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arxiv: 2604.16935 · v1 · submitted 2026-04-18 · 💻 cs.AI · cs.CY· cs.HC· cs.LG· cs.SI

Recognition: unknown

LLMs can persuade only psychologically susceptible humans on societal issues, via trust in AI and emotional appeals, amid logical fallacies

Alexis Carrillo , Salvatore Citraro , Ali Aghazhadeh Ardebili , Enrique Taietta , Giulio Rossetti , Emilio Ferrara , Giuseppe Alessandro Veltri , Massimo Stella
Authors on Pith no claims yet

Pith reviewed 2026-05-10 07:12 UTC · model grok-4.3

classification 💻 cs.AI cs.CYcs.HCcs.LGcs.SI
keywords LLM persuasionpsychological susceptibilitylongitudinal AI interactionlogical fallaciesopinion changeexplainable AIsocietal topicspersonality traits
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The pith

LLMs persuade humans on societal issues only among those who trust AI and show agreeable, extraverted personalities with high need for cognition.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Talk2AI, a longitudinal setup that tracks how four leading LLMs attempt to shift 770 participants' views on polarizing topics across 3,080 conversations and 60,000 turns. It documents stable anchoring to initial opinions despite repeated exposure, yet detects measurable opinion change that explainable AI links to specific traits: greater trust in LLMs, agreeableness, extraversion, and need for cognition. Both humans and LLMs produce logical fallacies at the same rate of roughly one every six statements, showing no reasoning advantage on the AI side. Perceived humanness of the LLM is the most predictable outcome from sociodemographic and psychological features, while conviction, opinion shift, and personal endowment follow with lower accuracy. The findings matter because they map concrete psycho-social pathways through which generative AI can influence public discourse on platforms.

Core claim

In the Talk2AI four-wave study, participants maintained longitudinal inertia in their initial stances on issues such as climate change and misinformation even after repeated LLM arguments, while NLP analysis showed equivalent fallacy rates between humans and models; explainable AI then isolated the subset of individuals susceptible to opinion change as those with higher trust in LLMs, agreeableness, extraversion, and need for cognition, with these results replicated via multiverse mixed-effects models that also confirmed strong individual differences.

What carries the argument

The Talk2AI longitudinal conversation framework combined with explainable AI (XAI) analysis of sociodemographic, psychological, and engagement features to isolate markers of susceptibility to LLM-driven opinion change.

If this is right

  • Initial convictions display inertia across repeated waves of AI exposure.
  • LLM perceived humanness is the outcome most strongly predicted by participant features with R squared of 0.44.
  • Opinion change occurs selectively in individuals scoring higher on trust in LLMs, agreeableness, extraversion, and need for cognition.
  • Humans and LLMs rely on fallacious reasoning at identical rates of one quip in six.
  • Mixed-effects models reveal substantial individual differences in persuasion outcomes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • AI interfaces on public platforms could incorporate safeguards that limit emotional appeals when engaging users who exhibit the identified susceptibility profile.
  • Teaching recognition of logical fallacies to the general public might blunt the effectiveness of LLM arguments independent of personality traits.
  • Long-term studies tracking whether reported opinion shifts translate into sustained changes in information-seeking or policy preferences would test the durability of these effects.
  • Developers might design models that explicitly flag their own fallacious statements to reduce unintended persuasion.

Load-bearing premise

Participants' self-reported conviction levels, perceived opinion shifts, and self-donations accurately reflect genuine belief changes rather than demand characteristics or social desirability biases created by the AI conversation setting.

What would settle it

A replication that measures actual downstream behaviors such as real charitable donations or voting intentions on the same topics and finds no correlation with the self-reported opinion changes recorded after the LLM conversations.

Figures

Figures reproduced from arXiv: 2604.16935 by Alexis Carrillo, Ali Aghazhadeh Ardebili, Emilio Ferrara, Enrique Taietta, Giulio Rossetti, Giuseppe Alessandro Veltri, Massimo Stella, Salvatore Citraro.

