Background Identifying healthcare services and also strengthening the healthcare systems
to effectively deliver them in the aftermath of large-scale disasters like the 2023 Turkey-Syria …

The exponential growth of digital data in recent years has spurred a significant interest in
natural language processing (NLP) and sentiment analysis. However, the effectiveness of …

This study examined the impact of Twitter usage development in crisis communication in
Türkiye during the 6 February 2023 by showing the development of its use during the 1999 …

In this paper, we propose SRL4NLP, a new approach for data augmentation by drawing an
analogy between image and text processing: Super-resolution learning. This method is …

Natural disasters have dire consequences for communities, leading to loss of life, property
destruction and environmental devastation. Effective disaster response necessitates prompt …

In the realm of disaster-related tweet classification, this study presents a comprehensive
analysis of various machine learning algorithms, shedding light on crucial factors influencing …

Le traitement de données provenant de réseaux sociaux en temps réel est devenu une outil
attractifdans les situations d'urgence, mais la surcharge d'informations reste un défi à …

The lack of trust is one of the most frequently addressed research gaps that hinders the
practical deployment of social media information into decision-making process of disaster …

The detection of hate speech is a subject extensively explored by researchers, and machine
learning algorithms play a crucial role in this domain. The existing resources mostly focus on …

Earthquakes are among the most inevitable natural disasters our country faces. Throughout
history, this natural phenomenon has caused significant losses multiple times, making the …