Causal Temporal Narrative

Causal temporal narrative research focuses on understanding and modeling how events unfold causally and temporally within narratives, aiming to extract and represent these relationships computationally. Current work utilizes large language models and specialized neural network architectures, such as cause-effect networks, to analyze narratives across diverse media like text and video, identifying causal links between events and their temporal ordering. This research has significant implications for social science, enabling the quantitative analysis of bias in narratives and the automated extraction of causal explanations from large datasets, ultimately improving our understanding of how narratives shape our perception of events.