ChangeMyView Corpus Overview

• ChangeMyView Corpus is a family of datasets from Reddit’s r/ChangeMyView, where original posts and counter-arguments are linked via delta awards indicating belief change.
• The corpus supports diverse analytical regimes including syntactic accommodation, narrative analysis, and moderation effects, each leveraging specific temporal slices and annotation methods.
• Variability in operational definitions and selection criteria across studies necessitates careful methodological design when comparing persuasive outcomes.

Searching arXiv for core ChangeMyView corpus papers and related work to ground the encyclopedia entry. to=arxiv_search.search 天天中彩票人工 ాల్సినయం 不中反json {"query":"ChangeMyView corpus Tan 2016 Winning Arguments Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions arXiv", "max_results": 5} to=arxiv_search.search ＿久久ատակյան 大发快三和值ి 天天中彩票实名json {"query":"r/ChangeMyView corpus persuasion Tan et al arXiv", "max_results": 10} Retrieving recent and foundational arXiv records mentioning r/ChangeMyView. to=arxiv_search.search 彩票总代 趣赢იციodata 北京赛车女郎json {"query":"r/ChangeMyView arXiv persuasion", "max_results": 10} ChangeMyView Corpus denotes a family of datasets derived from Reddit’s r/ChangeMyView (CMV), a subreddit in which an original poster (OP) states a view, other users challenge it, and the OP can award a delta ($\Delta$) to a reply judged to have changed that view. In NLP and computational social science, CMV functions as a naturalistic persuasion corpus because it combines explicit statements of belief, threaded counter-argument, repeated user participation, and a platform-specific outcome signal. It is not, however, a single frozen benchmark: different studies use different temporal slices, filtering rules, supervision units, and derived annotations, ranging from rooted path-units and one-challenger threads to direct-reply $(\text{post},\text{comment})$ pairs and manually annotated subsets for concessions, reframing, or narrativity (Na et al., 2022, Peguero et al., 2024, Nabhani et al., 27 Feb 2026).

1. Platform structure and the meaning of a delta

CMV is structurally distinctive among online argumentation corpora. The subreddit is organized around an OP who states a belief and invites challenge; respondents then supply counter-arguments in comments and replies. Several studies treat this configuration as especially useful because it combines an explicit belief statement, adversarial-but-normed interaction, and an endogenous persuasion marker. In one formulation, OPs must explain their view, be open to changing it, and engage with respondents; direct replies must challenge the OP’s view or ask clarifying questions; and if the OP’s view changes, the OP is expected to award a $\Delta$ (Musi et al., 2018).

A delta is the central supervision signal in most CMV work, but its interpretation is narrower than a generic “quality” label. It marks that the OP judged a reply to have changed their view. This makes CMV different from datasets based on third-party persuasive judgments or audience voting. At the same time, the literature is careful not to equate the delta with every relevant social outcome. Upvotes and downvotes also exist, and some work explicitly shows that “liked by the audience” and “changes the OP’s mind” are related but not identical outcomes (Na et al., 2022).

This architecture gives CMV a specific analytical role. It is a corpus of interpersonal persuasion under asymmetrical roles: one participant publicly states a view, other participants attempt to shift it, and success is recorded by the target of persuasion rather than by external adjudicators. A common simplification is to treat this as a general-purpose argument benchmark. The published work instead uses it as a highly specialized environment for studying persuasion, rhetorical strategy, moderation, reframing, and narrative effects under platform-specific norms (Musi et al., 2018, Srinivasan et al., 2019).

2. Corpus variants, time windows, and reported scales

“The ChangeMyView corpus” does not refer to one invariant release. Reported sizes differ because studies use different historical windows, different exclusions, and different operational definitions of the unit of analysis. Some use early historical slices; others use broad multi-year scrapes; others create filtered task-specific subsets.

