nahiar/hate_speech_detection

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.