. Desire is inferred by fusing an experience-driven distribution $s$7 with an LLM distribution $s$8 through coefficient $s$9; belief is generated with explicit exemplar injection; strategy is selected by combining retrieved experiential priors $b_{pre}$0 with LLM probabilities $b_{pre}$1 through coefficient $b_{pre}$2 (Ma et al., 21 May 2026). In the human–LLM framework, the Bayesian-network target instead decomposes each persuader turn into atomization, Bayesian state update, and verbalization. It maintains a small Bayesian network over proposition and related beliefs, updates node probabilities through rhetoric-weighted evidence, and generates a response conditioned on the updated BN state and persona (Moore et al., 3 Jun 2026).

A third line uses lightweight discriminative models over structured features or internal representations. In online discourse, three LLMs—LLaMA3-70B, Gemma2-9B, and Mixtral-8x7B—rate replies along eight theory-driven features, including Influential, Interesting, Interesting-If-True, Positive emotion, Negative emotion, Shareable, Truthfulness, and Attention. These ratings, plus a predicted belief-update score from an OLS model trained on Truth Wins, feed a Random Forest with 300 estimators; the paper distinguishes Hybrid-Independent and Hybrid-Interaction variants (Hoang et al., 27 Nov 2025). In multi-turn conversations, linear probes on frozen Llama-3.2-3B residual activations at middle-to-late layers are trained for binary persuasion success, three-way strategy classification over logos/pathos/ethos-style categories, and OCEAN personality inference. These probes operate at token or turn granularity and use logistic or softmax classifiers on cached hidden states (Jaipersaud et al., 7 Aug 2025).

A fourth line is retrieval-centered. TS-RAG routes debate states through a taxonomic strategy space $b_{pre}$3, maps a live state to $b_{pre}$4, and retrieves domain-agnostic blueprints by cosine similarity

$b_{pre}$5

Its purpose is to decouple argumentative structure from topical content and to prevent semantic leakage in cross-domain persuasion (Narayana et al., 23 Jun 2026). In agentic execution, persuasion propagation is modeled causally by an action policy

$b_{pre}$6

with belief updates $b_{pre}$7 and behavior-level outcomes extracted from the action trace (Jeong et al., 31 Jan 2026).

5. Empirical findings

The human–LLM PERSUASIONTRACE results show that belief trajectories are not homogeneous. KMeans on standardized normalized cumulative trajectories yielded two clusters: a low-shift cluster with $b_{pre}$8 and mean end-delta 0.039, and a higher-shift cluster with $b_{pre}$9 and mean end-delta 0.437. In a logistic model of cluster membership, pathos was positively associated with membership in the higher-shift cluster ($\Delta = (b_{post} - b_{pre}) \cdot s,$0, SE = 0.37, $\Delta = (b_{post} - b_{pre}) \cdot s,$1), and baseline belief was also positive ($\Delta = (b_{post} - b_{pre}) \cdot s,$2, SE = 0.35, $\Delta = (b_{post} - b_{pre}) \cdot s,$3). Across standard text, personalized text, and audio cohorts, LLM persuaders were significantly more persuasive than control. For simulation fidelity, the BN target scored 81.3 in LLM-judge human-likeness versus a human reference at 80.0, while unstructured and structure-conditioned LLM targets scored 64.7 and 64.2; in forced-initialization replay, the BN target had the lowest conditional replay error at 0.1429 (Moore et al., 3 Jun 2026).

The strategy-recognition literature records two negative results that are central for persuasion tracing. First, CRF did not capture persuasive label dependencies: adding CRF layers yielded negligible or inconsistent improvements, and transition heatmaps showed that beyond a few frequent combinations, most label transitions were low-frequency. Second, Transformer encoders trained from scratch were less capable of capturing sequential information than LSTM-based contextual encoders, which the paper attributes to the limitations of vanilla self-attention with sinusoidal positional encodings under sparse annotated data. On PersuasionForGood, cLSTMs reached Macro F1 65.5 for ER and 52.5 for EE, whereas plain Transformers reached 64.6 and 51.4; the proposed Transformers-ExtCRF reached 65.2 and 51.6 (Chen et al., 2020).

In online persuasion detection, theory-grounded feature extraction with LLMs and Random Forests substantially exceeded lexical and zero-shot baselines. On the CMV test set, the transparent logistic-regression baseline on term frequencies achieved 56.50% accuracy, zero-shot LLM baselines ranged from 56.90% to 64.93%, and hybrid models ranged from 72.55% to 82.69%, with Gemma2 Hybrid-Interaction performing best at 82.69%. Permutation-based variable importance showed that epistemic-emotion signals—especially Interesting and Interesting-If-True—and social transmission via Shareable were consistently among the strongest predictors, with Influential and Attention also prominent (Hoang et al., 27 Nov 2025).

