Upload tiktok-bot-detection model

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README.md

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+---
+language: en
+license: mit
+tags:
+  - bot-detection
+  - tiktok
+  - random-forest
+  - sklearn
+  - social-media
+  - classification
+metrics:
+  - accuracy
+  - precision
+  - recall
+  - f1
+  - roc-auc
+library_name: scikit-learn
+---
+
+# TikTok Bot Detection Model
+
+## Model Description
+
+This Random Forest classifier is designed to detect bot accounts on TikTok based on profile features and behavioral patterns. The model analyzes various account characteristics to determine whether an account is likely automated (bot) or genuine (human).
+
+## Model Details
+
+- **Model Type**: Random Forest Classifier
+- **Framework**: scikit-learn
+- **Task**: Binary Classification (Bot vs Human)
+- **Language**: Python
+- **License**: MIT
+
+## Performance Metrics
+
+The model achieves excellent performance on the test dataset:
+
+- **ROC-AUC Score**: 0.9747
+- **Accuracy**: High accuracy in distinguishing bots from legitimate accounts
+
+The ROC curve demonstrates strong discriminative ability with an AUC close to 1.0, indicating excellent model performance.
+
+## Features Used
+
+The model uses the following 12 features for prediction:
+
+1. **IsPrivate** - Whether the account is set to private
+2. **IsVerified** - Whether the account has a verification badge
+3. **HasProfilePic** - Whether the account has a profile picture
+4. **FollowingCount** - Number of accounts being followed
+5. **FollowerCount** - Number of followers
+6. **HasInstagram** - Whether Instagram link is present in bio
+7. **HasYoutube** - Whether YouTube link is present in bio
+8. **HasBio** - Whether the account has a bio description
+9. **HasLinkInBio** - Whether there's a link in the bio
+10. **HasPosts** - Whether the account has made any posts
+11. **PostsCount** - Total number of posts
+12. **FollowToFollowerRatio** - Ratio of following to followers
+
+## Intended Use
+
+### Primary Uses
+
+- Identifying potential bot accounts on TikTok
+- Content moderation and platform integrity
+- Research on social media bot behavior
+- Automated account screening
+
+### Out-of-Scope Uses
+
+- This model is specifically trained for TikTok and should not be used for other platforms without retraining
+- Should not be the sole basis for account suspension decisions
+- Not designed for real-time detection without proper infrastructure
+
+## How to Use
+
+### Installation
+
+```bash
+pip install scikit-learn pandas numpy joblib
+```
+
+### Loading the Model
+
+```python
+import joblib
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+
+# Load the model
+model = joblib.load('TIKTOK_BOT_Detection_Model_v1.pkl')
+
+# Prepare your data
+features = ['IsPrivate', 'IsVerified', 'HasProfilePic', 'FollowingCount',
+            'FollowerCount', 'HasInstagram', 'HasYoutube',
+            'HasBio', 'HasLinkInBio', 'HasPosts', 'PostsCount',
+            'FollowToFollowerRatio']
+
+# Example account data
+account_data = {
+    'IsPrivate': 0,
+    'IsVerified': 0,
+    'HasProfilePic': 1,
+    'FollowingCount': 5000,
+    'FollowerCount': 100,
+    'HasInstagram': 0,
+    'HasYoutube': 0,
+    'HasBio': 0,
+    'HasLinkInBio': 1,
+    'HasPosts': 1,
+    'PostsCount': 50,
+    'FollowToFollowerRatio': 50.0
+}
+
+# Create DataFrame
+df = pd.DataFrame([account_data])
+
+# Scale features (use the same scaler as training)
+scaler = MinMaxScaler()
+# Note: In production, you should save and load the scaler from training
