Machine Learning Frameworks for Fake News Detection and Datasets

The folder also includes several datasets for experimentation, alongside the source code. The rise of social media has transformed the landscape of news dissemination, presenting new challenges in combating the spread of fake news.

This study addresses the automated detection of misinformation within written content, a task that has prompted extensive research efforts across various methodologies. We evaluate existing benchmarks, introduce a novel hybrid word embedding model, and implement a web framework for text classification. Our approach integrates traditional frequency–inverse document frequency (TF–IDF) methods with sophisticated feature extraction techniques, considering linguistic, psychological, morphological, and grammatical aspects of the text.