Datasets:

MisileLab
/

youtube-bot-comments

license: cc-by-sa-4.0
task_categories:
  - text-classification
language:
  - ko
pretty_name: Youtube Bot Comment
size_categories:
  - 100K<n<1M
dataset_info:
  features:
    - name: comment_id
      dtype: string
    - name: content
      dtype: string
    - name: author_name
      dtype: string
    - name: author_image_url
      dtype: string
    - name: video_id
      dtype: string
    - name: parent_id
      dtype: string
    - name: is_bot_comment
      dtype: bool
  splits:
    - name: train
      num_bytes: 39287858
      num_examples: 130080
    - name: test
      num_bytes: 11008299
      num_examples: 37167
    - name: validation
      num_bytes: 5589340
      num_examples: 18583
  download_size: 29190712
  dataset_size: 55885497
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: test
        path: data/test-*
      - split: validation
        path: data/validation-*

Note for users, README.md generated by Claude 4 Sonnet.

Dataset Card for YouTube Korean Bot Comment Dataset

This dataset contains YouTube comments from the top 50 Korean videos, classified to identify bot-generated content for research in automated content detection and Korean natural language processing.

Dataset Details

Dataset Description

This dataset consists of 185,830 YouTube comments collected from the top 50 Korean videos and classified for bot detection research. Comments were extracted using the YouTube Data API v3 and labeled using Google's Gemini 2.5 Flash-Lite model to distinguish between human-generated and bot-generated content. The dataset serves as a resource for training and evaluating bot detection systems specifically for Korean social media content.

Curated by: MisileLab
Language(s) (NLP): Korean (ko)
License: CC BY-SA 4.0

Dataset Sources

Repository: YouTube Data API v3
Classification Model: Google Gemini 2.5 Flash-Lite

Uses

Direct Use

This dataset is suitable for:

Training bot detection models for Korean social media content
Research on automated content generation patterns in Korean language
Benchmarking natural language processing models for Korean text classification
Social media authenticity analysis and content moderation research
Comparative studies of bot behavior across different platforms

Out-of-Scope Use

This dataset should not be used for:

Generating or creating bot comments
Harassment or targeting of specific YouTube users
Commercial content moderation without proper validation
Training models to evade bot detection systems
Analysis outside the Korean language context without domain adaptation

Dataset Structure

The dataset contains the following fields:

comment_id: Unique identifier for each YouTube comment
content: The actual text content of the comment in Korean
author_name: Username of the comment author
author_image_url: Profile image URL of the comment author
video_id: Identifier of the YouTube video where the comment was posted
parent_id: Identifier of parent comment (for replies, null for top-level comments)
is_bot_comment: Boolean label indicating whether the comment is classified as bot-generated (True) or human-generated (False)

Split Information:

Training set: 130,080 examples (70%)
Test set: 37,167 examples (20%)
Validation set: 18,583 examples (10%)

Dataset Creation

Curation Rationale

This dataset was created to address the lack of Korean-language resources for bot detection research. With the increasing prevalence of automated content on social media platforms, there is a critical need for language-specific datasets to train and evaluate detection systems for non-English content, particularly Korean.

Source Data

Data Collection and Processing

Video Selection: The top 50 Korean videos were identified using YouTube's trending metrics and popularity indicators
Comment Extraction: Comments were systematically collected using the YouTube Data API v3, including both top-level comments and replies
Preprocessing: Comments were cleaned and normalized while preserving Korean linguistic characteristics
Classification: Each comment was processed through Google's Gemini 2.5 Flash-Lite model using carefully designed prompts to classify bot vs. human generation
Quality Control: A subset of classifications were manually reviewed to ensure label quality

Who are the source data producers?

The source data consists of comments from YouTube users who posted on popular Korean videos. The original content creators include a diverse range of Korean speakers, from casual viewers to active community members participating in discussions around trending Korean content.

Annotations

Annotation process

Comments were automatically classified using Google's Gemini 2.5 Flash-Lite model. The classification process involved:

Analyzing linguistic patterns, repetition, and semantic coherence
Identifying typical bot behavior markers such as generic responses, promotional content, and unnatural language patterns
Considering context relevance and engagement patterns
Binary classification into bot (True) or human (False) categories

Who are the annotators?

Primary annotation was performed by Google's Gemini 2.5 Flash-Lite model, with quality validation through manual review of sample classifications.

Personal and Sensitive Information

The dataset contains publicly available YouTube comments. While usernames and profile image URLs are included, all content was already public on the YouTube platform. No private or personal information beyond what users voluntarily shared in public comments is included. Users' real names, contact information, or other private details are not part of this dataset.

Bias, Risks, and Limitations

Limitations:

Classification accuracy is dependent on Gemini 2.5 Flash-Lite's performance, which may have inherent biases
Limited to Korean language content and may not generalize to other languages
Represents a snapshot of YouTube content from a specific time period
Bot detection patterns may evolve, making the dataset less effective over time
Source video selection may introduce topical or demographic biases

Potential Biases:

May reflect biases present in the source videos' audiences
Classification model may have systematic errors for certain types of content
Temporal bias as bot techniques and patterns change over time

Risks:

Potential misclassification of legitimate human comments as bot-generated
May be used to develop more sophisticated bot systems
Privacy concerns related to public comment analysis

Recommendations

Users should:

Validate model performance on their specific use case before deployment
Be aware that bot detection is an evolving challenge with no perfect solutions
Consider the temporal aspects of the data when applying to current content
Implement additional validation steps for high-stakes applications
Respect user privacy and platform terms of service when using insights from this research

Citation

BibTeX:

@dataset{youtube_korean_bot_dataset,
  title={YouTube Korean Bot Comment Dataset},
  author={[MisileLab]},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/MisileLab/youtube-bot-comments]}
}

APA:

[MisileLab]. (2025). YouTube Korean Bot Comment Dataset. Hugging Face. https://huggingface.co/datasets/[MisileLab]/[youtube-bot-comments]

Glossary

Bot Comment: Automated or artificially generated comment, typically created by software rather than human users
YouTube Data API v3: Google's official API for accessing YouTube data programmatically
Gemini 2.5 Flash-Lite: Google's language model used for classification tasks
Top-level Comment: Comments posted directly on a video (not replies to other comments)
Reply Comment: Comments posted in response to other comments

Dataset Card Authors

MisileLab