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