this thesis addresses the critical concerns of fairness, accountability, transparency, and ethics (fate) within the context of artificial intelligence (ai) systems applied to social media and healthcare domains. first, a comprehensive survey examines existing research on fate in ai, specifically focusing on the subdomains of social media and healthcare. the survey evaluates current solutions, highlights their benefits, limitations, and potential challenges, and charts out future research directions. key findings emphasize the significance of statistical and intersectional fairness in ensuring equitable healthcare access on social media platforms and highlight the pivotal role of transparency in ai systems to foster accountability. building upon the survey, this thesis delves into an analysis of social media usage by healthcare organizations, with a specific emphasis on engagement and sentiment forecasting during the covid-19 pandemic. data collection from twitter handles of pharmaceutical companies, public health agencies, and the world health organization enables extensive analysis. natural language processing (nlp)-based topic modeling techniques are applied to identify health-related topics, while sentiment forecasting models are employed to gauge public sentiment. the results uncover the impact of covid-19-related topics on public engagement, highlighting the varying levels of engagement across diverse healthcare organizations. notably, the world health organization exhibits dynamic engagement patterns over time, necessitating adaptable strategies. the thesis further presents latest sentiment forecasting models, such as autoregressive integrated moving average (arima) and seasonal autoregressive integrated moving average with exogenous factors (sarimax), which enable organizations to optimize their content strategies for maximum user engagement. furthermore, discourse analysis is conducted to unravel the factors that shape the content of tweets by healthcare organizations on twitter. [...]