Causality Enhanced Societal Event Forecasting With Heterogeneous Graph Learning

Abstract

Using observational event data to forecast societal events has been extensively studied in data-driven models. Prior work focuses on correlational analysis and ignores the importance of causal relationships behind events. Understanding the causality of events helps one infer future events by pinpointing potential triggers. In light of complex and dynamic social environments, it is difficult to comprehensively analyze the causes of societal events. In this work, we study the causal relationship between topics and events where topics are extracted from event-related documents. These topics represent probability distributions of words. We introduce a method to discover topics that have a causal effect on future events of interest. Next, we propose a causality-enhanced dynamic heterogeneous graph learning framework where topics, documents, and words are represented as nodes with changing edges. To handle the temporal dependencies of dynamic graphs, we introduce a temporal information learning module that updates node representations based on their evolving context and heterogeneous semantics. We conduct extensive experiments on four real-world datasets and demonstrate the effectiveness of our method in societal event prediction.