An Efficient Deep Diffusion Neural Network for Automated Fake News Classification

Abstract: In today's modern world, "fake news" has been a major concern, spreading like wildfire through many platforms. This phenomenon not only undermines the credibility of information but also misleads society. Nowadays, social media is the greatest means by which fake news spreads all over the place. This can cause many problems such as defamation of people and spreading news in favor of specific individuals. Fake news often targets the most prominent, powerful, and influential people in society, aiming to tarnish their reputation. The escalating impact of fake news knows no bounds. Fake news is often biased, favoring a single person or a section of people in society for their personal benefits. To mitigate these challenges and promote transparency, there is a need to reduce the spread of fake news. Introducing a "Fake News Classifier using NLP" offers a promising solution to combat this issue. By using machine learning algorithms, this classifier can effectively identify misleading information as fake news, thereby contributing to awareness in society and reducing losses.

Keywords: Natural Processing Language, TF-IDF, Flask, Classification, Multi nomialNB, Accuracy.