# A Survey on Societal Event Forecasting with Deep Learning

arXiv: https://arxiv.org/abs/2112.06345 · HTML: https://mlsanity.com/paper/2112.06345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 36 of its 167 references that are in the map.
- First posted on arXiv 12 Dec 2021.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
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- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Structural-RNN: Deep Learning on Spatio-Temporal Graphs](https://mlsanity.com/paper/1511.05298/) (2015)
- [FractalNet: Ultra-Deep Neural Networks without Residuals](https://mlsanity.com/paper/1605.07648/) (2016)
- [Person Re-identification with Deep Similarity-Guided Graph Neural Network](https://mlsanity.com/paper/1807.09975/) (2018)
- [Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification](https://mlsanity.com/paper/1711.05859/) (2017)
- [Using Social Media to Predict the Future: A Systematic Literature Review](https://mlsanity.com/paper/1706.06134/) (2017)
- [Event Prediction in the Big Data Era: A Systematic Survey](https://mlsanity.com/paper/2007.09815/) (2020)
- ['Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators](https://mlsanity.com/paper/1402.7035/) (2014)
- [Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data](https://mlsanity.com/paper/1804.00684/) (2018)
- [Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data](https://mlsanity.com/paper/1409.2983/) (2014)
- [Examining Deep Learning Architectures for Crime Classification and Prediction](https://mlsanity.com/paper/1812.00602/) (2018)
- [Predicting Crowd Behavior with Big Public Data](https://mlsanity.com/paper/1402.2308/) (2014)
- [Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots](https://mlsanity.com/paper/1508.02050/) (2015)
- [Crime prediction through urban metrics and statistical learning](https://mlsanity.com/paper/1712.03834/) (2017)
- [Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction](https://mlsanity.com/paper/1902.03237/) (2019)
- [Crime Prediction Using Spatio-Temporal Data](https://mlsanity.com/paper/2003.09322/) (2020)
- [AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction](https://mlsanity.com/paper/2012.08713/) (2020)
- [EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting System](https://mlsanity.com/paper/1604.00033/) (2016)

## Abstract (arXiv)

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: textitcivil unrest and textitcrime. 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.