Sieving Fake News From Genuine: A Synopsis
This is an incremental review that addresses the issue of fake news for social media users and businesses, focusing on summarizing existing methods without novel contributions.
The paper tackles the problem of fake news dissemination on social media by providing a synopsis of current machine learning techniques, specifically NLP and deep learning, for automatic detection, but does not present new results or concrete numbers.
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.