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

Songgaojun Deng, Maarten de Rijke, Yue Ning

Abstract:

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.