---
title: 'YTCommentVerse: Multilingual YouTube Comment Corpus'
url: https://www.emergentmind.com/topics/ytcommentverse
type: topic
---

# YTCommentVerse: Multilingual YouTube Comment Corpus

YTCommentVerse is a large-scale multilingual, multi-category corpus of YouTube comments introduced as a dataset paper in 2025. It contains **32,236,173 comments** from **178,027 videos** written by **20,568,637 commenters**, spans **15 YouTube content categories**, and covers **over 50 languages**. Each record links comment text to platform-native identifiers and metadata, including `videoID`, `commentID`, commenter fields, `originalChannelID`, upvotes, and category labels. The resource is framed as infrastructure for research on discourse, toxicity, engagement, multilingual NLP, and platform-specific language modeling on YouTube, a platform the paper describes as comparatively underrepresented in public research datasets [2509.11057].

## 1. Position within YouTube data infrastructure

YTCommentVerse is presented as a response to the mismatch between YouTube’s scale and the relative scarcity of large, publicly reusable YouTube comment corpora. The paper argues that YouTube has been harder to study than Twitter/X, Reddit, or Facebook because of data-collection and curation challenges, and it contrasts YTCommentVerse with earlier public datasets that were smaller, narrower in topic, or thinner in metadata [2509.11057].

| Dataset | Reported scale | Reported fields |
|---|---:|---|
| YouTube18K | ~18K comments | comment, sentiment |
| COVID-YT | ~24K | URL, title, channel, comment text |
| YTSpam | ~1.9K | commentID, commenterName, commentDate, comment, videoName |
| YouTube-180K | ~180K | comment |
| YTCommentVerse | 30M+ | comment, videoID, commentID, commenterName, commenterChannelID, upvotes, originalChannelID, category |

The corpus also sits within a broader lineage of YouTube-comment resources. An earlier large-scale dataset paper, YT-30M, reported the same core scale and a nearly identical comment-centric schema while releasing **YT-100K** as a **108,694-comment** sample; this suggests a direct lineage between the two resources [2412.03465]. By contrast, "YouNiverse" released a very large commenter-video incidence table and extensive channel/video metadata, but explicitly stripped comment text from the released comment table [2012.10378]. Earlier domain-specific corpora such as "YouTube AV 50K" focused on narrower topical slices, in that case autonomous-vehicle videos, rather than a cross-category platform-wide comment layer [1807.11227].

## 2. Collection frame, channel selection, and privacy model

The dataset construction begins from **SocialBlade rankings**. The authors use SocialBlade to obtain **top YouTube channels by subscriber count** within each category, then collect all videos posted by those channels and the associated comments and comment metadata using **custom web scrapers** [2509.11057]. The process is described in two stages: **(1) create a list of videos posted by a YouTube channel** and **(2) collect video metadata and comments, along with comment metadata, for each video**.

The **15 categories** used by the paper are: **Autos & Vehicles**, **Comedy**, **Education**, **Entertainment**, **Film**, **Gaming**, **Science & Technology**, **Howto & Style**, **Music**, **News & Politics**, **Nonprofit & Activism**, **People & Blogs**, **Pets & Animals**, **Sports**, and **Travel** [2509.11057]. Category labels appear to derive from the SocialBlade/YouTube channel category under which channels were selected; the paper does not describe a separate relabeling or validation stage.

A notable feature of the collection description is what it does **not** specify. The paper does **not** provide a collection date range or explicit temporal span of the videos and comments, and it explicitly characterizes the resulting resource as **non-temporal**. It also does **not** describe a deduplication procedure, spam filtering, bot filtering, deleted-comment handling, minimum language-confidence thresholds, or a detailed cleaning and normalization pipeline [2509.11057].

Privacy treatment is explicit. The paper states that **all Personally Identifiable Information (PII) has been redacted/anonymized**, that the data were collected only from **publicly accessible YouTube videos and comment sections**, and that individual consent was not sought because contacting millions of users would be impractical [2509.11057]. The sample records show opaque identifiers such as `"b65..."` and `"2f1..."`, indicating anonymized commenter fields rather than raw public names.

The language layer is analytically central but methodologically underspecified. The paper reports language distributions using **ISO 639-1 codes**, including `und` for undetermined language, but does **not** explain the language-identification algorithm, confidence thresholds, treatment of mixed-language comments, or whether language is included as a released field [2509.11057].