Study	Reported CMV slice	Primary use
(Chen et al., 2022)	18,363 posts and 1,114,533 comments in English; persuasion subset of 15,986 posts	Syntactic similarity and persuasion
(Na et al., 2022)	100,170 posts and 5,833,572 replies and counter-replies	Argument-pattern discovery
(Peguero et al., 2024)	Raw scrape of approximately 255,287 posts and 11,461,626 comments; final 32,306 $(\text{post},\text{comment})$ pairs	Reframing corpus construction
(Srinivasan et al., 2019)	73,047 post trees, 4,176,818 comments, 176,409 users; plus 22,788 moderator-removed comments	Moderation and causal analysis
(Nabhani et al., 27 Feb 2026)	20,436 posts, 1,017,724 comments, 11,643 awarded deltas; 963,253 comments after exclusions	Narrativity and persuasion

These differences are methodological rather than merely archival. One study uses all ChangeMyView threads from January 2013 to August 2015 and reports 20,436 posts, 13,704 unique original posters, 1,017,724 comments, and 11,643 awarded deltas after removing system- and moderator-generated threads (Nabhani et al., 27 Feb 2026). Another uses a public CMV dataset described as containing 18,363 posts and 1,114,533 comments in English, then filters to 15,986 posts for one-challenger persuasion analysis (Chen et al., 2022). A later raw scrape spans early 2012 through the end of 2022 and begins from approximately 255,287 posts and 11,461,626 comments, but usable reframing data is restricted to 2015–2022 because flair labels for delta awards only appear from 2015 onward (Peguero et al., 2024).

This variability has an important consequence. CMV is better understood as a corpus family with stable platform semantics and unstable operational slices. Comparisons across papers therefore require attention to date range, exclusion criteria, and task formulation, not just dataset name.

3. Units of analysis and supervision regimes

The basic CMV objects are posts, comments, replies, users, and deltas, but different studies elevate different objects to the status of supervised instance. This has produced several distinct CMV task regimes.

One influential regime treats the persuasive event at the level of a rooted interaction path. In the concession study, only replies by the root challenger are considered, and all replies by that challenger along a path are grouped into a rooted path-unit. Winning units are those that receive a $\Delta$; losing units are no-$\Delta$ path-units in the same discussion tree chosen to be most similar in topic using Jaccard similarity (Musi et al., 2018).

A second regime collapses the thread to a single OP–challenger pair. The FastKASSIM study keeps discussion trees with at least 10 challenger replies and at least one OP reply, removes posts with more than 10,000 comments, and further restricts attention to threads involving the OP and a single challenger. Its central measurement is the syntactic similarity between the OP’s root post and the challenger’s first reply, with success defined by whether that challenger eventually receives a delta in that thread (Chen et al., 2022).

A third regime turns CMV into a direct-reply parallel dataset. The reframing study defines each instance as a $(\text{post},\text{comment})$ pair in which the “post” is the OP’s original statement and the “comment” is a direct reply that received a delta from that OP. After removing moderation posts, deleted/removed/empty content, requiring that the OP themselves awarded the delta, requiring a direct reply, and imposing minimum lengths of 500 characters for posts and 100 characters for comments, the final corpus contains 32,306 pairs (Peguero et al., 2024).

A fourth regime treats comments themselves as labeled argumentative artifacts. The argument-diversity study classifies comments by dominant argument pattern and measures pattern-specific “$\Delta$ bonus” relative to the overall baseline $P(\Delta)=1.35\%$ (Na et al., 2022). ARGUS uses manual annotation of whole comments for Story versus Non-Story and for scalar narrative features, then applies trained models to 963,253 comments and analyzes delta receipt with mixed-effects logistic regression (Nabhani et al., 27 Feb 2026).

A fifth regime is longitudinal rather than discursive. The moderation study’s substantive unit is the “affected individual,” meaning the author of a removed comment, when that removal is the first or second one the user has experienced. Here the core labels are not persuasive success but future noncompliance, toxicity, achievement, and engagement, aligned to moderator deletion events with timestamps (Srinivasan et al., 2019).

