{"id":"d3d69612-3c83-435c-b48d-6ea96a7c7612","arxiv_id":"2505.12248","paper_version":1,"verdict":"UNVERDICTED","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"This is a student dissertation proposal outlining future work to build a taxonomy, dataset, and LLM benchmark for distinguishing rational persuasion from manipulation; no experiments or results are reported.","lead":"This paper is a PhD dissertation proposal that plans to build a taxonomy of persuasive techniques, a human-annotated dataset, and benchmarks for LLMs to distinguish rational persuasion from manipulation. A smart generalist might read it to see an early-stage academic plan for AI safety regulation, but the proposal contains no results yet.","discovery_kind":"unclear","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Planned dataset's construct validity is unsupported: Sec. 2.3 defines manipulation via sender intent and covertness, but annotators will label public r/ChangeMyView comments (Sec. 3.1) without access to intent, so the promised ground-truth labels are not operationalized.","rationale":"The reader identifies inter-annotator agreement as the weakest assumption. I agree with that general worry but refine it: the deeper issue is not only reliability but construct validity. The paper's definitions in Sec. 2.3 include hidden mental states (intent, covertness), while the data source in Sec. 3.1 offers no access to those states. This is a specific, load-bearing weakness in the plan to create a benchmark dataset. However, because the paper is a proposal with no empirical results, the reader's UNVERDICTED verdict remains appropriate; the concern strengthens the case for unverdictability rather than moving to rejection. Independent support is minimal: the paper cites prior theoretical work [7,18] and an existing source dataset [19], but it presents no taxonomy, no annotated examples, and no annotation protocol beyond a pilot test. The proposed concrete test would settle whether the construct can be operationalized in the chosen source.","tokens_in":5071,"tokens_out":5752,"duration_ms":59934,"concrete_test":"Run a construct-validity pilot before full annotation: select a random sample of 100 WinningArguments comments, have the research team annotate them using the proposed taxonomy, and separately contact the original comment authors to ask whether they intentionally attempted to covertly exploit readers' cognitive vulnerabilities. If annotator labels do not match authors' self-reported intent substantially better than chance, the annotation protocol cannot ground the definitions in Sec. 2.3, and the proposed dataset cannot serve as a benchmark. If author self-reports are unobtainable, that unavailability is itself evidence that the chosen source cannot supply the required ground truth.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that the author will fill a gap by producing a human-annotated dataset of rational persuasion vs. manipulation. For that dataset to be a valid ground truth, labels must correspond to the definitions used in Sec. 2.3, where manipulation is 'intentionally and covertly influencing' and rational persuasion includes sender intent. But the planned source, r/ChangeMyView comments, provides annotators with text only; a writer's actual intention and covertness are not observable. The proposal mentions tutorial sessions and a pilot test, which address inter-annotator agreement but not construct validity. Even perfect agreement would not show that annotators are recovering the intended construct. Without a method for grounding intent, the 2,000-comment dataset cannot support the proposed LLM benchmark, and the claim to fill the gap is not yet secured.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"This manuscript is a PhD-symposium dissertation proposal on distinguishing rational persuasion from manipulation in the era of generative AI. The author argues that existing NLP research treats persuasion at too coarse a level, that no dataset currently exists with human-annotated labels for rational persuasion versus manipulation, and that LLMs should be evaluated on their ability to make this distinction. The proposed work has three stages: (1) build a taxonomy of persuasive techniques, construct a roughly 2,000-comment human-annotated dataset from the WinningArguments/r/ChangeMyView corpus, and evaluate baseline LLMs via zero-shot and few-shot prompting; (2) design prompt-engineering, fine-tuning, or framework methods to improve LLM classification; and (3) contribute the resulting taxonomy, dataset, and benchmark to AI-safety research. The paper contains no empirical data, experiments, or formal derivations; it is a research plan.","tokens_in":5343,"tokens_out":2372,"duration_ms":27297,"significance":"If the proposed dataset and benchmark are built and validated, they could be a useful resource for NLP and AI-safety research on persuasive LLMs, particularly in light of the EU AI Act's prohibitions on manipulative AI. The paper's strengths are its clear framing of the rational-persuasion-versus-manipulation distinction, its integration of relevant literature from