## 3. Corpus structure, schema, and linguistic composition

At release scale, YTCommentVerse comprises **32,236,173 comments**, **178,027 videos**, **20,568,637 commenters**, **15 categories**, and **over 50 languages** [2509.11057]. The paper defines each dataset entry as **one comment**.

| Field | Meaning |
|---|---|
| `videoID` | YouTube video ID |
| `commentID` | Comment ID |
| `commentorName` | Commenter name |
| `commentorChannelID` | Commenter’s channel ID |
| `comment` | Comment text |
| `upvotes` | Upvotes received by the comment |
| `originalChannelID` | Channel ID of the original video uploader |
| `category` | YouTube video category |

The schema text uses **`upvotes`**, while the sample JSON uses **`votes`**; the paper treats these as the same metadata dimension [2509.11057]. Just as important are the omissions. The paper does **not** list timestamp fields, video publication time, language as a released field, sentiment labels, toxicity labels, reply-thread structure, dislikes, number of replies, or country/region labels. The released resource is therefore comment-centric and metadata-rich, but not conversationally structured.

The paper’s language distribution table places **English (`en`)** first with **8.9M comments (22.0%)**, followed by **Spanish (`es`) 4.4M (10.8%)**, **Korean (`ko`) 3.8M (9.4%)**, **Portuguese (`pt`) 3.7M (9.0%)**, **Undetermined (`und`) 2.3M (5.6%)**, **Thai (`th`) 2.3M (5.6%)**, **Arabic (`ar`) 1.2M (3.1%)**, **Indonesian (`id`) 1.2M (2.9%)**, **Somali (`so`) 1.1M (2.8%)**, and **German (`de`) 1.1M (2.7%)** [2509.11057]. The language table itself lists approximately **57 entries**, counting `und`, although the paper uses the more conservative summary **“over 50 languages.”**

The resource’s structure makes it simultaneously textual and relational. Each comment is anchored to a video, uploader channel, commenter identity, category, and engagement signal. This suggests a corpus suitable not only for text analysis but also for commenter-level, channel-level, and cross-category aggregation.

## 4. Reported descriptive analyses

YTCommentVerse is primarily a dataset-description paper, but it includes several descriptive analyses intended to demonstrate the corpus’s analytical range [2509.11057].

First, the paper uses **upvotes** as a proxy for audience engagement and reports that **most comments receive little to no engagement**, often **fewer than five upvotes**. It also notes that upvotes are a **partial** engagement signal because the dataset does not include dislikes, replies, or richer interaction metadata. This makes engagement modeling possible, but incomplete.

Second, the paper analyzes **comment length** by category. It reports that **Nonprofit** has the highest average comment length at **111.7 characters**, while **Comedy** has the highest variability in comment length with **$\sigma = 1604.7$**. The associated interpretation is that nonprofit discussions tend to be more detailed, whereas comedy supports a much broader range of reaction styles.

Third, the paper presents **sentiment** results at category level. It reports that **Music** is the most positively received category with **mean sentiment = 0.142**, and that Music also has the highest sentiment variability with **$\sigma = 0.299$**. It further states that **most comments have neutral sentiment** and that **both positive and negative comments can receive high upvotes**, leading to the conclusion that **sentiment alone is not a strong predictor of popularity** [2509.11057].

Fourth, the paper includes a semantic-structure analysis based on a **uniform random sample of 100,000 comments**, embedded and projected with **UMAP**, with **DBSCAN** used to isolate the main dense cluster and remove outliers. In that space, **Music**, **Gaming**, and **Education** form relatively tight clusters, while **Comedy**, **Entertainment**, and **Tech** show more overlap. The paper interprets this as evidence that some categories support more semantically focused audience discourse than others.

Fifth, the paper runs **Perspective API** analyses using six dimensions: **toxicity**, **severe toxicity**, **obscene**, **threat**, **insult**, and **identity**. The highest toxicity levels appear in **Entertainment**, **Gaming**, and **Sports**, while **Education**, **Animals**, and **Travel** exhibit lower toxicity. **Identity-related toxicity** is reported as generally low overall, but somewhat more present in **Entertainment** and **Gaming** [2509.11057].

