2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 10 , Issue 1 , PP: 34-62, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud M. Ismail 1 * , Nihal N. Mostafa 2 , Esmeralda Kazia 3 , Ibrahim Elhenawy 4
Doi: https://doi.org/10.54216/FPA.100103
By capitalizing on object relationships and local navigability, the social internet of things (SIoT) is one of the burgeoning paradigms that could solve the technical challenges of conventional IoT. Because of this paradigm's capacity to combine conventional IoT with social media, it is possible to create smart objects and services with greater utility than those created using conventional IoT infrastructures. In recent years, scholars have become interested in SIoT, leading to a plethora of works examining various mechanisms for providing services and technologies within this context. In this vein, we present a comprehensive review of recent research covering important aspects of SIoT. In this research, we give a detailed justification for the function of several machine learning paradigms and provide a practical application of it to unexamined concerns relating to erroneous data and other social IoT. First, we give a classification of false news detection approaches and an analysis of these techniques. Second, the potential uses for detecting fake news are examined at length, including how it might be applied to the areas of fake profile detection, traffic management, bullying detection, etc . We also suggested a detailed review of the possibilities of machine learning algorithms for detecting bogus news and intervening in social networks. In our paper, we introduce categories of fake news detection methods providing a comparison between these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and the security and privacy of SIoT. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in SIoT networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks for aiding the readers and researchers in explaining the motive and role of the different machine learning paradigms to offer them a comprehensive realization of so far unexplored issues related to false information and other scenarios of SIoT networks.
Social Internet of Things , Machine learning , False Information , Data Fusion
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