---
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: video_title
    dtype: string
  - name: video_author
    dtype: string
  - name: parent_id
    dtype: string
  - name: is_bot_comment
    dtype: bool
  splits:
  - name: train
    num_bytes: 4165725
    num_examples: 9381
  - name: test
    num_bytes: 1172862
    num_examples: 2680
  - name: validation
    num_bytes: 591155
    num_examples: 1341
  download_size: 2812002
  dataset_size: 5929742
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
license: mit
task_categories:
- text-classification
language:
- ko
size_categories:
- 10K<n<100K
---
# Dataset Card for youtube-bot-comment-v2

This dataset contains Korean YouTube comments labeled for bot detection, focusing on identifying automated comments that promote adult content or gambling websites.

## Dataset Details

### Dataset Description

This dataset consists of Korean YouTube comments collected from top South Korean videos, with binary classification labels indicating whether each comment is generated by a bot or a human user. The dataset specifically targets the identification of spam bots that promote adult content and gambling websites using repetitive patterns and specific keywords.

- **Curated by:** Misile
- **Language(s) (NLP):** Korean (ko)
- **License:** MIT
- **Task:** Binary text classification (bot detection)

### Dataset Sources

- **Data Source:** YouTube comments from top 200 South Korean videos
- **Collection Method:** Random sampling of comments from selected videos

## Uses

### Direct Use

This dataset is suitable for:
- Training Korean text classifiers for bot detection
- Research on spam detection in Korean social media content
- Developing content moderation systems for Korean online platforms
- Academic research on automated content generation detection

### Out-of-Scope Use

This dataset should not be used for:
- General Korean language modeling without considering the specific domain bias
- Classification tasks unrelated to bot/spam detection
- Applications requiring detection of sophisticated or evolving bot patterns beyond the specific types captured

## Dataset Structure

The dataset contains the following fields:

- **comment_id**: Unique identifier for each comment
- **content**: The actual comment text in Korean
- **author_name**: Username of the comment author
- **author_image_url**: URL of the author's profile image
- **video_id**: YouTube video identifier
- **video_title**: Title of the video where the comment was posted
- **video_author**: Creator of the video
- **parent_id**: ID of parent comment (for replies)
- **is_bot_comment**: Boolean label indicating if the comment is from a bot (True) or human (False)

## Dataset Creation

### Curation Rationale

This dataset was created to address the need for Korean-language bot detection in social media comments, particularly focusing on spam bots that promote inappropriate content on YouTube. The prevalence of such automated comments on popular Korean videos motivated the creation of this specialized dataset.

### Source Data

#### Data Collection and Processing

Comments were collected from the top 200 most popular South Korean YouTube videos through random sampling. The dataset focuses on a specific time period and represents a cross-section of popular Korean video content.

The classification process involved:
1. **Regex-based initial filtering**: Using common spam keywords and patterns such as '19금' (adult content indicator) and '19금' variations
2. **Manual verification**: Human review and labeling to ensure accuracy
3. **Pattern identification**: Focus on repetitive promotional patterns for adult and gambling websites

#### Who are the source data producers?

The source data consists of comments from YouTube users on popular Korean videos. The bot comments were generated by automated systems designed to promote adult content and gambling websites.

### Annotations

#### Annotation process

The annotation process combined automated and manual approaches:
- Initial filtering using regex patterns targeting common spam keywords
- Manual classification and verification of edge cases
- Focus on identifying repetitive promotional patterns

#### Who are the annotators?

Annotations were performed by the dataset curator (Misile) using a combination of pattern matching and manual review.

#### Personal and Sensitive Information

The dataset contains YouTube usernames and profile image URLs as they appeared publicly. No additional personal information has been added, but users should be aware that this data was originally public on YouTube.

## Bias, Risks, and Limitations

### Technical Limitations
- **Temporal bias**: Comments represent a specific time period and may not capture evolving bot patterns
- **Platform specificity**: Patterns are specific to YouTube and may not generalize to other platforms
- **Language specificity**: Optimized for Korean language patterns and may not work for other languages
- **Bot type limitation**: Focuses specifically on adult/gambling promotion bots, not general spam or other bot types

### Content and Sampling Bias
- **Video selection bias**: Limited to top 200 South Korean videos, which may not represent the full spectrum of Korean YouTube content
- **Geographic bias**: Focused on South Korean content and user patterns
- **Topic bias**: Popular videos may have different comment patterns than niche content

### Classification Limitations
- **Pattern dependency**: Relies heavily on specific keywords and patterns that may become outdated
- **False positive risk**: Legitimate comments containing flagged keywords may be misclassified
- **Evolving threats**: Bot patterns evolve over time, potentially reducing long-term effectiveness

### Recommendations

Users should be made aware of the following:
- **Regular updates needed**: Bot detection patterns require frequent updating as spam techniques evolve
- **Platform adaptation**: Models trained on this data may need adjustment for other platforms
- **Balanced evaluation**: Consider both precision and recall when evaluating bot detection performance
- **Ethical considerations**: Ensure privacy protection when deploying models trained on this data
- **Complementary approaches**: Combine with other detection methods for robust spam filtering

## Citation

If you use this dataset in your research, please cite:

**BibTeX:**
```bibtex
@dataset{misile2024youtube_bot_comment_v2,
  title={youtube-bot-comment-v2: Korean YouTube Bot Comment Detection Dataset},
  author={Misile},
  year={2025},
  license={MIT}
}
```

## Dataset Card Contact

For questions or issues regarding this dataset, please contact Misile(misile@duck.com).