Related papers: Leveraging Social Media Data and Artificial Intell…
Natural disasters can have catastrophic consequences, a poignant example is the series of $7.7$ and $7.6$ magnitude earthquakes that devastated T\"urkiye on February 6, 2023. To limit the damage, it is essential to maintain the…
Critical infrastructure, such as transport networks, underpins economic growth by enabling mobility and trade. However, ageing assets, climate change impacts (e.g., extreme weather, rising sea levels), and hybrid threats ranging from…
Individuals who shared actionable information during Hurricane Sandy were significantly more likely to exhibit risk-prone behavior, as measured by a novel Risk Behavior Quotient (RBQ). Using a dataset of 36595 geo-located tweets from 774…
Climate change is increasing the frequency and severity of natural disasters worldwide. Media coverage of these events may be vital to generate empathy and mobilize global populations to address the common threat posed by climate change.…
The escalating frequency and severity of disasters routinely overwhelm traditional response capabilities, exposing critical vulnerability in disaster management. Current practices are hindered by fragmented data streams, siloed…
We study how emojis are used to express solidarity in social media in the context of two major crisis events - a natural disaster, Hurricane Irma in 2017 and terrorist attacks that occurred on November 2015 in Paris. Using annotated…
In this paper, we explore the formation of network relationships among disaster relief agencies during the process of responding to an unexpected event. The relationship is investigated through variables derived from the policy network…
Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among millions of posts being posted every day can be difficult, and developing a data…
In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different…
"Social sensing" is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case…
During times of crisis, social media platforms play a crucial role in facilitating communication and coordinating resources. In the midst of chaos and uncertainty, communities often rely on these platforms to share urgent pleas for help,…
Many people use social media to seek information during disasters while lacking access to traditional information sources. In this study, we analyze Twitter data to understand information spreading activities of social media users during…
This paper examines collaborative sensemaking during severe weather events through the emerging phenomenon of "weatherfluencers" or content creators who livestream meteorological interpretation on platforms like YouTube. Drawing from…
Disasters impact communities through interconnected social, spatial, and physical networks. Analyzing network dynamics is crucial for understanding resilience and recovery. We highlight six studies demonstrating how hazards and recovery…
During natural disasters, people often use social media platforms such as Twitter to ask for help, to provide information about the disaster situation, or to express contempt about the unfolding event or public policies and guidelines. This…
Rapid post-earthquake damage assessment is crucial for rescue and resource planning. Still, existing remote sensing methods depend on costly aerial images, expert labeling, and produce only binary damage maps for early-stage evaluation.…
We use wireless voice-call and text-message volumes to quantify spatiotemporal communication patterns in the New York Metro area before, during, and after the Virginia earthquake and Hurricane Irene in 2011. The earthquake produces an…
The emergence of data-driven weather forecast models provides great promise for producing faster, computationally cheaper weather forecasts, compared to physics-based numerical models. However, while the performance of artificial…
The recent evolution of induced seismicity in Central United States calls for exhaustive catalogs to improve seismic hazard assessment. Over the last decades, the volume of seismic data has increased exponentially, creating a need for…
Frequent occurrences of extreme weather events substantially impact the lives of the less privileged in our societies, particularly in agriculture-inclined economies. The unpredictability of extreme fires, floods, drought, cyclones, and…