Synergistic News Aggregation Paradigm Utilizing Autonomous Web Extraction and Computational Intelligence

The exponential growth of digital news sources has revolutionized the way information
is consumed, but it has also introduced significant challenges, such as information
overload, data redundancy, misinformation, and biased reporting. To address these
issues, this chapter proposes a Synergistic News Aggregation Paradigm Utilizing
Autonomous Web Extraction and Computational Intelligence. This system leverages
advanced technologies like artificial intelligence (AI), natural language processing
(NLP), and machine learning (ML) to autonomously extract, filter, and deliver
personalized, real- time news content from diverse online sources. The proposed
framework integrates autonomous web extraction techniques for real- time data
collection with computational intelligence models for content analysis, sentiment
detection, and context- based classification. It ensures the credibility and relevance
of aggregated news while eliminating data redundancy through sophisticated clustering
and summarization methods.