+df_scaled = scaler.fit_transform(df[features])
+
+# Make prediction
+prediction = model.predict(df_scaled)
+probability = model.predict_proba(df_scaled)
+
+print(f"Prediction: {'Bot' if prediction[0] == 1 else 'Human'}")
+print(f"Confidence - Human: {probability[0][0]:.2%}, Bot: {probability[0][1]:.2%}")
+```
+
+### Batch Prediction
+
+```python
+# For multiple accounts
+accounts_df = pd.read_csv('accounts_to_check.csv')
+accounts_scaled = scaler.transform(accounts_df[features])
+
+predictions = model.predict(accounts_scaled)
+probabilities = model.predict_proba(accounts_scaled)
+
+# Add results to DataFrame
+accounts_df['is_bot'] = predictions
+accounts_df['bot_probability'] = probabilities[:, 1]
+```
+
+## Training Data
+
+The model was trained on a dataset of TikTok accounts with labeled bot/human classifications. The dataset includes:
+
+- Balanced distribution of bot and human accounts
+- Various account types and behavioral patterns
+- Features extracted from public profile information
+
+**Note**: The training data is proprietary and not included in this repository.
+
+## Training Procedure
+
+### Preprocessing
+
+1. Feature extraction from TikTok account profiles
+2. Calculation of derived features (e.g., FollowToFollowerRatio)
+3. MinMax normalization of all features to [0, 1] range
+4. Train-test split with stratification
+
+### Hyperparameters
+
+- **Algorithm**: Random Forest Classifier
+- **Normalization**: MinMaxScaler
+- **Cross-validation**: Stratified K-Fold
+
+The model was trained using scikit-learn's RandomForestClassifier with optimized hyperparameters selected through cross-validation.
+
+## Limitations and Bias
+
+### Limitations
+
+- Model performance depends on the quality of input features
+- May not generalize to new bot patterns not seen during training
+- Requires accurate feature extraction from TikTok profiles
+- Performance may degrade over time as bot behaviors evolve
+
+### Potential Biases
+
+- May be biased toward bot patterns present in the training data
+- Could have regional or cultural biases depending on training data composition
+- May misclassify legitimate accounts with unusual behavior patterns
+
+### Recommendations
+
+- Regularly retrain the model with new data
+- Use as part of a multi-layered detection system
+- Implement human review for high-stakes decisions
+- Monitor for false positives and adjust thresholds accordingly
+
+## Ethical Considerations
+
+- This model should be used responsibly and not for harassment
+- Consider privacy implications when analyzing user accounts
+- Ensure compliance with TikTok's terms of service and relevant laws
+- Implement appropriate safeguards against misuse
+
+## Model Card Authors
+
+This model card was created as part of the Bot Detection project for social media platforms.
+
+## Citation
+
+If you use this model in your research, please cite:
+
+```bibtex
+@misc{tiktok_bot_detection_2024,
+  title={TikTok Bot Detection Model},
+  author={Your Name/Organization},
+  year={2024},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/your-username/tiktok-bot-detection}}
+}
+```
+
+## Contact
+
+For questions or feedback about this model, please open an issue in the repository or contact the maintainers.
+
+## Updates and Maintenance
+
+- **Version**: 1.0
+- **Last Updated**: November 2024
+- **Status**: Active
+
+Future updates may include:
+
+- Improved feature engineering
+- Additional training data
+- Hyperparameter optimization
+- Support for new TikTok features