Linear probes recover fine-grained temporal structure. In PersuasionForGood, AUROC for success detection peaks in middle turns; in DailyPersuasion, the signal concentrates in the final turns, with the context probe achieving turn classification accuracy of 91% and conversation classification accuracy of 95% on DP. The probe-based strategy distribution is closer to a GPT-4.1-Nano reference than prompting across most turns, and credibility appeals dominate much of PersuasionForGood (Jaipersaud et al., 7 Aug 2025).

Debate and agentic systems extend the empirical picture from belief trajectories to trace-level control and downstream behavior. In held-out-domain debate, TS-RAG improved a weak persuader against a strong opponent from 70.5 to 78.5 win rate and reduced average rounds from 15.19 to 15.0; removing OP resistance constraints caused evaluation collapse by sycophancy, such as a Flash 3.0 Self-Routed Heuristics win rate inflating to 93.0 (Narayana et al., 23 Jun 2026). In agentic web research, belief-prefilled agents conducted on average 26.9% fewer searches and visited 16.9% fewer unique sources than neutral-prefilled agents; by contrast, on-the-fly persuasion produced weak and inconsistent aggregate effects, with persona-level dispersion often larger than pooled means (Jeong et al., 31 Jan 2026).

6. Extensions, limitations, and open problems

PERSUASIONTRACE has been extended beyond multi-turn dyadic dialogue. In persuasive requests on r/Borrow, strategy orderings are represented as sentence-level sequences over Concreteness, Reciprocity, Impact, Credibility, Politeness, and boundary markers SOS/EOS. The central finding is that specific triplets interact with content and outcome: for example, $\Delta = (b_{post} - b_{pre}) \cdot s,$4 has success 0.82, whereas concreteness-heavy triplets such as $\Delta = (b_{post} - b_{pre}) \cdot s,$5 have success 0.27, and there is a strong negative correlation between triplet-level strategy attention and empirical success ($\Delta = (b_{post} - b_{pre}) \cdot s,$6, $\Delta = (b_{post} - b_{pre}) \cdot s,$7) (Shaikh et al., 2020). In text-only tactic detection, a domain-independent unsupervised framework classifies persuasive spans into 14 tactic types by normalized edit distance in parse-string space, yielding ParseTree+SP multi-class F1 of 0.489 on ChangeMyView and 0.501 on Supreme Court data, with better performance than a Doc2Vec baseline across all four evaluated domains (Iyer et al., 2019). In advertising, persuasion traces become multimodal: a 3,000-image corpus annotated with 20 strategies in 9 groups supports a multi-task attention fusion model that reaches 59.2% top-1 and 84.8% top-3 accuracy for strategy prediction, with OCR and DenseCap contributing strongly to performance (Singla et al., 2022).

Limitations recur across these formulations. Data scarcity, label imbalance, and sparse transitions remain a major constraint: the persuasive-strategy sequence-labeling study uses only 300 annotated dialogues and reports weak label grammar (Chen et al., 2020). Online discourse detection is evaluated on CMV, which the paper explicitly cautions is a persuasion-focused environment; it reports accuracy but not precision, recall, F1, ROC-AUC, calibration, confidence intervals, significance tests, or ablations (Hoang et al., 27 Nov 2025). The human–LLM framework relies on self-reported numeric beliefs, proposition-specific Bayesian-network construction, and modest sample sizes; it does not model social or affective mechanisms such as trust dynamics or identity threat (Moore et al., 3 Jun 2026). ToM-BPD is limited to 504 dialogues and does not directly annotate intention, which is summarized instead through $\Delta = (b_{post} - b_{pre}) \cdot s,$8 (Ma et al., 21 May 2026). In debate systems, evaluation collapse under default sycophancy necessitates stringent constraints such as Pre-Concession Analysis and the Concession Rule (Narayana et al., 23 Jun 2026). In agentic persuasion propagation, the on-the-fly comparison between persuaded and non-persuaded trials is explicitly post-treatment and can carry selection bias (Jeong et al., 31 Jan 2026).

Taken together, these findings suggest several open directions. Relative-position modeling and pretrained conversational encoders are repeatedly proposed where vanilla Transformers fail under small data (Chen et al., 2020). Richer ToM knowledge bases, causal models of how $\Delta = (b_{post} - b_{pre}) \cdot s,$9 changes $\Delta$0 or $\Delta$1, and user-specific long-term traces are proposed for ToM-based persuasion (Ma et al., 21 May 2026). Domain adaptation, rolling human validation, and broader metric suites are recommended for online persuasion detection (Hoang et al., 27 Nov 2025). In the human–LLM framework, a central safety direction is to optimize with fidelity metrics rather than raw endpoint movement, so that persuasion systems are constrained by human-likeness, stance symmetry, and resistance to naive persuasion rather than only by net movement (Moore et al., 3 Jun 2026).