Improving Disaster Response With Advanced Machine Learning To Analyse Social Media Content

Diplôme Thèse Improving Disaster Response With Advanced Machine Learning To Analyse Social Media Content eng 2025-01-14 2025-01-29 author Firmansyah Hafiz Budi 941271 director Di Marzo Serugendo Giovanna 0000-0001-5048-5251 28107 Service enseignement, recherche, administratif et technique (SdS) (SCSPS) director Fernandez Marquez Jose Luis 0000-0002-0254-0794 750231 Centre universitaire d'informatique (CUINF) 153 10.13097/archive-ouverte/unige:183585 urn:nbn:ch:unige-1835852 SdS 278 Over the last decade, disasters have impacted more than 1.5 billion people worldwide, causing more than half a million deaths and resulting in major economic losses for the affected areas. The most impacted are those residing in the least developed countries and small island developing states. Accessing relevant information in a timely manner is key for providing an accurate and efficient response to these situations. Traditionally, satellite imagery has been a common approach, yet analyzing these images is both costly and time-consuming. In contrast, recent advancements indicate that social media plays a pivotal role in delivering timely information. On these platforms, users share critical updates about disasters, including the extent of damage to infrastructure, severity of the damage, and the condition of victims. Despite its potential, the utility of social media data in operational contexts is limited by challenges such as filtering irrelevant information and the lack of location in the content. This thesis addresses the problem to get relevant information and retrieve location from social media content. It investigates the role of advanced machine learning in two directions. Firstly, it seeks to enhance information classification by applying sophisticated machine learning techniques to improve the accuracy of categorizing social media data into vital response categories, such as information relevance, damage severity, and urgent needs. Secondly, it involves predicting the geolocation of social media content to aid disaster response. This thesis employs advanced machine learning algorithms to infer the geographical origins of social media posts lacking explicit location data, aiming to accurately identify areas in need during disaster situations, thereby optimizing response efforts. This problem-driven research leverages and analyzes real-world datasets from social media platforms. Motivated by real-world problems, the thesis is conducted in collaboration with various practitioners and institutions, including the Joint Research Center of the European Commission, Digital Humanitarian Organization VOST Portugal, Artificial Intelligence Research Center CSIC Spain, and Politecnico di Milano, Italy. As a contribution to the field, this thesis offers an approach, design, and implementation of advanced machine learning to analyse social media data. The results are expected to assist policymakers in understanding the technical approach to applying social media analysis to enhance disaster response. Dewey 300 Dewey 025.063 10 Faculté des sciences de la société T 13 Centre universitaire d'informatique 0D3 false 929 Institute of Information Service Science (ISS) 62bcdf90-f409-4040-8113-cc4d12dcb0bd Thesis Public 21_02_2025_06_00_PhD_manuscript___H.pdf 23464295 application/pdf 9f833102d5a6ede13e99f89c164c72491c6fb8c6e4f5ac41b45453f39a44f402 ef7de4f1-570b-4aa0-a721-742368a30185 Imprimatur Public Imprimatur.pdf 56630 application/pdf 4b867a15d1eea4ad99038da06ec9a22301cc59e8a266fac8a092fa0b7dd63d69 openAccess Faculté des sciences de la société Centres et instituts / Centre universitaire d'informatique

Title	Published in	Year	Views	Downloads

Improving Disaster Response With Advanced Machine Learning To Analyse Social Media Content		2025	408	599
Artificial Intelligence Approach To Improve Social Media Images Classification For Crisis Response	UNIGE Data Science Day 2022	2022	192	0

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