Novel approach for predicting fake news stance detection using large word embedding blending and customized CNN model.

ABSTRACT: The proliferation of fake news is one of the major problems that causes personal and societal harm. In today's fast-paced digital age, misinformation spreads rapidly, often leaving individuals without the time to verify the authenticity of the information. This can cause irreparable damage to personal reputations and organizational credibility. Thus, instigated by the quintessential necessity, there is a dire need to construct a framework for the automatic detection and identification of fake news at its inception. This research presents a novel approach that leverages a combination of three popular word embeddings (FastText, FastText-Subword, and GloVe) integrated with a customized convolutional neural-network(CNN) to classify fake news accurately. The proposed model was tested against th

SUBMITTER: Altamimi A 

PROVIDER: S-EPMC11643300 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature