Fake News Detection Review: Concepts, Tools, Research GAPS, and Possible solutions

The increasing number of social platforms means that there is a rapid expansion of online data. However, not all of this data can be trusted, as some users intentionally manipulate information to spread false news for personal or political reasons. Fake news can significantly impact public perception and societal outcomes. Its widespread influence was especially noticeable during the 2016 United States election cycle, leading to heated discussions about its role and consequences. Given the changing nature of news, real-time data processing has become essential for effectively identifying and countering misinformation. This research provides a comprehensive examination of the datasets, tools, and techniques utilized for detecting and combating fake news. Many researchers have developed models based on specific datasets, limiting their scope to particular domains. A thorough review of advanced models for real-time fake news detection highlights existing research gaps. The analysis of the review reveals that numerous datasets used in the literature are outdated, and there is a methodological challenge associated with the use of traditional machine learning approaches. With the continuous evolution of news, there is an urgent demand for utilizing sophisticated deep learning models for the automated, real-time verification of misinformation. This research presents promising approaches to tackle the challenge of detecting fake news.