These descriptive modules are accompanied by a set of intended use cases. The paper explicitly discusses **cross-cultural discourse analysis**, **content moderation**, **fine-tuning large language models**, and **bias and fairness audits** as prospective research directions for the corpus [2509.11057].

## 5. Relation to established research agendas on YouTube comments

YTCommentVerse enters an already diverse literature on YouTube comments, but it does so with substantially broader scale and category coverage than most earlier resources. This broader scale is especially relevant for research that requires commenter linkage, channel linkage, and cross-video structure.

One established line of work models comments as networks rather than isolated text. "Commenter Behavior Characterization on YouTube Channels" constructed per-channel **co-commenter networks** for **20 channels**, **7,782 videos**, **294,199 commenters**, and **596,982 comments** in a misinformation-related military domain, using graph features, PCA, clustering, clique analysis, and cross-channel overlap to detect **commenter mobs** [2304.07681]. "Detecting Suspicious Commenter Mob Behaviors on YouTube Using Graph2Vec" studies the same basic problem with a graph-embedding perspective [2311.05791]. Because YTCommentVerse releases comment text together with commenter identifiers, `videoID`, `originalChannelID`, and category labels, this suggests direct compatibility with large-scale co-commenting, coordination, and audience-overlap analyses.

Another major line of work studies YouTube comments as political discourse. "Cross-Partisan Discussions on YouTube" analyzed **973 channels**, **274,241 political videos**, and **134M comments** to show substantial but asymmetric cross-partisan participation, with comment ranking making cross-partisan comments modestly less visible [2104.05365]. "Asymmetric Dynamics of Partisan Warriors in YouTube Comments" examined **1,854,320 comments** around the **2024 U.S. second presidential debate** and shifted the focus from generic incivility to **target-specific hostile cross-cutting behavior** and channel-level ecological effects [2603.21776]. YTCommentVerse does not itself annotate ideology or attack targets, but its multilingual, category-linked scale makes such downstream political analyses plausible.

A third line concerns safety and moderation. "Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube" collected approximately **four million records** around children’s videos and reported **11%** toxic comments in that setting [2103.09050]. Here the relevance of YTCommentVerse is infrastructural rather than label-complete: it provides the raw comment layer, but not native moderation labels. A similar pattern appears in instructional-video QA: "YTCommentQA" extracted **2,332 questions from 2,004 videos** from naturally occurring YouTube comments and showed that answerability depends on whether evidence is present in the video and on which modality carries that evidence [2401.17343]. These adjacent studies indicate that YTCommentVerse can function as a base corpus for moderation, discourse, retrieval, QA, and platform-analysis tasks, even though it is not itself a benchmark suite for any one of them.

## 6. Access, limitations, and scholarly significance

The release model is tiered. The paper states that a smaller version, **YT-100K**, is available on **Hugging Face**, while the full **YTCommentVerse** dataset was available by **directly emailing the authors** and is also said to be uploaded to **Zenodo**; the release format is described as an **SQLite database** [2509.11057]. The paper also invokes **FAIR** principles, tying findability to Hugging Face, accessibility to dataset identifiers, interoperability to a Hugging Face datasets-style structure, and reusability to documentation and dataset cards.

The paper is explicit about several limitations. It identifies **public visibility bias**, because only publicly visible comments are included; **non-temporal design**, because the dataset is not suitable for many temporal modeling tasks; **lack of multimodal context**, because no video, audio, image, or thumbnail information is included; and a **limited engagement signal**, because only upvotes are released, not dislikes, replies, or richer interaction metadata [2509.11057]. It also leaves several important methodological issues unspecified: the exact language-identification method, the collection time frame, filtering and preprocessing details, and formal annotation or validation procedures.

These constraints place YTCommentVerse closer to a **large-scale YouTube comment infrastructure dataset** than to a heavily annotated benchmark corpus. Its central value lies in breadth: **32.2M comments**, **178K videos**, **20.6M users**, **15 categories**, and **50+ languages**, combined with identifiers that permit comment-level, commenter-level, video-level, and channel-level aggregation [2509.11057]. Its central limitation is the absence of temporal, conversational, and multimodal fields. Within the YouTube data landscape, it is therefore best understood as a foundational corpus for large-scale empirical work on comment text and platform-linked metadata, rather than as a complete representation of the full interactional ecology of YouTube comments.

Source: https://www.emergentmind.com/topics/ytcommentverse