Fake News Detection Using Machine Learning Classification Algorithms

Abstract

In the context of Indian politics in particular, the proliferation of fake news poses a serious threat to the reliability of news and information. To combat this problem, we present a machine learning-based method for identifying false news stories by com-paring a dataset of true and false news items with a model that analyses news titles via vectorization and tokenization. In order to tell the difference between real news and fake propaganda, we’re working on a model to appropriately categorize news articles based on their textual content. Our method is tested on a standard collection of news articles in order to gauge its efficacy. Several state-of-the-art methods in the literature are outperformed by our suggested method, demonstrating its superior accuracy in detecting bogus news. Our methodology is particularly well-suited for use in the context of Indian politics, where it can aid in the detection of fake news and the defence of trustworthy news and information sources. A strong and successful methodology for detecting and limiting the effects of fake news, this method can be expanded to additional fields and languages. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.