Advances in Human Event Modeling: From Graph Neural Networks to Language Models

Human events such as hospital visits, protests, and epidemic outbreaks directly affect individuals, communities, and societies. These events are often influenced by factors such as economics, politics, and public policies of our society. The abundance of online data sources such as social networks, official news articles, and personal blogs chronicle societal events, facilitating the development of AI models for social science, public health care, and decision making. Human event modeling generally comprises both the forecasting stage, which estimates future events based on historical data, and interpretation, which seeks to identify influential factors of such events to understand their causative attributes. Recent achievements, fueled by deep learning and the availability of public data, have significantly advanced the field of human event modeling. This survey offers a systematic overview of deep learning technologies for forecasting and interpreting human events, with a primary focus on political events. We first introduce the existing challenges and background in this domain. We then present the problem formulation of event forecasting and interpretation. We investigate recent achievements in graph neural networks, owing to the prevalence of relational data and the efficacy of graph learning models. We also discuss the latest studies that utilize large language models for event reasoning. Lastly, we provide summaries of data resources, open challenges, and future research directions in the study of human event modeling.