Fake News Detection in Internet Using Deep Learning: A Review

Abstract

The main objective of this research was to explore, from a reflexivity approach, the current state of Deep learning techniques for automatic detection of fake news on the Internet, analyzing the most important Deep learning algorithms and studies on their effectiveness in detecting distrustful information. The research methodology employed was bibliographic, documentary and descriptive. The information was collected from several scientific articles provided by indexed journals and web platforms, using keywords such as "fake news", "Deep learning" and "neural networks" for the compilation. As a result of this research, it was concluded that Deep learning techniques present a better performance than conventional methods and will be of great importance in the future of war against fake news due to their potential in automatic detection.

Top ten public health challenges to track in 2022

“…There is significant discordance between information on the Internet and evidence‐based sources. For example, studies have shown that up to 90% of information on diet and nutrition available on the Internet, usually social media, is unverified by science [57]. This shows the extent to which individuals and companies may go to sell their products using false science.…”