Socially Enhanced Situation Awareness from Microblogs using Artificial Intelligence: A Survey
arXiv:2209.07272 · doi:10.1145/3524498
Abstract
The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the "big picture"--the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime, Disasters, Finance, Physical Environment, Politics, and Health and Population. We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.
Accepted to ACM Computing Surveys (CSUR) 2022
References in corpus (8)
- How transferable are features in deep neural networks?
- CoAtNet: Marrying Convolution and Attention for All Data Sizes
- Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
- Improving Crime Count Forecasts Using Twitter and Taxi Data
- "Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study
- LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention
- Keyphrase Extraction from Disaster-related Tweets
- Better Fine-Tuning by Reducing Representational Collapse