  Population-level societal events, such as civil unrest and crime, often have
a significant impact on our daily life. Forecasting such events is of great
importance for decision-making and resource allocation. Event prediction has
traditionally been challenging due to the lack of knowledge regarding the true
causes and underlying mechanisms of event occurrence. In recent years, research
on event forecasting has made significant progress due to two main reasons: (1)
the development of machine learning and deep learning algorithms and (2) the
accessibility of public data such as social media, news sources, blogs,
economic indicators, and other meta-data sources. The explosive growth of data
and the remarkable advancement in software/hardware technologies have led to
applications of deep learning techniques in societal event studies. This paper
is dedicated to providing a systematic and comprehensive overview of deep
learning technologies for societal event predictions. We focus on two domains
of societal events: \textit{civil unrest} and \textit{crime}. We first
introduce how event forecasting problems are formulated as a machine learning
prediction task. Then, we summarize data resources, traditional methods, and
recent development of deep learning models for these problems. Finally, we
discuss the challenges in societal event forecasting and put forward some
promising directions for future research.