philosophy, psychology, and NLP, and its concrete plan to use an existing public dataset rather than collecting new raw data. However, the central contribution is entirely prospective: the manuscript reports no results, no annotation, and no evaluation. The significance therefore hinges on whether the planned annotation can actually instantiate the constructs defined in Section 2.3, and on whether the claimed gap (no existing dataset) is established by a systematic search. These points are presently not secured.","major_comments":[{"comment":"The proposed dataset's construct validity is not operationalized. Section 2.3 defines manipulation as 'intentionally and covertly influencing' decision-making and rational persuasion as involving sender intent, but Section 3.1 plans to annotate public r/ChangeMyView comments, where annotators see only text and cannot observe the sender's actual intention or covertness. The manuscript says the research team will hold tutorial sessions and a pilot test, but these address inter-annotator agreement, not whether the labels correspond to the stated constructs. Please specify observable textual criteria or a coding protocol that operationalizes intent and covertness, or explicitly revise the construct definitions to what can be annotated from text, and discuss how the resulting labels support the proposed LLM benchmark.","section":"Section 2.3 and Section 3.1"},{"comment":"The load-bearing claim that 'there is no existing dataset built on this classification in the literature' is asserted without a systematic search protocol. The author does not state which databases were searched, which queries were used, which inclusion/exclusion criteria were applied, or how prior persuasion datasets (e.g., [10, 19, 20]) were checked for relevant sublabels. Because this gap motivates the entire dissertation, please provide a reproducible search method or weaken the claim to a scoped statement (e.g., 'no dataset with explicit rational-persuasion-versus-manipulation labels that we located'), so a reviewer or reader can verify the gap.","section":"Section 3.1"},{"comment":"The annotation plan does not address measurement reliability beyond mentioning tutorial sessions and a pilot. For a dataset intended as a benchmark, it needs explicit inter-annotator agreement targets (e.g., Cohen's kappa or Krippendorff's alpha), a plan for resolving disagreements, and a description of how annotation guidelines will be iterated. Additionally, r/ChangeMyView is a deliberative, good-faith debate community; its comments may be unrepresentative of manipulative persuasion, which is often covert and may not appear in public argumentative contexts. Please discuss how the sampling frame affects the prevalence and representativeness of the two target classes and whether the dataset can support claims about manipulation in general.","section":"Section 3.1"}],"minor_comments":[{"comment":"The model is repeatedly called the 'Heuristics and Systematic Model'; the standard name is the 'Heuristic-Systematic Model,' and 'heuristics' should be singular. Please correct this terminology throughout.","section":"Section 2.4"},{"comment":"The target of roughly 1,000 comments per class is stated without justification. Please explain how this size was chosen (e.g., annotation budget, expected class balance, or desired statistical power for the downstream LLM evaluation).","section":"Section 3.1"},{"comment":"Several references are cited only by URL (e.g., [1], [6], [15]). For reproducibility, provide version numbers, access dates, and stable identifiers or DOIs where available.","section":"References"}],"recommendation":"major_revision","confidential_remarks":"This manuscript is a PhD-symposium proposal rather than a completed study, and the main claims are plans for future work. If the venue's scope includes research proposals at an early stage, the framing is appropriate; if the journal expects substantive results, the paper is not yet suitable. The construct-validity concern in the stress test is real and should be the central focus of revision: without operationalizing intent and covertness, the proposed dataset cannot serve as the promised ground truth. I would encourage the editor to send the revised version to a reviewer with annotation-methodology expertise."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"This is a PhD symposium proposal, not a results paper. The author proposes a taxonomy, a ~2,000-comment annotated dataset, and an LLM evaluation to distinguish rational persuasion from manipulation. The conceptual distinction already exists in the cited literature [7, 11, 18]; what's new is the plan to build resources around it. The writing is clear, the motivation is relevant given the EU AI Act, and the author is appropriately upfront that everything is future work. That honesty earns credit.\n\nThe paper does several things well. It correctly notes that NLP persuasion research tends to treat persuasion as a single category, ignoring the rational/manipulative distinction. The two-stage plan—baseline evaluation, then improvement with cognitive-theory-inspired prompting—is sensible. The connection to dual-process theory is plausible, though it is a hypothesis, not a result.