Cristian Cosentino
Biographic Data
| ID | 10055111 |
|---|---|
| NAME | Cristian Cosentino |
| GIVEN NAMES | Cristian |
| FAMILY NAME | Cosentino |
| SIGNATURE | COSENTINO C |
| AFFILIATIONS | University of Calabria |
| ORCID | 0000-0002-6368-373X |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Harnessing prompt-based large language models for disaster monitoring and automated reporting from social media feedback
In recent years, social media has emerged as one of the main platforms for real-time reporting of issues during disasters and catastrophic events. While great strides have been made in collecting such information, there remains an urgent need to improve user reports’ automation, aggregation, and organization to streamline various tasks, including rescue operations, resource allocation , and communication with the press. This paper introduces an i…
Multistakeholder Disaster Insights From Social Media Using Large Language Models
In recent years, social media has emerged as a primary channel for users to promptly share feedback and issues during disasters and emergencies, playing a key role in crisis management. While significant progress has been made in collecting and analyzing social media content, there remains a pressing need to enhance the automation, aggregation, and customization of this data to deliver actionable insights tailored to diverse stakeholders, includi…
No prominent works on this page.
Harnessing prompt-based large language models for disaster monitoring and automated reporting from social media feedback
In recent years, social media has emerged as one of the main platforms for real-time reporting of issues during disasters and catastrophic events. While great strides have been made in collecting such information, there remains an urgent need to improve user reports’ automation, aggregation, and organization to streamline various tasks, including rescue operations, resource allocation , and communication with the press. This paper introduces an i…
Multistakeholder Disaster Insights From Social Media Using Large Language Models
In recent years, social media has emerged as a primary channel for users to promptly share feedback and issues during disasters and emergencies, playing a key role in crisis management. While significant progress has been made in collecting and analyzing social media content, there remains a pressing need to enhance the automation, aggregation, and customization of this data to deliver actionable insights tailored to diverse stakeholders, includi…
Public Relations and Crisis Communication (2 works) · Social media (2 works) · Computer Science (1 work) · Computer security (1 work) · Data science (1 work) · Generative grammar (1 work) · Generative model (1 work) · Mental Health via Writing (1 work) · Misinformation and Its Impacts (1 work) · Personalization (1 work)