ChangeMyView Reddit Dataset

• ChangeMyView Reddit Dataset is a collection of Reddit posts and comments featuring explicit 'delta' labels to indicate successful persuasion in discussions.
• It supports studies in personalized persuasiveness prediction, user profiling, and computational social science with rich historical user data.
• The dataset enables context-aware retrieval and profiling pipelines that drive measurable improvements in view-change prediction metrics.

The ChangeMyView Reddit Dataset is a domain-specific resource widely employed for research on personalized persuasiveness prediction, user modeling, argumentation, and computational social science. Its detailed conversational records, support for explicit "view change" (delta) labels, and rich user history enable in-depth analyses of persuasion mechanisms and user profiling in naturalistic settings.

1. Dataset Definition and Structure

The ChangeMyView (CMV) dataset is constructed from Reddit’s r/ChangeMyView forum, where users post opinions ("original posts" or OPs), invite others to change their view, and respond to comments. When a user's view is changed by a reply, they explicitly award a "delta," creating a labeled outcome for successful persuasion. Data include:

• Original posts (xᵢ) with user-generated content and metadata.
• Candidate comments/replies (cᵢ) with associated user and timestamp.
• Ground truth for persuasion success: yᵢ∈{0,1}, where yᵢ=1 indicates the OP author awarded a delta to cᵢ.
• For user-centric research, the dataset additionally includes pre-existing user histories R_u = {r^{u,1},...,r^{u,|R_u|}}, aggregating a user’s prior posts and comments both within and outside CMV, filtered to ensure sufficient length (e.g., min |R_u|=15) (Park et al., 9 Jan 2026).

Each data instance links the persuadee’s original post, the candidate persuader’s comment, explicit delta labeling, and the persuadee’s historical writing, supporting both standard argumentation studies and context-aware personalization research.

2. Research Motivations and Applications

ChangeMyView’s explicit focus on persuasion and the existence of outcome labels make it uniquely valuable for:

• Persuasion, Influence, and Argument Quality Studies: Quantitative evaluation of what makes an argument persuasive, and development of predictors for "change of view" (Park et al., 9 Jan 2026).
• Personalized Persuasiveness Prediction: Modeling how user-specific traits, values, and reasoning styles interact with persuasive communication, allowing the development of context-aware predictors that model the individual persuadee rather than treating all users as interchangeable (Park et al., 9 Jan 2026).
• User Profiling and Retrieval: Profiling users along latent dimensions (e.g., value orientations, cognitive style), retrieving relevant personal history, and integrating background data with current context for user-adaptive systems.
• Natural Language Understanding: Advances in representation learning, prompt engineering for LLMs, and context-adaptive model architectures.

3. Data Processing Pipeline and Experimental Setup

The canonical pipeline, as exemplified in recent context-aware user profiling frameworks, comprises:

1. User History Aggregation: All available posts/comments for user u prior to OP xᵢ are retrieved, filtered (e.g., truncation to the 100 most recent), then indexed offline using dense or hybrid retrieval (BM25, BGE-M3) (Park et al., 9 Jan 2026).
2. Query Generation: For each OP xᵢ, a trainable Query Generator φ^{query} produces a focused retrieval query qᵢ. This is optimized for persuasion relevance via direct preference optimization on retrieval utility (NDCG@5, as measured by downstream F1 in prediction tasks), with query candidates generated by sampling and reranked against the pool R_u (Park et al., 9 Jan 2026).
3. Contextualized Profiling: A Profiler φ^{prof}, typically a fine-tuned LLM, summarizes the k most persuasion-relevant records (e.g., k=5) and the current OP into a compact, context-sensitive, textual user profile Pᵢ. The profiler is itself optimized for end-task (F1) supervision using weak preference signals inferred from downstream prediction performance (Park et al., 9 Jan 2026).
4. Persuasiveness Prediction: The triplet (xᵢ, cᵢ, Pᵢ) is fed as input to a frozen persuasiveness predictor (e.g., Llama-3.3-70B, GPT-4o-mini), which outputs the probability of view change. Predictor models remain frozen; only the retrieval and profiling modules are trained for personalization.
5. Evaluation: Macro-averaged F1 and AUC metrics on held-out splits (8:1:1 train/val/test), with raw deltas as ground truth. The pipeline supports plug-and-play personalization across a variety of predictors (Park et al., 9 Jan 2026).

