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A Survey on Societal Event Forecasting with Deep Learning

arXiv 2112.06345 ↗First posted 12 Dec 2021cs.LG cs.AIcited by 0

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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.

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References · 36

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  5. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  6. 2013Auto-Encoding Variational Bayescited by 3,980
  7. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  8. 2015Effective Approaches to Attention-based Neural Machine Translationcited by 1,024
  9. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  10. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 504
  11. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 395
  12. 2018Graph Convolutional Networks for Text Classificationcited by 345
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  1. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
  2. 2015Structural-RNN: Deep Learning on Spatio-Temporal Graphscited by 234
  3. 2016FractalNet: Ultra-Deep Neural Networks without Residualscited by 216
  4. 2018Person Re-identification with Deep Similarity-Guided Graph Neural Networkcited by 49
  5. 2017Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classificationcited by 40
  6. 2017Using Social Media to Predict the Future: A Systematic Literature Reviewcited by 12
  7. 2020Event Prediction in the Big Data Era: A Systematic Surveycited by 12
  8. 2014'Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicatorscited by 10
  9. 2018Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Datacited by 10
  10. 2014Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Datacited by 9
  11. 2018Examining Deep Learning Architectures for Crime Classification and Predictioncited by 7
  12. 2014Predicting Crowd Behavior with Big Public Datacited by 6
  13. 2015Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspotscited by 6
  14. 2017Crime prediction through urban metrics and statistical learningcited by 6
  15. 2019Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime predictioncited by 6
  16. 2020Crime Prediction Using Spatio-Temporal Datacited by 5
  17. 2020AIST: An Interpretable Attention-based Deep Learning Model for Crime Predictioncited by 5
  18. 2016EMBERS at 4 years: Experiences operating an Open Source Indicators Forecasting Systemcited by 4
  19. 2017Deep Learning for Real-Time Crime Forecasting and its Ternarizationcited by 4
  20. 2018Forecasting Crime with Deep Learningcited by 4
  21. 2016Modeling Precursors for Event Forecasting via Nested Multi-Instance Learningcited by 3
  22. 2018Spatio-temporal prediction of crimes using network analytic approachcited by 3
  23. 2019Modelling Regional Crime Risk using Directed Graph of Check-inscited by 3
  24. 2019Spatial Modeling of Trends in Crime over Time in Philadelphiacited by 2

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