Integrating Temporality

Integrating temporality into data analysis focuses on accurately representing and utilizing the temporal dimension of information, improving the understanding and modeling of dynamic systems. Current research employs machine learning models, including transformer-based architectures and topic models like hierarchical temporal topic modeling, to detect and incorporate temporal information in diverse data types such as text, images, and social media posts. This work is crucial for enhancing the accuracy and reliability of analyses across various fields, from financial prediction and historical image dating to stance detection and cultural studies, by accounting for evolving contexts and trends over time.