## **Objective**

Develop a high-accuracy, real-time bot detection model using the PyTorch Python Framework to identify bot-like behavior in comments on web platforms. The solution should effectively differentiate between authentic human users and bots, even as bot behaviors evolve.

## **Scope of Work**

### **Data Collection & Preprocessing**:

• Use existing labeled datasets (e.g., platform comment data with bot/human labels) and/or generate synthetic data if real-world labeled data is limited.

• Clean, tokenize, and preprocess text comments. Implement NLP techniques to standardize input for the model.

### **Model Development**:

• Develop a deep learning model in PyTorch. Options could include:

• Transformer-based models (e.g., BERT, RoBERTa) fine-tuned for bot detection.

• RNN or LSTM models with attention mechanisms if appropriate.

• Logistic Regression

• Incorporate both text content and potential behavioral features (e.g., comment frequency, sentiment score, posting patterns).

###  **Training & Optimization**:

• Train the model on labeled data and optimize for high precision and recall, especially given the class imbalance often present (bots being a minority).

• Employ techniques like class weighting or oversampling to improve bot identification accuracy.

• Fine-tune hyperparameters, evaluate multiple architectures, and explore regularization methods to improve generalization.

###  **Evaluation & Testing**:

•  Validate the model on a separate test set, ideally containing real-world comments with varying bot characteristics.

•  Provide detailed metrics, including precision, recall, F1 score, and ROC-AUC.

•  Test model robustness against adversarial examples, such as bots mimicking human behavior.

##  **Deployment Readiness**:

•  Package the trained model with PyTorch and document steps for deployment in Google Drive and provide the link on the proposal

•  Include considerations for model scalability, including batch processing for real-time scenarios.

### **Requirements**

• **Experience** with NLP, deep learning models, and PyTorch.

• **Proficiency** in handling text data, model optimization, and classification in the presence of imbalanced datasets.

•  **Ability** to develop a robust, scalable solution suitable for deployment.

### **Deliverables**

• **Codebase**: Well-documented PyTorch code, including model architecture, training pipeline, and evaluation scripts.

• **Trained Model**: Exportable PyTorch model (.pth file) ready for deployment.

• **Documentation**: Detailed README with installation instructions, dataset preparation steps, and usage guidelines.

• **Performance Report**: Analysis with metrics, test set performance, and tuning details.

Develop an AI PyTorch Model to Identify Bot Activity in User Comments

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