Sieving Fake News From Genuine: A Synopsis

TL;DR

This paper provides an overview of fake news, illustrating its impact, and discusses current NLP and deep learning methods for automatic fake news detection, emphasizing the importance of quality data and features.

Contribution

It offers a comprehensive synopsis of fake news, including examples and an analysis of machine learning techniques for detection, highlighting future potential.

Findings

NLP and deep learning can improve fake news detection

Quality data and features are crucial for effective models

Fake news significantly impacts society and economy

Abstract

With the rise of social media, it has become easier to disseminate fake news faster and cheaper, compared to traditional news media, such as television and newspapers. Recently this phenomenon has attracted lot of public attention, because it is causing significant social and financial impacts on their lives and businesses. Fake news are responsible for creating false, deceptive, misleading, and suspicious information that can greatly effect the outcome of an event. This paper presents a synopsis that explains what are fake news with examples and also discusses some of the current machine learning techniques, specifically natural language processing (NLP) and deep learning, for automatically predicting and detecting fake news. Based on this synopsis, we recommend that there is a potential of using NLP and deep learning to improve automatic detection of fake news, but with the right set of data and features.