These regimes share the delta mechanism but not the same target variable. In CMV research, “success” can mean delta receipt by a specific challenger, membership in a delta-winning rooted path, persuasive direct reply selection, higher modeled odds of receiving a delta, or changes in subsequent rule compliance after moderation. The corpus is therefore unified more by platform semantics than by a single canonical label.

4. Derived annotations and methodological extensions

A large fraction of CMV research does not merely reuse raw posts and comments; it builds new annotations, representations, or metrics on top of them.

The study of argument diversity uses CMV to induce what it calls argument fragments: words and bigrams that signal argumentative style or pragmatic strategy rather than semantic topic. Starting from LIWC “Tentative” and “Certain” seeds, it expands to bigrams, builds a PMI-style linkage network, applies Louvain community detection, and ultimately constructs a lexicon of 1,506 unigrams and bigrams grouped into six argument-pattern clusters. A separate MALLET topic model is then used to control for semantic context (Na et al., 2022).

The concession study defines a narrower pragmatic target: argumentative concessions introduced by the highly polysemous markers but, though, however, while. It constructs an expert-labeled training set of 980 instances after duplicate removal, two crowdsourced sets of 1,220 instances each for development and test, and a self-training SVM with linguistically motivated features such as bag-of-words, personal pronouns and adjectives, modal verbs, hedges, Jaccard similarity to the OP, sentiment lexicons, and bootstrapped lexical patterns. The best model reaches F1 $57.4\%$ on development and $(\text{post},\text{comment})$0 on test (Musi et al., 2018).

The syntactic-similarity line of work uses CMV as both benchmark and application domain. FastKASSIM is a document-level syntactic similarity metric based on constituency parse trees and tree kernels. It computes sentence-pair tree similarities with a normalized Label-based Tree Kernel, solves a Hungarian maximum-cost matching problem over the sentence-pair matrix, and averages the selected similarities. On CMV documents it runs between $(\text{post},\text{comment})$1 and $(\text{post},\text{comment})$2 faster than CASSIM, and the corpus provides the substantive setting for testing whether syntactic similarity is associated with persuasion (Chen et al., 2022).

The reframing study treats delta-awarded direct replies as naturally occurring perspective shifts. Its raw CMV pairs are passage-level rather than sentence-level, and the paper later uses GPT-4 to trim some of them into shorter “original text/reframing” pairs, producing a 3,624-example refined subset used for additional fine-tuning experiments with BART and T5 (Peguero et al., 2024).

ARGUS adds a narrativity layer to CMV. It first enriches for narrative content by applying StorySeeker to the corpus and selecting the 100 discussions with the highest proportion of comments predicted as Story. From these, it obtains 620 posts and comments for manual annotation. Seven annotators label Story versus Non-Story; three annotate the text-oriented features Agency, Event Sequencing, and World Making; four annotate the reader-oriented features Suspense, Curiosity, and Surprise. Feature labels use a five-point Likert scale, soft labels are preserved, and calibrated encoder models are then trained to annotate the full CMV collection (Nabhani et al., 27 Feb 2026).

CMV has also supported quasi-causal methodology. In the moderation study, delayed moderator removal creates a pre-removal window in which a user has posted a problematic comment but has not yet received moderation feedback. This supports a delayed-feedback design that compares the last comment before removal with the first comment after removal, using matched controls with slightly longer delays to approximate a difference-in-differences contrast (Srinivasan et al., 2019).

Taken together, these extensions show that CMV is not only a persuasion dataset. It is also an annotation substrate for discourse relations, argument forms, syntactic accommodation, reframing, narrativity, and moderation-response dynamics.

5. Empirical findings associated with CMV

Several major substantive findings recur in the CMV literature, though they arise from different derived corpora and should not be conflated.

