Exploring machine learning for fake news detection: techniques, tools, challenges, and future research directions

Document Type

Article

Publication Title

Discover Applied Sciences

Abstract

Social media usage has reached its peak across all age groups, resulting in vast quantities of data being generated daily, some internet users disseminate fake information for their own benefit. Manually checking each user profile is time-consuming, necessitating automated solutions. This paper addresses multimodal fake news detection, examining evolving landscape of fake information on social media. It highlights the limitations of techniques that rely on single modality. This review underscores the necessity of automated systems capable of detecting and monitoring fake information in multimodal content on social media platforms. The variety of multimodal datasets, tools, and machine learning techniques used to determine the authenticity and virality of online social media content. The research outlines standard performance indicators to evaluate content, aiming to enhance the accuracy of this ongoing global issue. Furthermore, this study highlights the main challenges that are identified and suggests directions for future research to mitigate its harmful social impact. This paper emphasizes the importance of automating the verification of online information to ensure authenticity, thereby contributing to a more secure digital atmosphere.

DOI

10.1007/s42452-025-07548-3

Publication Date

8-1-2025

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