Related papers: Social Media Attributions in the Context of Water …
Attribution of responsibility and blame are important topics in political science especially as individuals tend to think of political issues in terms of questions of responsibility, and as blame carries far more weight in voting behavior…
Social media has become an essential channel for posting disaster-related information, which provide governments and relief agencies real-time data for better disaster management. However, research in this field has not received sufficient…
Massive and diverse web data are increasingly vital for government disaster response, as demonstrated by the 2022 floods in New South Wales (NSW), Australia. This study examines how X (formerly Twitter) and public inquiry submissions…
Lack of global data inventories obstructs scientific modeling of and response to landslide hazards which are oftentimes deadly and costly. To remedy this limitation, new approaches suggest solutions based on citizen science that requires…
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…
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to coarsely distributed sensors or sensor failures. At the same time, a plethora of information is buried in an abundance of images of…
This paper analyses social media data in multiple disaster-related collections of floods and heat waves in the UK. The proposed method uses machine learning classifiers based on deep bidirectional neural networks trained on benchmark…
Social media are more than just a one-way communication channel. Data can be collected, analyzed and contextualized to support disaster risk management. However, disaster management agencies typically use such added-value information to…
The analysis of natural disasters such as floods in a timely manner often suffers from limited data due to a coarse distribution of sensors or sensor failures. This limitation could be alleviated by leveraging information contained in…
Traditional disaster analysis and modelling tools for assessing the severity of a disaster are predictive in nature. Based on the past observational data, these tools prescribe how the current input state (e.g., environmental conditions,…
People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making…
Floods are the most common and among the most severe natural disasters in many countries around the world. As global warming continues to exacerbate sea level rise and extreme weather, governmental authorities and environmental agencies are…
Online social microblogging platforms including Twitter are increasingly used for aiding relief operations during disaster events. During most of the calamities that can be natural disasters or even armed attacks, non-governmental…
Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone…
Social media analysis of disaster events is a critical task in crisis informatics research. It involves analyzing social media data generated during natural disasters, crisis events, or other mass convergence events. Due to the large data…
Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post useful information on what is actually happening. Given the large amounts of posts, a…
Mumbai, a densely populated city, experiences frequent extreme rainfall events leading to floods and waterlogging. However, the lack of real-time flood monitoring and detailed past flooding data limits the scientific analysis to extreme…
AI data-driven models (Graphcast, Pangu Weather, Fourcastnet, and SFNO) are explored for storyline-based climate attribution due to their short inference times, which can accelerate the number of events studied, and provide real time…
In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive…
The role of social media, in particular microblogging platforms such as Twitter, as a conduit for actionable and tactical information during disasters is increasingly acknowledged. However, time-critical analysis of big crisis data on…