Social Medium Mining

Social media mining leverages artificial intelligence and natural language processing to extract valuable insights from online platforms, primarily focusing on understanding public sentiment, identifying trends, and detecting specific events or behaviors. Current research heavily utilizes transformer-based models like BERT and LLMs, along with graph convolutional networks, to analyze textual data, often incorporating techniques like data augmentation to address class imbalances and improve model performance in tasks such as sentiment analysis, hate speech detection, and the identification of health-related discussions. This field significantly impacts public health surveillance, political analysis, and crisis response by providing timely and nuanced data unavailable through traditional methods.