metadata
language:
- ms
size_categories:
- 1K<n<10K
pretty_name: Cyberbully Dataset for Bahasa Malaysia
Dataset Card for MYBully
MYBully is a manually and HITL-annotated dataset of social media posts in Bahasa Malaysia (with some code-mixed English), designed for multiple text classification tasks including cyberbullying detection, hate speech detection, sentiment analysis, and emotion recognition.
Uses
- Training and evaluation of classification models for cyberbullying detection.
- Benchmarking multitask NLP models for low-resource languages (Malay).
- Cross-task learning (transfer from sentiment/emotion to bullying-related tasks).
Dataset Structure
- Instances: 4,680 tweets
- 2,687 manually annotated
- 1,993 annotated via Human-in-the-Loop (HITL) strategy
- Tasks:
- Cyberbullying (binary/multi-label)
- Hate Speech (binary)
- Sentiment (positive/neutral/negative)
- Emotion (e.g., anger, joy, sadness, fear, neutral)
- Each instance includes:
- Tweet
- Sentiment: Positive, Negative or Neutral
- Emotion: Anger, Disgust, Neutral, Sadness, Fear, Surprise, Happiness
- Bully: Yes, No
- Hate: Yes, No
- anno_type (To indicate, if record is Manually Annotated or HITL-Based Annotation): manual, hitl More details will be shared soon