metadata

license: mit
task_categories:
  - text-classification
language:
  - id
tags:
  - hate-speech-detection
  - abusive-language
  - text-classification
  - indonesian
  - social-media
  - nlp
  - content-moderation
  - multi-label-classification
size_categories:
  - 10K<n<100K

Indonesian Hate Speech Detection Dataset

Dataset Summary

This dataset contains 13,169 Indonesian tweets annotated for hate speech detection and abusive language classification. The dataset provides comprehensive multi-label annotations covering different types of hate speech, target categories, and intensity levels, making it valuable for building robust content moderation systems for Indonesian social media.

Dataset Details

Total Samples: 13,169 Indonesian tweets
Language: Indonesian (Bahasa Indonesia)
Annotation Type: Multi-label binary classification
Labels: 12 different hate speech and abusive language categories
Format: CSV file
Text Length: 4-561 characters (average: 114 characters)

Label Categories

Primary Classifications

Label	Description	Positive Cases	Percentage
`HS`	Hate Speech - General hate speech detection	5,561	42.2%
`Abusive`	Abusive Language - Offensive or abusive content	5,043	38.3%

Target-Based Classifications

Label	Description	Positive Cases	Percentage
`HS_Individual`	Hate speech targeting specific individuals	3,575	27.1%
`HS_Group`	Hate speech targeting groups/communities	1,986	15.1%
`HS_Religion`	Religious hate speech	793	6.0%
`HS_Race`	Racial/ethnic hate speech	566	4.3%
`HS_Physical`	Physical appearance-based hate speech	323	2.5%
`HS_Gender`	Gender-based hate speech	306	2.3%
`HS_Other`	Other types of hate speech	3,740	28.4%

Intensity Classifications

Label	Description	Positive Cases	Percentage
`HS_Weak`	Weak/mild hate speech	3,383	25.7%
`HS_Moderate`	Moderate hate speech	1,705	12.9%
`HS_Strong`	Strong/severe hate speech	473	3.6%

Key Statistics

Text Characteristics:

Average tweet length: 114 characters
Shortest tweet: 4 characters
Longest tweet: 561 characters
Language: Indonesian (Bahasa Indonesia)

Label Distribution:

Balanced primary labels: ~42% hate speech, ~38% abusive
Imbalanced target categories: Physical (2.5%) to Individual (27.1%)
Severity pyramid: Weak (25.7%) > Moderate (12.9%) > Strong (3.6%)

Use Cases

This dataset is ideal for:

Multi-label Text Classification: Train models to detect multiple types of hate speech
Indonesian NLP: Develop language-specific content moderation systems
Social Media Monitoring: Build automated detection for Indonesian platforms
Severity Assessment: Create models that classify hate speech intensity
Target Analysis: Understand different targets of hate speech
Content Moderation: Deploy real-time filtering systems
Research: Study hate speech patterns in Indonesian social media

Quick Start

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.multioutput import MultiOutputClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

# Load dataset
df = pd.read_csv('data.csv')

# Prepare features and targets
X = df['Tweet']
y = df[['HS', 'Abusive', 'HS_Individual', 'HS_Group', 'HS_Religion', 
        'HS_Race', 'HS_Physical', 'HS_Gender', 'HS_Other']]

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Vectorize text
vectorizer = TfidfVectorizer(max_features=10000, ngram_range=(1, 2))
X_train_vec = vectorizer.fit_transform(X_train)
X_test_vec = vectorizer.transform(X_test)

# Train multi-label classifier
classifier = MultiOutputClassifier(LogisticRegression(random_state=42))
classifier.fit(X_train_vec, y_train)

# Evaluate
y_pred = classifier.predict(X_test_vec)
print("Multi-label Classification Report:")
for i, label in enumerate(y.columns):
    print(f"\n{label}:")
    print(classification_report(y_test.iloc[:, i], y_pred[:, i]))

Advanced Usage Examples

Intensity-Based Classification

# Focus on hate speech intensity levels
intensity_labels = ['HS_Weak', 'HS_Moderate', 'HS_Strong']
hate_speech_data = df[df['HS'] == 1]  # Only hate speech samples