Figure 1
Figure 1. Figure 1: Infographics for Talk2AI’s features. 3. Results 3.1. Feedback Dynamics [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Feedback transitions as Sankey flows across the four waves and Markov state transition patterns grouped by [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: (A-B) Frequency distribution of fallacies across quips, grouped by topic (A) and by LLM (B). (C-D) Fallacy [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (A) R 2 values of the feature selection method for identifying the best performing feature set (top); Scatter plot where each point is a feature, described by the last iteration li where it occurs according to feature selection and its relative R 2 value at li (bottom). (B) Feature importance according to beeswarms highligthing Shapley values. Feature space chosen as the union of the top-5 best performing … view at source ↗
read the original abstract

Scarce longitudinal evidence examines LLMs' persuasiveness and humanness along time-evolving psychological frameworks. We introduce Talk2AI, a longitudinal framework quantifying psycho-social, reasoning and affective dimensions of LLMs' persuasiveness about polarizing societal topics. In a four-way longitudinal setup, Talk2AI's 770 participants engaged in structured conversations with one of four leading LLMs on topics like climate change, social media misinformation, and math anxiety. This produced 3,080 conversations over 60,000 turns. After each wave, participants reported conviction in their initial topic stance, perceived opinion change, LLM's perceived humanness, a self-donation to the topic and a textual explanation. Feedback time series showed longitudinal inertia in convictions, indicating some human anchoring to initial opinions even after repeated exposure to AI-generated arguments. Interestingly, NLP analyses revealed that both humans and LLMs relied on fallacious reasoning in 1 conversational quip every 6, countering the ``LLMs as superior systems" stereotype behind LLMs' cognitive surrender. LLMs' perceived humanness was most learnable from sociodemographic, psychological and engagement features ($R^2=0.44$), followed by opinion change ($R^2=0.34$), conviction ($R^2=0.26$) and personal endowment ($R^2=0.24$). Crucially, explainable AI (XAI) indicated: (i) the presence of individuals more susceptible to LLM-based opinion changes; (ii) psychological susceptibility to LLM-convincing consisted of having more trust in LLMs, being more agreeable and extraverted and with a higher need for cognition. A multiverse approach with mixed-effects models confirmed XAI results, alongside strong individual differences. Talk2AI provides a grounded framework and evidence for detecting how GenAI can influence human opinions via multiple psycho-social pathways in AI-human digital platforms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The manuscript introduces the Talk2AI longitudinal framework to quantify psycho-social, reasoning, and affective dimensions of LLM persuasiveness on polarizing societal topics. In a four-wave design, 770 participants engaged in 3,080 structured conversations with one of four leading LLMs on topics including climate change, social media misinformation, and math anxiety. Key results include longitudinal inertia in self-reported conviction, fallacious reasoning occurring in approximately one conversational quip every six for both humans and LLMs, and XAI analyses showing that perceived opinion change, conviction, humanness, and endowment are predictable from sociodemographic, psychological, and engagement features (R² ranging from 0.24 to 0.44), with susceptibility linked specifically to higher trust in LLMs, agreeableness, extraversion, and need for cognition; these XAI findings are corroborated by multiverse mixed-effects models.

Significance. If the self-reported measures validly index genuine persuasion rather than artifacts, the work supplies rare longitudinal evidence on AI-human opinion dynamics at scale, identifies replicable individual-difference pathways (trust, personality, cognition), and documents equivalent fallacy rates that challenge assumptions of LLM cognitive superiority. The combination of large conversation corpus, repeated-measures design, XAI interpretability, and multiverse robustness checks constitutes a concrete methodological contribution to human-AI interaction research.

