A data-driven global observatory addressing worldwide challenges through text mining and complex data visualisation

Joao Costa; M. Besher Massri; Marko Grobelnik; Ignacio Casals del Busto; Dale Weston

doi:10.12688/openreseurope.14471.2

A data-driven global observatory addressing worldwide challenges through text mining and complex data visualisation

Joao Costa^*
, M. Besher Massri
, Marko Grobelnik
, Ignacio Casals del Busto
, Dale Weston

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Observing the world on a global scale can help us understand better the context of problems that engage us all. In this paper, we propose a data-driven global observatory methodology that puts together the different perspectives of media, science, statistics and sensing over heterogeneous data sources and text mining algorithms. We also discuss the implementation of this global observatory in the context of epidemic intelligence, monitoring the impact of the COVID-19 pandemic, and in the context of climate change, with a specific focus on water resource management. Moreover, we discuss the value of this global solution in local contexts and priorities, based on the exchange with stakeholders in municipalities, utilities and governmental institutions.

Original language	English
Article number	68
Journal	Open Research Europe
Volume	2
DOIs	https://doi.org/10.12688/openreseurope.14471.2
Publication status	Published - 2023

Bibliographical note

Publisher Copyright:
Copyright: © 2023 Costa J et al.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 13 Climate Action

Keywords

Big Data
MEDLINE
MeSH Headings
Public Health
Semantic Technologies
Social media
Text Mining
Water Management
multilingual news

Access to Document

10.12688/openreseurope.14471.2

Cite this

@article{e3e5b40a41cf414c92206a46b13ce3da,

title = "A data-driven global observatory addressing worldwide challenges through text mining and complex data visualisation",

abstract = "Observing the world on a global scale can help us understand better the context of problems that engage us all. In this paper, we propose a data-driven global observatory methodology that puts together the different perspectives of media, science, statistics and sensing over heterogeneous data sources and text mining algorithms. We also discuss the implementation of this global observatory in the context of epidemic intelligence, monitoring the impact of the COVID-19 pandemic, and in the context of climate change, with a specific focus on water resource management. Moreover, we discuss the value of this global solution in local contexts and priorities, based on the exchange with stakeholders in municipalities, utilities and governmental institutions.",

keywords = "Big Data, MEDLINE, MeSH Headings, Public Health, Semantic Technologies, Social media, Text Mining, Water Management, multilingual news",

author = "Joao Costa and Massri, \{M. Besher\} and Marko Grobelnik and \{Casals del Busto\}, Ignacio and Dale Weston",

note = "Publisher Copyright: Copyright: {\textcopyright} 2023 Costa J et al.",

year = "2023",

doi = "10.12688/openreseurope.14471.2",

language = "English",

volume = "2",

journal = "Open Research Europe",

issn = "2732-5121",

publisher = "F1000 Research Ltd",

}

TY - JOUR

T1 - A data-driven global observatory addressing worldwide challenges through text mining and complex data visualisation

AU - Costa, Joao

AU - Massri, M. Besher

AU - Grobelnik, Marko

AU - Casals del Busto, Ignacio

AU - Weston, Dale

PY - 2023

Y1 - 2023

N2 - Observing the world on a global scale can help us understand better the context of problems that engage us all. In this paper, we propose a data-driven global observatory methodology that puts together the different perspectives of media, science, statistics and sensing over heterogeneous data sources and text mining algorithms. We also discuss the implementation of this global observatory in the context of epidemic intelligence, monitoring the impact of the COVID-19 pandemic, and in the context of climate change, with a specific focus on water resource management. Moreover, we discuss the value of this global solution in local contexts and priorities, based on the exchange with stakeholders in municipalities, utilities and governmental institutions.

AB - Observing the world on a global scale can help us understand better the context of problems that engage us all. In this paper, we propose a data-driven global observatory methodology that puts together the different perspectives of media, science, statistics and sensing over heterogeneous data sources and text mining algorithms. We also discuss the implementation of this global observatory in the context of epidemic intelligence, monitoring the impact of the COVID-19 pandemic, and in the context of climate change, with a specific focus on water resource management. Moreover, we discuss the value of this global solution in local contexts and priorities, based on the exchange with stakeholders in municipalities, utilities and governmental institutions.

KW - Big Data

KW - MEDLINE

KW - MeSH Headings

KW - Public Health

KW - Semantic Technologies

KW - Social media

KW - Text Mining

KW - Water Management

KW - multilingual news

UR - https://www.scopus.com/pages/publications/85202725524

U2 - 10.12688/openreseurope.14471.2

DO - 10.12688/openreseurope.14471.2

M3 - Article

AN - SCOPUS:85202725524

SN - 2732-5121

VL - 2

JO - Open Research Europe

JF - Open Research Europe

M1 - 68

ER -

A data-driven global observatory addressing worldwide challenges through text mining and complex data visualisation

Abstract

Bibliographical note

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this