DeepSpam: Neural Network Approach to Detect Spam in YouTube Comments

Abstract:

With the exponential rise in user-generated content on platforms like YouTube, spam comments have become a major concern, reducing content quality and user engagement. Traditional spam detection systems rely on manual moderation or rule-based filtering, which are often inefficient and unable to adapt to evolving spam tactics. This project proposes an Artificial Neural Network (ANN)-based spam detection model to automatically classify YouTube comments as spam or non-spam. The model is trained using text preprocessing techniques such as tokenization, stemming, and vectorization to improve accuracy. By leveraging ANN, the system achieves superior performance compared to conventional approaches, providing a scalable and efficient spam detection mechanism.

Introduction:

YouTube is one of the largest content-sharing platforms, attracting millions of users daily. However, the platform is increasingly plagued by spam comments that promote unrelated content, phishing links, and misleading information. Existing moderation techniques struggle to keep pace with the sheer volume of comments, necessitating an automated solution. Artificial Neural Networks (ANNs) provide a powerful mechanism for text classification, making them suitable for identifying and filtering spam comments. This project explores an ANN-based approach to enhance the reliability and efficiency of spam detection on YouTube.

Problem Statement:

Spam comments on YouTube videos negatively impact user experience, content credibility, and engagement. Manual moderation is labor-intensive and rule-based filtering methods lack adaptability. The need for an intelligent, automated spam detection system that efficiently classifies comments while minimizing false positives and false negatives is critical.

Existing System and Disadvantages:

Existing System:

• Many spam detection mechanisms rely on keyword-based filtering, regular expressions, or predefined rules.
• Machine learning classifiers such as Naïve Bayes and Decision Trees have been used in some cases.
• YouTube’s built-in spam detection system filters potentially harmful comments, but its accuracy is often insufficient.

Disadvantages:

• Rule-based filters are easy to bypass by spammers using slight text modifications.
• Machine learning models like Naïve Bayes fail to capture complex relationships in text data.
• High false positive and false negative rates in existing models.
• Manual moderation is time-consuming and inefficient at scale.

Proposed System and Advantages:

Proposed System:

• This project utilizes an Artificial Neural Network (ANN)-based model for spam detection.
• The system preprocesses YouTube comments using techniques such as tokenization, stopword removal, stemming, and TF-IDF vectorization.
• The ANN model learns from labeled data and adapts to evolving spam patterns.
• The architecture includes an input layer, hidden layers with activation functions, and an output layer for classification.

Advantages:

• Higher Accuracy: ANN can learn complex patterns in spam comments.
• Scalability: Can handle large volumes of comments efficiently.
• Adaptability: ANN models can be retrained on new data to detect evolving spam trends.
• Automation: Reduces dependency on manual moderation, saving time and effort.

Modules:

1. Data Collection Module:
   • Collects YouTube comments dataset from public sources.
2. Data Preprocessing Module:
   • Cleans text, removes special characters, stopwords, and applies stemming/lemmatization.
3. Feature Extraction Module:
   • Converts text into numerical format using TF-IDF vectorization.
4. Model Training Module:
   • Trains an ANN model with labeled data.
5. Model Evaluation Module:
   • Evaluates performance using accuracy, precision, recall, and F1-score.
6. Deployment Module:
   • Integrates the trained model into a web interface for real-time spam detection.

Algorithms:

• Artificial Neural Network (ANN)
  • Input layer: Processes text features.
  • Hidden layers: Extract deep patterns in text.
  • Output layer: Classifies comments as spam or non-spam.
• Activation Functions: ReLU, Sigmoid
• Loss Function: Binary Cross-Entropy
• Optimizer: Adam

Software and Hardware Requirements:

Software Requirements:

• Python 3.x
• Jupyter Notebook
• TensorFlow/Keras (for ANN)
• NLTK, SpaCy (for NLP processing)
• Scikit-learn (for feature extraction and evaluation)

Hardware Requirements:

• Processor: Intel Core i5 or higher
• RAM: Minimum 8GB (Recommended: 16GB for larger datasets)
• Storage: 50GB free disk space
• GPU: (Optional) NVIDIA GPU for faster training

Conclusion:

The ANN-based spam detection system effectively classifies YouTube comments, improving accuracy over traditional filtering methods. By leveraging deep learning, the model can adapt to evolving spam trends, ensuring a more robust and scalable solution. The proposed system significantly reduces manual moderation efforts while enhancing the reliability of spam detection.

Future Enhancements:

• Multilingual Support: Extend the model to detect spam in multiple languages.
• Real-Time Detection: Deploy the system in real-time YouTube comment sections.
• Hybrid Models: Combine ANN with NLP-based transformers like BERT for higher accuracy.
• Explainability: Implement SHAP or LIME to interpret model decisions.
• Cloud Deployment: Deploy the system as a cloud API for integration with multiple platforms.