Data analytics of social media 3.0: Privacy protection perspectives for integrating social media and Internet of Things (SM-IoT) systems

Abstract

With the rapid evolution of web technologies, Web 3.0 aims to expand on current and emerging social media platforms such as Facebook, Twitter, and TikTok, and integrate emerging computing paradigms, including the Internet of Things (IoT), named social media 3.0. The combinations of these platforms in Web 3.0 promises consumers greater integration, interaction, and more seamless movement between physical spaces. However, ensuring the privacy of data across such systems is a potential challenge in this space. In this study, we propose a new privacy-preserving social media 3.0 framework that illustrates the interaction of SM and IoT services and estimates how this interaction could impact users’ behaviors. The framework consists of three main components. First, a new relational dataset, named SM-IoT, is designed to dynamically connect users with their IoT services and assist in processing data heterogeneity. Second, a data pre-processing module is employed for filtering heterogeneous data and providing a certain level of privacy preservation on the data. Third, data analytics using different statistical and machine/deep learning methods are applied to examine data complexity and identify users’ behaviors. The results revealed that our proposed framework can efficiently identify users’ behaviors from social media 3.0 data sources. The outcomes of comparing our SM-IoT dataset with two other well-known SM datasets, namely Pokec and Renren, as well as Activity Recognition with Ambient Sensing (ARAS) IoT dataset reveals the fidelity of our dataset to be used for future evaluations of privacy-preserving and machine learning-based decision-making techniques.