DEEP LEARNING FRAMEWORK FOR IDENTIFYING AND HYBRID SYNTHESIZING WIDESPREAD NEWS HEADLINES ONSOCIAL MEDIA

The proliferation of social media platforms has revolutionized the way news is distributed and consumed, raising significant challenges for news authenticity and reliability.This paper introduces a deep learning framework designed to identify and hybridize the synthesis of widespread news headlines on social media.By integrating natural language processing (NLP) with deep learning techniques, this framework aims to tackle the challenges posed by the rapid dissemination of information, including the detection of sarcasm, fake news, and misleading headlines.The study explores various deep learning models, such as BERT and ensemble methods, to enhance the accuracy and reliability of news headline synthesis and verification.