inference_example.py

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+"""
+Example inference script for TikTok Bot Detection Model
+"""
+
+import joblib
+import pandas as pd
+from sklearn.preprocessing import MinMaxScaler
+
+
+def load_model(model_path="TIKTOK_BOT_Detection_Model_v1.pkl"):
+    """Load the trained bot detection model"""
+    return joblib.load(model_path)
+
+
+def prepare_features(account_data):
+    """
+    Prepare account features for prediction
+
+    Args:
+        account_data (dict): Dictionary containing account features
+
+    Returns:
+        numpy.ndarray: Scaled features ready for prediction
+    """
+    features = [
+        "IsPrivate",
+        "IsVerified",
+        "HasProfilePic",
+        "FollowingCount",
+        "FollowerCount",
+        "HasInstagram",
+        "HasYoutube",
+        "HasBio",
+        "HasLinkInBio",
+        "HasPosts",
+        "PostsCount",
+        "FollowToFollowerRatio",
+    ]
+
+    df = pd.DataFrame([account_data])
+
+    # Scale features
+    scaler = MinMaxScaler()
+    df_scaled = scaler.fit_transform(df[features])
+
+    return df_scaled
+
+
+def predict_single_account(model, account_data):
+    """
+    Predict if a single account is a bot
+
+    Args:
+        model: Trained sklearn model
+        account_data (dict): Account features
+
+    Returns:
+        dict: Prediction results with probabilities
+    """
+    features_scaled = prepare_features(account_data)
+
+    prediction = model.predict(features_scaled)[0]
+    probability = model.predict_proba(features_scaled)[0]
+
+    return {
+        "is_bot": bool(prediction),
+        "bot_probability": float(probability[1]),
+        "human_probability": float(probability[0]),
+        "confidence": float(max(probability)),
+    }
+
+
+def predict_batch(model, accounts_df):
+    """
+    Predict for multiple accounts at once
+
+    Args:
+        model: Trained sklearn model
+        accounts_df (pd.DataFrame): DataFrame with account features
+
+    Returns:
+        pd.DataFrame: Original data with predictions added
+    """
+    features = [
+        "IsPrivate",
+        "IsVerified",
+        "HasProfilePic",
+        "FollowingCount",
+        "FollowerCount",
+        "HasInstagram",
+        "HasYoutube",
+        "HasBio",
+        "HasLinkInBio",
+        "HasPosts",
+        "PostsCount",
+        "FollowToFollowerRatio",
+    ]
+
+    scaler = MinMaxScaler()
+    features_scaled = scaler.fit_transform(accounts_df[features])
+
+    predictions = model.predict(features_scaled)
+    probabilities = model.predict_proba(features_scaled)
+
+    accounts_df["is_bot"] = predictions
+    accounts_df["bot_probability"] = probabilities[:, 1]
+    accounts_df["human_probability"] = probabilities[:, 0]
+
+    return accounts_df
+
+
+# Example usage
+if __name__ == "__main__":
+    # Load model
+    print("Loading TikTok bot detection model...")
+    model = load_model()
+    print("✓ Model loaded successfully!\n")
+
+    # Example 1: Single account prediction
+    print("=" * 60)
+    print("Example 1: Single Account Prediction")
+    print("=" * 60)
+
+    suspicious_account = {
+        "IsPrivate": 0,
+        "IsVerified": 0,
+        "HasProfilePic": 1,
+        "FollowingCount": 5000,
+        "FollowerCount": 100,
+        "HasInstagram": 0,
+        "HasYoutube": 0,
+        "HasBio": 0,
+        "HasLinkInBio": 1,
+        "HasPosts": 1,
+        "PostsCount": 50,
+        "FollowToFollowerRatio": 50.0,
+    }
+
+    result = predict_single_account(model, suspicious_account)
+
+    print(f"Account Analysis:")
+    print(f"  Following: {suspicious_account['FollowingCount']}")
+    print(f"  Followers: {suspicious_account['FollowerCount']}")
+    print(f"  Posts: {suspicious_account['PostsCount']}")
+    print(f"\nPrediction:")
+    print(f"  Is Bot: {result['is_bot']}")
+    print(f"  Bot Probability: {result['bot_probability']:.2%}")
+    print(f"  Confidence: {result['confidence']:.2%}")
+
+    # Example 2: Batch prediction
+    print(f"\n{'='*60}")
+    print("Example 2: Batch Prediction")
+    print("=" * 60)
+
+    accounts = pd.DataFrame(
+        [
+            {
+                "IsPrivate": 0,
+                "IsVerified": 1,
+                "HasProfilePic": 1,
+                "FollowingCount": 500,
+                "FollowerCount": 10000,
+                "HasInstagram": 1,
+                "HasYoutube": 1,
+                "HasBio": 1,
+                "HasLinkInBio": 1,
+                "HasPosts": 1,
+                "PostsCount": 200,
+                "FollowToFollowerRatio": 0.05,
+            },
+            {
+                "IsPrivate": 0,
+                "IsVerified": 0,
+                "HasProfilePic": 0,
+                "FollowingCount": 8000,
+                "FollowerCount": 50,
+                "HasInstagram": 0,
+                "HasYoutube": 0,
+                "HasBio": 0,
+                "HasLinkInBio": 1,
+                "HasPosts": 1,
+                "PostsCount": 10,
+                "FollowToFollowerRatio": 160.0,
+            },
+        ]
+    )
+
+    results = predict_batch(model, accounts.copy())
+
+    print("\nResults:")
+    for idx, row in results.iterrows():
+        print(f"\nAccount {idx + 1}:")
+        print(f"  Followers: {row['FollowerCount']}")
+        print(f"  Is Bot: {bool(row['is_bot'])}")
+        print(f"  Bot Probability: {row['bot_probability']:.2%}")

requirements.txt

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+scikit-learn>=1.3.0
+pandas>=2.0.0
+numpy>=1.24.0
+joblib>=1.3.0