Combating Fake News: The Role of Effective Pre-Processing Techniques

Abstract

This study highlights the importance of detecting fake news and the necessity of effective pre-processing techniques to clean and merge datasets. By utilizing NLP techniques and Python programming, this study successfully merged two Indian datasets and performed pre-processing tasks to improve the quality of the data. The experiment section provides detailed insights into the process of merging and pre-processing, including code snippets and graphs to replicate the results. While this study contributes to the development of effective pre-processing techniques for fake news detection, there is still much work to be done in improving the accuracy of fake news detection. Future research can explore the use of deep learning classifiers and multimodality in fake news detection to enhance our ability to detect fake news and promote a more informed society.