Towards Robust Fake News Detection: An Emperical Investigation of Preprocessing and Modeling Techniques

The challenge of identifying fake news through text classification has been met with extensive experimentation to find the best mix of preprocessing techniques and algorithms. Despite efforts, an effective fake news detection model is still out of reach, hindered by model implementation issues and the lack of clean, unbiased data. This situation highlights the need for further research in this area. We investigated many models and approaches for preparing data, including both deep learning algorithms like CNN and LSTM networks and more conventional machine learning algorithms like Random Forest and Gradient Boost. This exploration revealed specific challenges, notably the tendency of automated learning to focus narrowly on individual news items. This paper presents a thorough application of various documented preprocessing techniques and model architectures, aiming to lay a foundation for future research, particularly for newcomers. The performance of these models was tested on two widely used datasets, making a significant contribution to the field of fake news detection. The insights from this study are expected to guide future research and aid in developing more effective detection mechanisms.