A Survey on Societal Event Forecasting with Deep Learning

• URL: http://arxiv.org/abs/2112.06345v1
• Date: Sun, 12 Dec 2021 22:57:22 GMT
• Title: A Survey on Societal Event Forecasting with Deep Learning
• Authors: Songgaojun Deng and Yue Ning
• Abstract summary: 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.
• Score: 12.343312954353639
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Generalizing to Unseen Disaster Events: A Causal View [17.9089265435157]
  We propose a method to reduce event- and domain-related biases, enhancing generalization to future events.<n>Our approach outperforms multiple baselines by up to +1.9% F1.
  arXiv  Detail & Related papers  (2025-11-13T09:29:39Z)
• Misspellings in Natural Language Processing: A survey [52.419589623702336]
  misspellings have become ubiquitous in digital communication. We reconstruct a history of misspellings as a scientific problem. We discuss the latest advancements to address the challenge of misspellings in NLP.
  arXiv  Detail & Related papers  (2025-01-28T10:26:04Z)
• Predicting Country Instability Using Bayesian Deep Learning and Random Forest [0.0]
  Country instability is a global issue, with unpredictably high levels of instability thwarting socio-economic growth and possibly causing a slew of negative consequences. The Global Database of Activities, Voice, and Tone (GDELT Project) records broadcast, print, and web news in over 100 languages every second of every day. The main goal of our research is to investigate how, when our data grows more voluminous and fine-grained, we can conduct a more complex methodological analysis of political conflict.
  arXiv  Detail & Related papers  (2024-11-11T00:23:03Z)
• OpenEP: Open-Ended Future Event Prediction [57.63525290892786]
  We introduce OpenEP (an Open-Ended Future Event Prediction task), which generates flexible and diverse predictions aligned with real-world scenarios. For question construction, we pose questions from seven perspectives, including location, time, event development, event outcome, event impact, event response, and other. For outcome construction, we collect free-form text containing the outcomes as ground truth to provide semantically complete and detail-enriched outcomes.
  arXiv  Detail & Related papers  (2024-08-13T02:35:54Z)
• Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry [1.9370374882811083]
  The petroleum industry is crucial for modern society, but the production process is complex and risky. undesired production events can cause severe environmental and economic damage. Previous studies have investigated machine learning (ML) methods for undesired event detection. This paper proposes two ML approaches, random forests and temporal convolutional networks, to detect undesired events in real-time.
  arXiv  Detail & Related papers  (2023-10-12T21:50:53Z)
• A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas [0.0]
  An event prediction method learns the relation between features of past events and future events.<n>It is applied to newly observed events to predict corresponding future events that are evaluated with respect to the user's desired future state.
  arXiv  Detail & Related papers  (2023-02-08T12:21:02Z)
• Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
  This survey delves into various data-driven methodological and practical advancements. We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting. We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
  arXiv  Detail & Related papers  (2022-07-19T16:15:11Z)
• Causal Knowledge Guided Societal Event Forecasting [24.437437565689393]
  We introduce a deep learning framework that integrates causal effect estimation into event forecasting. Two robust learning modules, including a feature reweighting module and an approximate loss, are introduced to enable prior knowledge injection.
  arXiv  Detail & Related papers  (2021-12-10T17:41:02Z)
• Individual Explanations in Machine Learning Models: A Case Study on Poverty Estimation [63.18666008322476]
  Machine learning methods are being increasingly applied in sensitive societal contexts. The present case study has two main objectives. First, to expose these challenges and how they affect the use of relevant and novel explanations methods. And second, to present a set of strategies that mitigate such challenges, as faced when implementing explanation methods in a relevant application domain.
  arXiv  Detail & Related papers  (2021-04-09T01:54:58Z)
• REST: Relational Event-driven Stock Trend Forecasting [76.08435590771357]
  We propose a relational event-driven stock trend forecasting (REST) framework, which can address the shortcoming of existing methods. To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts. To address the second shortcoming, we construct a stock graph and design a new propagation layer to propagate the effect of event information from related stocks.
  arXiv  Detail & Related papers  (2021-02-15T07:22:09Z)
• Patterns, predictions, and actions: A story about machine learning [59.32629659530159]
  This graduate textbook on machine learning tells a story of how patterns in data support predictions and consequential actions. Self-contained introductions to causality, the practice of causal inference, sequential decision making, and reinforcement learning equip the reader with concepts and tools to reason about actions and their consequences.
  arXiv  Detail & Related papers  (2021-02-10T03:42:03Z)
• Deep Learning for Human Mobility: a Survey on Data and Models [5.9623431392389]
  The study of human mobility is crucial due to its impact on several aspects of our society. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, triggered the application of deep learning to human mobility. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, and trajectory generation.
  arXiv  Detail & Related papers  (2020-12-04T19:59:39Z)
• Event-Related Bias Removal for Real-time Disaster Events [67.2965372987723]
  Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. We train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
  arXiv  Detail & Related papers  (2020-11-02T02:03:07Z)
• Event Prediction in the Big Data Era: A Systematic Survey [7.3810864598379755]
  Event prediction is becoming a viable option in the big data era. This paper aims to provide a systematic and comprehensive survey of the technologies, applications, and evaluations of event prediction.
  arXiv  Detail & Related papers  (2020-07-19T23:24:52Z)
• Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements [55.33496599723126]
  disinformation including fake news has become a global phenomenon due to its explosive growth. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation.
  arXiv  Detail & Related papers  (2020-01-02T21:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.