\n\nThe soft spots are real, and the stress-test note lands. The definition of manipulation used in Sec. 2.3 hinges on sender intent and covertness. But the planned data source, r/ChangeMyView comments, gives annotators only the text. They can guess intent but cannot observe it. Pilot tests and tutorial sessions address inter-annotator reliability, not construct validity. Even perfect agreement would not show that the labels measure what the paper claims they measure. This is the central weakness of the proposal, and it needs a fix before the dataset can support the benchmark. One possibility is to define manipulation in terms of observable textual tactics rather than unobservable intent, or to generate examples where intent is known by construction.\n\nMinor issues: the claim that no existing dataset exists (Sec. 3.1) is asserted without a systematic search, and r/ChangeMyView is a good-faith debate setting where manipulative comments may be rare, creating possible imbalance or unrepresentativeness. Those are worth noting but not fatal.\n\nWho is this for? Anyone working on persuasive AI safety or on the design of datasets for ethically loaded constructs might find the proposal a useful starting point. As a research preprint it has no citable results yet. For a PhD symposium, it is appropriate; a serious referee would give useful feedback, mainly on the construct validity issue. I would not desk reject it if the venue accepts proposals, but I would recommend revision to make the annotation protocol operationalize the definition.\n\nIf you are in this area, it is worth a quick read as a proposal example. If you are asked to referee, the key point to raise is the intent problem.","headline":"A clear, honest PhD proposal that identifies a real gap, but the annotation plan does not operationalize the definition of manipulation; the construct validity problem is load-bearing.","tokens_in":5723,"tokens_out":2201,"would_cite":false,"duration_ms":23863,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This dissertation proposal claims that the missing empirical resource for AI persuasion safety is a human-annotated dataset that separates rational persuasion from manipulation, and it sets out to build one.","keywords":["persuasion","manipulation","LLM safety","human annotation","taxonomy","EU AI Act","persuasive AI","cognitive bias"],"falsifier":"The central gap claim would be falsified by locating any published dataset that already carries human labels for rational persuasion versus manipulation; the annotation plan would be falsified by a pilot study in which independent annotators only reach low agreement (for example, Cohen's kappa below 0.4) on the proposed taxonomy.","tokens_in":4859,"feed_emoji":"🧠","tokens_out":6147,"duration_ms":54937,"temperature":0.7,"pith_summary":"This paper is a dissertation proposal arguing that one specific gap blocks progress on AI persuasion safety: there is no human-annotated dataset that labels persuasive texts as either rational persuasion or manipulation. To close that gap, the author plans a three-part project: design a taxonomy that sorts persuasive techniques by whether they appeal to reason or exploit cognitive biases, annotate roughly 2,000 comments from a public online-discussion dataset, and evaluate whether current large language models can make the same distinction. The author contends that this distinction matters because the EU AI Act prohibits AI that uses manipulative techniques to impair informed decision-making, making safe-versus-unsafe persuasion a regulatory and technical problem. A successful dataset would let researchers benchmark and train models to flag manipulative AI output.","feed_headline":"A dataset to tell rational persuasion from manipulation is coming","feed_subtitle":"A dissertation will annotate ~2,000 discussion comments and test how well LLMs spot manipulative persuasion.","key_machinery":"The central object is the taxonomy that separates persuasive techniques into rational persuasion and manipulation, grounded in cognitive theory: rational persuasion is aligned with the deliberate, systematic mode of information processing (System 2), while manipulation targets the fast, automatic, heuristic mode (System 1). This taxonomy is the mechanism that turns a fuzzy normative distinction into annotatable labels, and it does the work of making the planned dataset possible. A second load-bearing piece is the chosen source corpus, a public dataset of persuasive online comments, which supplies the raw texts that the research team will filter and annotate. The project's evaluation step then uses the annotated dataset as a benchmark to test whether LLMs reproduce the human distinction under zero-shot and few-shot prompting.","core_discovery":"The paper claims that rational persuasion, which engages reason and evidence through a person's deliberate System 2 processing, and manipulation, which covertly exploits cognitive shortcuts in System 1/heuristic processing, have not yet been separated empirically in NLP. It asserts that no existing