Operationalizing Common Crawl News: AI-Enabled Data Pipeline for Large-Scale News Analysis

The exponential growth of online news data has created new opportunities and challenges for academic, commercial and government analysis and workflows. This paper proposes an intelligent data system that operationalizes the Common Crawl News dataset for diverse applications, focusing on parsing, filtering, machine learning enrichment, and data modeling. This paper outlines the development of a robust data pipeline that extracts relevant metadata, filters mislabeled news articles, and applies machine learning techniques, including Named Entity Recognition and language-agnostic sentence embeddings, to enhance the utility of the data. The work focuses on news classification, event detection, and ongoing efforts to develop dashboards and interactive applications for visualizing trends, named entity distributions, and geospatial patterns. This system enables efficient search and analysis of sizeable multilingual news data, empowering users to derive actionable insights from a vast and varied corpus. This research contributes to the growing body of work on leveraging open-source news data for real-world applications in decision-making and research.