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Found 7 papers, 2 papers with code
A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence
This paper presents an online system that leverages social media data in real time to identify landslide-related information automatically using state-of-the-art artificial intelligence techniques.
TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels
The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports.
Landslide Detection in Real-Time Social Media Image Streams
Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly.
HumAID: Human-Annotated Disaster Incidents Data from Twitter with Deep Learning Benchmarks
Social networks are widely used for information consumption and dissemination, especially during time-critical events such as natural disasters.
Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response
In this study, we propose new datasets for disaster type detection, and informativeness classification, and damage severity assessment.
GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information
The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters.
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence
Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings.
