Hybrid Ensemble for Fake News Detection: An attempt
This is an incremental approach to fake news detection, a problem for researchers and practitioners in machine learning and NLP.
The paper tackles fake news detection by exploring various methods and proposing a hybrid ensemble that combines classical machine learning with modern deep learning approaches, but it does not report specific results or concrete numbers.
Fake News Detection has been a challenging problem in the field of Machine Learning. Researchers have approached it via several techniques using old Statistical Classification models and modern Deep Learning. Today, with the growing amount of data, developments in the field of NLP and ML, and an increase in the computation power at disposal, there are infinite permutations and combinations to approach this problem from a different perspective. In this paper, we try different methods to tackle Fake News, and try to build, and propose the possibilities of a Hybrid Ensemble combining the classical Machine Learning techniques with the modern Deep Learning Approaches