Future Broadcast Rating Projection Using Social Media Signals

The system segments the rating projection task into multiple components: collecting historical TV ratings, gathering social media indicators, obtaining sponsored-media spending data, transforming raw data into predictive features, and applying machine learning models (Stochastic Gradient Boosting Machine). Each component is processed separately and integrated to achieve high measurement precision while managing system complexity through modular architecture.

The patent introduces a central facility operated by an audience measurement entity as an intermediary that collects and processes data from multiple sources (TV ratings, social media platforms, advertising networks). This intermediary transforms raw data into predictive features and applies machine learning models to generate accurate rating projections, bridging the gap between diverse data sources and the final measurement output.

The system performs preliminary actions by collecting and storing historical TV ratings, social media indicators, and sponsored-media spending data before the actual rating projection is needed. The central facility maintains databases of these historical data, allowing the machine learning models to quickly process and generate accurate projections without the complexity of real-time data collection and processing.

The patent replaces traditional mechanical data processing methods with machine learning techniques, specifically Stochastic Gradient Boosting Machine (GBM). This substitution enables the system to handle complex, multi-source data (TV ratings, social media metrics, advertising spending) and automatically identify patterns and relationships that would be difficult to extract using conventional statistical methods, thereby improving measurement precision while managing processing complexity through algorithmic efficiency.