The growing significance of social media in research demands new ethical standards and practices. Although a substantial body of literature on social media ethics exists, studies on the ethics of conducting research using social media are scarce. The emergence of new evidence sources, like social media, requires innovative methods and renewed consideration of research ethics. Therefore, we pose the following question: What are the defining characteristics of ethics papers on social media research? Following a modified version of the Preferred Reporting (...) Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, we analyzed 34 publications based on ten variables: author gender, publication year, region, academic discipline, type, design, methodology, social media platform in focus, positionality statement, and ethical issues. Our findings suggest contemporary social media research ethics primarily reflects the ethical ideals of the Global North, with limited representation from the Global South. Women authors have published more papers than men authors. Previous studies have prioritized ethical concerns such as privacy, informed consent, and anonymity while overlooking researchers’ risks and the ethics of social media sites. We particularly emphasized the lack of researchers’ positionality statements in research. Our findings will pave the way to understanding social media ethics better, especially with the rapid growth of social media research in global scholarship. (shrink)

The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning and deep learning models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k-nearest neighbor, and optimized artificial neural (...) network, have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1-score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score. (shrink)