4. Methodological Innovations

The distinctive methodological contributions enabled by the CMV dataset include:

• End-Task Supervision for Personalization: Both retrieval (query generation) and profiling components are trainable and optimized via direct preference signals derived from increases in view-change prediction performance (F1 improvement), enabling personalization to be adaptive to both context and predictor architecture (Park et al., 9 Jan 2026).
• Utility-Weighted Retrieval: Persuasion utility of each record is empirically estimated by randomly subsampling, profiling, and measuring its incremental value for F1 prediction—rather than assuming mere topical or temporal relevance suffices.
• Task-Oriented Contextual Profiling: Profiles are bullet-point summaries optimized to highlight the most persuasion-relevant user attributes (e.g., ideological stance, cognitive style), and their content distribution is shown to be context- and model-dependent. Profiler optimization via direct preference and weak supervision (via DPO) is central.
• Frozen Predictor Modularity: The system, by design, facilitates research on the profile–predictor interface, supporting experiments with multiple frozen LLMs and the assessment of profile robustness and generalization across architectures.

The key distinction from previous baselines is the explicit, end-to-end tuning for context- and task-dependent personalization, rigorously measured by downstream delta-prediction improvement.

5. Empirical Results and Benchmarks

State-of-the-art personalized frameworks using CMV data show:

• Retrieval and Profiling Gains: Personalized profiling via tuned query-generator and profiler components yields significant F1 gains over non-personalized and static-demographic baselines. On Llama-70B, absolute F1 improvements of up to +13.77 points over non-personalized setups are achieved. The retrieval–profiler synergy is essential; DPO-trained profilers outperform demographic-based and generic summarization profilers in almost all topic-claim bins (Park et al., 9 Jan 2026).
• Ablation Analyses: Table entries reveal that utility-weighted, context-dependent profiling outperforms demographic and base methods across retrieval strategies. The top-5 overlap in useful user-history records between different predictors is low (0.24–0.28), with near-zero Spearman correlation, indicating that personalization must be both context- and model-specific (Park et al., 9 Jan 2026).
• Analytic Findings: Profile dimension shifts correlate with task performance in a topic- and claim-type dependent manner (e.g., cognitive profile features have divergent effects on prediction depending on whether the post is political or sociomoral). Merely retrieving thematically similar history records does not guarantee improved prediction—effective profiles depend strongly on persuasion context and end-task optimization.

6. Significance, Limitations, and Outlook

The CMV dataset provides an unparalleled testbed for research at the intersection of argumentation, dynamic user modeling, and context-aware personalization. Its structure enables precise, quantitative evaluation of the pipeline from retrieval through profiling to downstream effect. A key finding is that personalization success hinges not on superficial user similarity but on optimizing for predictor-specific, context-conditioned user representations (Park et al., 9 Jan 2026).

Limitations include:

• History Length Constraints: Users with insufficient historical data are excluded, potentially biasing the sample.
• Personal Information Opaqueness: Explicit demographics or static user attributes are largely unavailable, shifting emphasis to inferring (and validating) latent traits from context.
• Computational Scalability: The framework involves nontrivial pre-indexing, profile generation, and scoring over substantial user histories.

A plausible implication is the need for scalable, privacy-aware implementations as researcher interest in real-world personalized decision-support and safety assessment for LLMs expands. Future directions include extending the methodology to open-domain and multi-turn dialogue settings, integrating multimodal cues, and advancing automated, privacy-preserving, end-to-end personalized modeling architectures.

• “A Framework for Personalized Persuasiveness Prediction via Context-Aware User Profiling” (Park et al., 9 Jan 2026).