Unmasking Deceptive Information: Strategies and Tools for False Information Detection using Machine Learning

The widespread adoption of social media platforms has given rise to a plethora of multimedia content circulating across these networks.The open and unrestricted sharing of information on these platforms has created an environment where data dissemination on the internet is unbounded by considerations of its credibility.Within the realm of social media, the propagation of misinformation, often in the form of false information or rumors, is a prevalent issue.Such unverified information can have dire consequences.Despite the extensive usage of social media platforms, their unregulated nature frequently fosters the generation and diffusion of unverified and speculative content.Consequently, the automatic detection of rumors on social media platforms has emerged as a crucial research domain within the field of social analysis.With this same motivation in mind, this article places its focus on datasets and cutting-edge methodologies employed for rumor detection.Furthermore, it delves into both supervised and unsupervised approaches, as well as delving into the application of deep learning techniques for word recognition.