Adobe Behaviour Simulation Challenge

Inter IIT Tech Meet 12.0 - Social Media Content & Behavior Analysis

Project Overview

Developed a comprehensive system for social media behavior simulation and content generation, leveraging a combination of classic machine learning models, transformers, and large language models to analyze and predict user behavior on Twitter.

Challenge Description

The Adobe Behaviour Simulation Challenge required building models that could:

  • Predict User Engagement: Accurately forecast likes, retweets, and other engagement metrics
  • Generate Content: Create realistic social media content and captions
  • Understand Behavior Patterns: Analyze and simulate user behavior across different content types

Technical Architecture

1. Tweet Classification & Bucketing

  • DistilBERT Classifier: Trained a DistilBERT model to categorize tweets into different buckets
  • Multi-class Classification: Handled various tweet types and content categories
  • Feature Engineering: Extracted meaningful features from tweet text, metadata, and user information

2. Engagement Prediction

  • Bucket-specific Regressors: Developed separate regression models for each tweet bucket
  • Likes Prediction: Specialized models to predict engagement metrics
  • Performance Optimization: Fine-tuned models for accuracy and inference speed

3. Media Description & Captioning

  • BLIP-2 Integration: Used BLIP-2 for automatic image description generation
  • Metadata Fusion: Combined visual descriptions with tweet metadata
  • Llama-2 Fine-tuning: Fine-tuned Llama-2 for context-aware captioning

Results & Performance

Classification Performance

  • 74.3% accuracy in bucket classification
  • Robust Performance: Consistent results across different tweet categories
  • Fast Inference: 0.9 seconds average inference time for classification

Content Generation

  • BLEU-1 Score: Achieved 0.13 BLEU-1 score for generated content
  • Generation Speed: 5.4 seconds average generation time
  • Quality Assessment: High-quality, contextually relevant content generation

Technical Implementation

Model Pipeline

  1. Preprocessing: Tweet text cleaning and normalization
  2. Classification: DistilBERT-based categorization
  3. Feature Extraction: Multi-modal feature engineering
  4. Regression: Bucket-specific engagement prediction
  5. Generation: Context-aware content creation

Optimization Techniques

  • Model Compression: Optimized for deployment efficiency
  • Batch Processing: Efficient handling of multiple requests
  • Caching: Smart caching for frequently accessed data

Key Innovations

Multi-Modal Approach

  • Text + Visual: Combined textual content with visual media analysis
  • Metadata Integration: Leveraged user profiles and temporal data
  • Cross-Modal Learning: Learning representations across different data types

Hierarchical Processing

  • Coarse-to-Fine: Initial categorization followed by fine-grained analysis
  • Specialized Models: Bucket-specific models for better accuracy
  • Ensemble Methods: Combined multiple model predictions

Technical Stack

  • Deep Learning: PyTorch, Transformers (DistilBERT, Llama-2)
  • Computer Vision: BLIP-2 for image understanding
  • Classical ML: Scikit-learn for regression models
  • Data Processing: Pandas, NumPy for data manipulation
  • Evaluation: Custom metrics for social media content assessment

Impact & Applications

Real-World Applications

  • Social Media Analytics: Understanding user engagement patterns
  • Content Strategy: Optimizing content for better engagement
  • Automated Content Creation: AI-powered social media management

Business Value

  • Predictive Insights: Help brands understand audience behavior
  • Content Optimization: Improve content creation strategies
  • Engagement Forecasting: Predict viral content potential

This project showcased the power of combining traditional machine learning with modern transformer architectures to solve complex, multi-modal problems in social media analytics and content generation.