dataset carries human-annotated labels for this classification, and that current persuasion datasets treat persuasion at too broad a level. The proposed contribution is therefore to create the missing resource: a taxonomy of persuasion techniques, a human-annotated dataset of roughly 1,000 comments in each category drawn from public online discussion data, and baseline evaluations of current LLMs using zero-shot and few-shot prompting. The author frames this as the first empirical step toward automatic detection of unsafe persuasion and as a benchmark that future AI-safety research can build on.","pith_inferences":["If the taxonomy reliably separates the two, the same annotation scheme could be applied to other persuasive texts such as advertisements, political campaign messages, or chatbot sales dialogues, not just online discussion comments.","The project's success will hinge on whether a small team can annotate a concept that philosophy and law still debate; a likely testable extension is measuring inter-annotator agreement and publishing disagreement cases as a secondary dataset.","The paper's framing implies a monotone link between System 1 processing and harm, but some System 1 cues can be benign; a more detailed account of which heuristics are harmful would sharpen the taxonomy.","LLM evaluation results on this dataset could later be used to design probes that test whether models can be induced to produce manipulative output, connecting classification to generation-side safety."],"forward_implications":["If the dataset is built as planned, it becomes the first standard benchmark for classifying rational persuasion versus manipulation, letting future work train detectors rather than argue definitions anew.","The taxonomy would give AI-safety teams a concrete list of techniques that count as manipulation, making it easier to test models against the EU AI Act's prohibition on manipulative AI practices.","Baseline evaluations with zero- and few-shot prompting would measure how far current LLMs are from reliably distinguishing the two, quantifying the safety gap.","The follow-up proposal to improve LLM classification with cognitive-theory-inspired prompting would connect human dual-process models to machine classification performance.","Future researchers could extend the taxonomy with additional subtechniques and use the dataset to benchmark their own detectors."],"supporting_citations":[{"why":"Defines the EU AI Act's prohibition on AI that uses manipulative or deceptive techniques, the regulatory motivation for the project.","marker":"[1]"},{"why":"Supplies the mechanism-based taxonomy and the core definitions of rational persuasion and manipulation that the project extends.","marker":"[7]"},{"why":"Supplies the definition of manipulation as intentionally and covertly exploiting decision-making vulnerabilities.","marker":"[18]"},{"why":"Provides the public source dataset of persuasive online comments that will be filtered and annotated for the new dataset.","marker":"[19]"}],"fun_headline_variants":["New dataset separates rational persuasion from manipulation","LLMs learn to spot manipulative persuasion in new dataset","First dataset to classify persuasion vs manipulation in AI","Annotated dataset to curb manipulative AI persuasion"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The whole argument rests on the premise that a small research team can define the boundary between rational persuasion and manipulation clearly enough that human annotators will mostly agree on it.","fun_headline_variants_meta":{"raw":{"variants":["New dataset separates rational persuasion from manipulation","LLMs learn to spot manipulative persuasion in new dataset","First dataset to classify persuasion vs manipulation in AI","Annotated dataset to curb manipulative AI persuasion"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00015,"raw_usage":{"total_tokens":1123,"prompt_tokens":798,"completion_tokens":325,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":414,"completion_tokens_details":{"reasoning_tokens":265}},"tokens_in":414,"tokens_out":325,"duration_ms":3438,"temperature":1.0,"reasoning_tokens":265,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-15T20:36:17.694261+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"The central gap claim would be falsified by locating any published dataset that already carries human labels for rational persuasion versus manipulation; the annotation plan would be falsified by a pilot study in which independent annotators only reach low agreement (for example, Cohen's kappa below 0.4) on the proposed taxonomy.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Defines the EU AI Act's prohibition on AI that uses manipulative or deceptive techniques, the regulatory motivation for the project."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the definition of manipulation as intentionally and covertly exploiting decision-making vulnerabilities."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the public source dataset of persuasive online comments that will be filtered and annotated for the new dataset."}],"review_version":1}