major comments (1)
  1. [Abstract / XAI results] Abstract and XAI results section: the central claim that LLMs 'can persuade only psychologically susceptible humans ... via trust in AI and emotional appeals' rests on XAI feature importances and mixed-effects models predicting self-reported 'perceived opinion change' and conviction. No objective validation of these dependent variables (implicit measures, behavioral choice tasks, or blinded follow-up assessments) is described, leaving open the possibility that reported changes reflect demand characteristics or social-desirability biases—especially among high-agreeableness or high need-for-cognition participants in a repeated-exposure design.
minor comments (3)
  1. [Abstract] The abstract states fallacious reasoning occurs '1 conversational quip every 6' but does not define 'quip' operationally or report inter-annotator agreement for the NLP pipeline used to detect fallacies.
  2. [Abstract] R² values for humanness (0.44), opinion change (0.34), conviction (0.26), and endowment (0.24) are reported without accompanying standard errors, confidence intervals, or baseline model comparisons.
  3. [Title / Abstract] The title asserts persuasion occurs 'via ... emotional appeals,' yet the XAI susceptibility profile listed in the abstract emphasizes trust, agreeableness, extraversion, and need for cognition without isolating emotional-appeal features or their incremental contribution.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback, which helps clarify the scope and limitations of our Talk2AI study. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract / XAI results] Abstract and XAI results section: the central claim that LLMs 'can persuade only psychologically susceptible humans ... via trust in AI and emotional appeals' rests on XAI feature importances and mixed-effects models predicting self-reported 'perceived opinion change' and conviction. No objective validation of these dependent variables (implicit measures, behavioral choice tasks, or blinded follow-up assessments) is described, leaving open the possibility that reported changes reflect demand characteristics or social-desirability biases—especially among high-agreeableness or high need-for-cognition participants in a repeated-exposure design.

    Authors: We appreciate this important observation on the validity of our dependent variables. The manuscript relies exclusively on self-reported measures of perceived opinion change, conviction, humanness, and endowment, with no implicit measures, behavioral choice tasks, or blinded follow-up assessments included in the four-wave protocol. We acknowledge that demand characteristics and social-desirability biases remain plausible alternative explanations, particularly given the repeated-exposure design and the role of agreeableness and need for cognition as predictors. At the same time, the observed longitudinal inertia in convictions (many participants maintained initial stances across waves) provides some counter-evidence to uniform compliance effects, and the susceptibility profile identified by XAI aligns with established theories of persuasion. The multiverse mixed-effects models further incorporate individual-difference controls. We will revise the manuscript to (a) add an explicit limitations subsection discussing the absence of objective validation, (b) qualify the central claim to refer specifically to self-reported perceived changes rather than implying objective persuasion, and (c) outline future directions for behavioral and implicit-measure extensions. These changes constitute a partial revision. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical associations derived from independent participant data

full rationale

The paper reports a longitudinal empirical study with 770 participants producing 3080 conversations, self-reported outcomes (conviction, perceived opinion change, endowment), NLP fallacy counts, mixed-effects regressions, and XAI feature importance. No equations, ansatzes, or derivations appear that reduce any claimed result to a fitted parameter or self-citation by construction. Central findings on susceptibility profiles (trust, agreeableness, extraversion, need for cognition) are statistical associations extracted from the collected data rather than tautological redefinitions of the inputs. The multiverse confirmation and R² values for learnability are standard model diagnostics, not load-bearing self-references. The derivation chain remains self-contained against the external benchmark of participant reports and model outputs.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim depends on the validity of self-reported psychological measures and the assumption that the selected LLMs and topics generalize to broader AI-human interactions.

free parameters (1)
  • Selection of four leading LLMs and three societal topics
    The specific models and discussion topics were chosen by the authors and could influence observed persuasion rates and fallacy frequencies.
axioms (1)
  • domain assumption Self-reported conviction and perceived opinion change reliably measure actual belief shifts
    The study uses post-conversation self-reports as primary outcome variables without external behavioral validation.

pith-pipeline@v0.9.0 · 5696 in / 1480 out tokens · 61746 ms · 2026-05-10T07:12:34.588590+00:00 · methodology

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