# Multi-class intensity classification
y_intensity = hate_speech_data[intensity_labels]

Target-Specific Models

# Build specialized models for different targets
target_labels = ['HS_Individual', 'HS_Group', 'HS_Religion', 'HS_Race', 
                'HS_Physical', 'HS_Gender', 'HS_Other']

# Train target-specific classifiers
for target in target_labels:
    # Create binary classifier for each target type
    pass

Indonesian Text Preprocessing

import re

def preprocess_indonesian_text(text):
    # Convert to lowercase
    text = text.lower()
    
    # Remove URLs
    text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE)
    
    # Remove user mentions and RT
    text = re.sub(r'@\w+|rt\s+', '', text)
    
    # Remove extra whitespace
    text = re.sub(r'\s+', ' ', text).strip()
    
    return text

# Apply preprocessing
df['Tweet_processed'] = df['Tweet'].apply(preprocess_indonesian_text)

Model Architecture Suggestions

Traditional ML

TF-IDF + Logistic Regression: Baseline multi-label classifier
TF-IDF + SVM: Better performance on imbalanced classes
Ensemble Methods: Random Forest or Gradient Boosting

Deep Learning

BERT-based Models: Use Indonesian BERT (IndoBERT) for better performance
Multilingual Models: mBERT or XLM-R for cross-lingual transfer
Custom Architecture: BiLSTM + Attention for sequence modeling

Multi-task Learning

# Hierarchical classification approach
# 1. First classify: Normal vs Abusive vs Hate Speech
# 2. If Hate Speech: Classify target and intensity
# 3. Multi-task loss combining all objectives

Evaluation Metrics

Given the multi-label and imbalanced nature:

Primary Metrics

F1-Score: Macro and micro averages
AUC-ROC: For each label separately
Hamming Loss: Multi-label specific metric
Precision/Recall: Per-label analysis

Specialized Metrics

from sklearn.metrics import multilabel_confusion_matrix, jaccard_score

# Multi-label specific metrics
jaccard = jaccard_score(y_true, y_pred, average='macro')
hamming = hamming_loss(y_true, y_pred)

Data Quality & Considerations

Strengths

✅ Comprehensive Labeling: Multiple dimensions of hate speech
✅ Large Scale: 13K+ samples for robust training
✅ Real-world Data: Actual Indonesian tweets
✅ Intensity Levels: Enables nuanced classification
✅ Multiple Targets: Covers various hate speech types

Limitations

⚠️ Class Imbalance: Some categories <5% positive samples
⚠️ Language Specific: Limited to Indonesian context
⚠️ Temporal Bias: Tweet collection timeframe not specified
⚠️ Cultural Context: May not generalize across Indonesian regions

Ethical Considerations

Content Warning: This dataset contains hate speech and abusive language examples.

Responsible Use

Research Purpose: Intended for academic and safety research
Content Moderation: Building protective systems
Bias Awareness: Monitor for demographic biases in predictions
Privacy: Tweets should be handled according to platform policies

Not Suitable For

Training generative models that could amplify hate speech
Creating offensive content detection without human oversight
Commercial use without proper ethical review

Related Work & Benchmarks

Indonesian NLP Resources

IndoBERT: Pre-trained Indonesian BERT model
Indonesian Sentiment: Related sentiment analysis datasets
Multilingual Models: Cross-lingual hate speech detection

Benchmark Performance

Consider comparing against:

Traditional ML baselines (TF-IDF + SVM)
Pre-trained language models (mBERT, IndoBERT)
Multi-task learning approaches

Citation

@dataset{indonesian_hate_speech_2025,
  title={Indonesian Hate Speech Detection Dataset},
  year={2025},
  publisher={Dataset From Kaggle},
  url={https://huggingface.co/datasets/nahiar/indonesian-hate-speech},
  note={Multi-label hate speech and abusive language detection for Indonesian social media}
}

Acknowledgments

This dataset contributes to safer Indonesian social media environments and supports research in:

Multilingual content moderation
Southeast Asian NLP
Cross-cultural hate speech patterns
Social media safety systems

Note: Handle this sensitive content responsibly and in accordance with ethical AI principles.