In the digital landscape, trivial rumors can spark significant online reactions. Accurate prediction of public opinion is important for crisis management, misinformation mitigation, and fostering public trust. However, existing methods often fail to thoroughly investigate multiple informational factors and their timely interactions, thereby limiting their efficacy in analyzing public opinion.
A comprehensive framework for predicting public opinion by tracking multi-informational dynamics
