This study investigates the factors that influence the sharing of information on social media platforms and examines the effects of perceived security, perceived privacy, and user awareness on users' trust in social media platforms, as well as the moderating effects of age, gender, educational attainment, and internet proficiency on information sharing. The study collected data from 837 social media users in Jordan and analyzed them using structural equation modeling (SEM), confirmatory factor analysis (CFA), and machine learning (ML) methods. The findings of the study indicate that perceived security, perceived privacy, and user awareness all have a significant impact on users' trust in social media platforms. Trust, in turn, has a significant impact on the amount of information shared on these platforms. Also, the findings of this study provide valuable insights into the dynamics of information sharing on social networks. This knowledge will be of interest to managers, policymakers, and developers of social media platforms. In addition, the findings of the study also have implications for the privacy and security of social media users. For example, social media users can be more careful about the information they share on social media platforms, and they can take steps to protect their privacy.

In the era of IoT gaining traction, attacks on IoT-enabled devices are the order of the day that emanates the need for more protected IoT networks. IoT's key feature deals with massive amounts of data sensed by numerous heterogeneous IoT devices. Numerous machine learning techniques are used to collect data from different types of sensors on the objects and transform them into information relevant to the application. Furthermore, business and data analytics algorithms help in event prediction based on observed behavior and information. Routing information securely over the internet with limited resources in IoT applications is a key problem. The study proposes a model for detecting network anomalies in IoT devices to enhance the security of the devices. The study employed the IoT Botnet dataset, and K-fold cross-validation tests were used for validating the values of evaluation metrics. The average values of Accuracy, Precision, Recall, and F Score was 97.4.

The expression "social media" refers to a software-based platform developed for users’ benefit. People use it to gain social power, market their products, conduct online business, and share information and ideas. This digital ecosystem has become helpful in various ways, but research indicates that it does not come for free. Addiction, depression, and anxiety are some of the adverse conditions discussed in many studies. The purpose of this study is to mark if there is a relationship between using social media networks and the numbering of people with anxiety or depression. Also, by addressing the need to learn more about what makes people use social networks and how that use affects anxiety and depression in Arabic-speaking users in Jordan, we can help people from different cultures understand each other better. This research uses TAM, telepresence, and survey data from 1050 people, mainly from Jordan. The research looks at how the usage of social media is related to supposed usefulness, supposed ease of use, trust, social influence, age, gender, level of education, marital status, the time spent on the internet, preferred social media network, and perceived usefulness of SNS. AMOS 20 methods of confirmatory factor analysis (CFA), structural equation modeling (SEM), and machine learning (ML), such as SMO, ANN, random forest, and the bagging reduced error pruning tree (RepTree), were used to test the proposed model hypotheses. According to the results, the researchers found high correlations between social network usage and depression and anxiety. The use of social networking sites is also affected by how useful they are seen to be, how easy they are to use, trust, social influence, and telepresence. Also, the moderator's age, gender, level of education, marital status, amount of time spent on the internet, experience with the internet, and favorite social networks all affect how they plan to use social networks.