Advanced Machine Learning Techniques for Fake News (Online Disinformation) Detection: A Systematic Mapping Study

TL;DR

This systematic mapping study reviews advanced machine learning techniques for fake news detection, analyzing current knowledge, datasets, challenges, and future research directions to combat disinformation effectively.

Contribution

It provides a comprehensive overview of ML-based fake news detection methods, datasets, and research gaps, guiding future developments in the field.

Findings

Identifies key datasets used in fake news detection

Highlights main challenges and methodological gaps

Summarizes current R&D projects and solutions

Abstract

Fake news has now grown into a big problem for societies and also a major challenge for people fighting disinformation. This phenomenon plagues democratic elections, reputations of individual persons or organizations, and has negatively impacted citizens, (e.g., during the COVID-19 pandemic in the US or Brazil). Hence, developing effective tools to fight this phenomenon by employing advanced Machine Learning (ML) methods poses a significant challenge. The following paper displays the present body of knowledge on the application of such intelligent tools in the fight against disinformation. It starts by showing the historical perspective and the current role of fake news in the information war. Proposed solutions based solely on the work of experts are analysed and the most important directions of the application of intelligent systems in the detection of misinformation sources are pointed out. Additionally, the paper presents some useful resources (mainly datasets useful when assessing ML solutions for fake news detection) and provides a short overview of the most important R&D projects related to this subject. The main purpose of this work is to analyse the current state of knowledge in detecting fake news; on the one hand to show possible solutions, and on the other hand to identify the main challenges and methodological gaps to motivate future research.