In argument-pattern discovery, six empirically induced patterns are reported: Relevance and Presumption, Definitions and Clarity, Deduction and Certainty, Causation and Examples, Induction and Probability, and Personal and Anecdotal. Their frequencies in CMV are Relevance $(\text{post},\text{comment})$3, Definitions $(\text{post},\text{comment})$4, Deduction $(\text{post},\text{comment})$5, Causation $(\text{post},\text{comment})$6, Induction $(\text{post},\text{comment})$7, and Personal $(\text{post},\text{comment})$8. Their persuasion effects, measured as relative $(\text{post},\text{comment})$9 bonus, are sharply differentiated: Relevance $\Delta$0, Definitions $\Delta$1, Deduction $\Delta$2, Causation $\Delta$3, Induction $\Delta$4, and Personal $\Delta$5. At the user level, principal components analysis yields a first component explaining $\Delta$6 of the variance and a second explaining $\Delta$7, interpreted respectively as personal–impersonal and concrete–abstract axes, together accounting for nearly $\Delta$8 of between-user strategy variance (Na et al., 2022).

In syntactic accommodation, FastKASSIM reports that successful first arguments are more syntactically similar to the OP’s root post than unsuccessful ones: mean similarity $\Delta$9 versus $(\text{post},\text{comment})$0, with $(\text{post},\text{comment})$1 and $(\text{post},\text{comment})$2. When initial arguments are stratified by similarity, the bottom third has a persuasion rate of $(\text{post},\text{comment})$3 while the top third has $(\text{post},\text{comment})$4, with $(\text{post},\text{comment})$5 and $(\text{post},\text{comment})$6. The paper treats this as support for the hypothesis that syntactic similarity contributes to persuasion, while explicitly stopping short of a strong causal claim (Chen et al., 2022).

In concession analysis, the main result is negative. The specific argumentative concession subtype studied appears roughly equally distributed across winning and losing CMV comments. Manual $(\text{post},\text{comment})$7 tests are not significant at $(\text{post},\text{comment})$8 on the training and test sets, and the one significant result on the development set runs in the opposite direction, with argumentative concessions less frequent in winning arguments (Musi et al., 2018).

In reframing-as-generation, the key result is that raw delta-awarded direct replies are difficult targets for direct post-to-comment learning. On the GPT-4-trimmed subset of 3,624 pairs, T5 obtains ROUGE-1 $(\text{post},\text{comment})$9, ROUGE-2 $\Delta$0, ROUGE-L $\Delta$1, BLEU $\Delta$2, BERTScore $\Delta$3; BART obtains ROUGE-1 $\Delta$4, ROUGE-2 $\Delta$5, ROUGE-L $\Delta$6, BLEU $\Delta$7, BERTScore $\Delta$8. The paper interprets the low overlap scores as evidence that raw CMV pairs are too context-heavy to serve as clean reframing supervision without further trimming or annotation (Peguero et al., 2024).

In narrativity research, ARGUS reports that modeled narrativity predicts persuasive success at scale. In a mixed-effects logistic regression over 963,253 comments, Story scalar has $\Delta$9, $\Delta$0, $\Delta$1, $\Delta$2, $\Delta$3, while binary story presence has $\Delta$4, $\Delta$5, $\Delta$6, $\Delta$7, $\Delta$8. When narrative features are decomposed, Response score is much stronger than Structural score: $\Delta$9 versus $(\text{post},\text{comment})$0. At the individual-feature level, Curiosity is the strongest positive predictor of delta receipt with $(\text{post},\text{comment})$1, followed by Suspense with $(\text{post},\text{comment})$2, whereas Surprise becomes negative in scalar modeling with $(\text{post},\text{comment})$3 (Nabhani et al., 27 Feb 2026).

In moderation research, CMV yields a narrower but causal result. Comment removal causally reduces immediate future noncompliance among users who remain active, both in non-affected trees and affected trees, whereas apparent improvements in toxicity, achievement, score, and engagement seen in interrupted time-series analysis do not survive the delayed-feedback design (Srinivasan et al., 2019).

