• Engagement Tracking:
• Monitors tweet performance via Twitter API.
• Stores metadata in a database to detect engagement patterns.
• Uses sentiment analysis to measure response quality.

• Reinforcement Learning-Based Optimization:
• Assigns scores to engagement metrics (likes, retweets, replies, sentiment shifts).
• Uses PPO/DQN algorithms to fine-tune future tweets.
• Implements weighted scoring models to prioritize successful engagement patterns.
• Reward signals are updated in real-time based on engagement feedback.

• Trend-Based Adaptation:
• Identifies and interacts with targeted users based on relevance to the client’s industry or campaign.
• Auto-adjusts engagement frequency and style to match high-value interactions.

• Dynamic Audience Segmentation: Categorizes users into high-engagement, moderate, and passive followers for more tailored interactions.

• Anomaly Detection for Spam Avoidance: Uses behavioral tracking to filter out engagement from low-quality accounts or bot interactions.