A plausible synthesis is that CMV supports several distinct explanatory families of persuasion and behavior: pragmatic strategy, syntactic accommodation, narrative form, and institutional feedback. The corpus does not imply that any single family is sufficient.

6. Interpretive limits, biases, and comparison with adjacent corpora

CMV’s analytical value is inseparable from its constraints. Multiple papers emphasize that it is a highly curated, strictly moderated, explicitly good-faith environment rather than a generic sample of online discourse. Users are self-selected, Reddit-specific, and participating under norms that reward explicit openness to persuasion (Musi et al., 2018, Na et al., 2022).

The delta signal is likewise narrower than it first appears. It is a self-reported and platform-specific marker. A delta-awarded comment may reflect changed belief, compliance with subreddit norms, generosity, or differential willingness to acknowledge persuasion. One paper explicitly notes that delta-awarded direct replies mark successful persuasion, not reframing per se; a winning comment may succeed because of evidence, factual correction, rhetorical structure, empathy, concession, clarification, or interaction dynamics rather than because it re-expresses the OP’s point in a different frame (Peguero et al., 2024). Another uses delta receipt as a proxy for persuasion while still modeling author and OP random effects and comment length, precisely because repeated participation and comment properties matter (Nabhani et al., 27 Feb 2026).

Representativeness is another recurrent concern. The syntactic-similarity study states that assuming CMV is representative of online arguments is questionable because Reddit anonymity means the sample may not reflect broader diversity in location, education, socioeconomic status, ethnicity, and other factors (Chen et al., 2022). The moderation study adds that pseudonymity prevents linkage across alternate accounts, that moderation labels are action-based rather than exhaustive ground truth, and that analyses condition on users who remain active after removal (Srinivasan et al., 2019).

Derived CMV datasets also introduce strong selection effects. One-challenger analyses remove confounds from multiple challengers but exclude a large portion of the platform’s interactional complexity (Chen et al., 2022). Direct-reply reframing datasets gain interpretability by excluding nested persuasive dynamics (Peguero et al., 2024). Narrative annotation in ARGUS is intentionally narrative-rich rather than representative of all CMV comments, and World Making is so rare in this domain that it is dropped from downstream persuasion analyses (Nabhani et al., 27 Feb 2026). Marker-based concession studies observe only comments containing four discourse connectives, which is suitable for one pragmatic question but not for general rhetoric (Musi et al., 2018).

Relative to adjacent argumentation corpora, CMV is most often contrasted with Debate.org. CMV is centered on one opinion holder posting a view and challengers trying to change it in threaded discussion; persuasion is signaled by whether the OP awards a delta. Debate.org is a two-sided competitive debate platform with multi-round pro/con exchanges, comments, and third-party voting on convincingness, conduct, reliability of sources, and spelling/grammar. Its corpus links debate text to unusually rich demographic, ideological, issue-position, activity-history, and social-network metadata for both debaters and voters, whereas CMV usually provides usernames and discussion structure but not explicit demographic or ideological profiles (Durmus et al., 2019). This makes CMV better suited to studying direct belief change in threaded interaction, and Debate.org better suited to studying audience alignment, prior belief similarity, and user-history effects.

For research practice, the central implication is that CMV is best treated neither as a single benchmark nor as a neutral sample of “online persuasion.” It is a platform-specific, norm-governed corpus family whose distinctiveness lies in the combination of explicit views, challenger replies, deltas, rich thread structure, and, in some studies, moderation history. That combination has made it a durable substrate for work on argument forms, concessive discourse, syntactic accommodation, reframing, narrativity, and moderation, but each result is conditioned by the exact slice and operationalization used (Chen et al., 2022, Na et al., 2022, Peguero et al., 2024, Srinivasan et al., 2019, Nabhani et al., 27 Feb 2026).