Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach

Josimar Edinson Chire Saire; Jimy Frank Oblitas Cruz

doi:10.1109/ICS51289.2020.00056

Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach

Josimar Edinson Chire Saire
, Jimy Frank Oblitas Cruz

UPN Directorate of Research, Innovation, and Sustainability

Universidade de São Paulo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

The fast spreading of coronavirus name covid19, generated the actual pandemic forcing to change daily activities. Health Councils of each country promote health policies, close borders and start a partial or total lockdown. One of the first countries in Europe with high impact was Italy. Besides at the end of April, one country with a shared border was on the top of 10 countries with more total cases, then France started with its own battle to beat coronavirus. This paper studies the impact of coronavirus in the poopulation of Paris, France from April 23 to June 18, using Text Mining approach, processing data collected from Social Network and using trends related of searching. First finding is a decreasing pattern of publications/interest, and second is related to health crisis and economical impact generated by coronavirus.

Original language	English
Title of host publication	Proceedings - 2020 International Computer Symposium, ICS 2020
Pages	242-246
Number of pages	5
ISBN (Electronic)	9781728192550
DOIs	https://doi.org/10.1109/ICS51289.2020.00056
State	Published - Dec 2020
Event	2020 International Computer Symposium, ICS 2020 - Tainan, Taiwan, Province of China Duration: 17 Dec 2020 → 19 Dec 2020

Publication series

Name	Proceedings - 2020 International Computer Symposium, ICS 2020

Conference

Conference	2020 International Computer Symposium, ICS 2020
Country/Territory	Taiwan, Province of China
City	Tainan
Period	17/12/20 → 19/12/20

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Coronavirus
Covid-19
Data mining
Data science
Europe
France
Infodemiology
Infoveillance
Natural Language Processing
Pandemic
Paris
People behaviour
Public Health
Sars-cov2
Social Networks
Text Mining
Twitter

Access to Document

10.1109/ICS51289.2020.00056

Cite this

@inproceedings{0b719da81b82460b98ef30f23491a613,

title = "Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach",

abstract = "The fast spreading of coronavirus name covid19, generated the actual pandemic forcing to change daily activities. Health Councils of each country promote health policies, close borders and start a partial or total lockdown. One of the first countries in Europe with high impact was Italy. Besides at the end of April, one country with a shared border was on the top of 10 countries with more total cases, then France started with its own battle to beat coronavirus. This paper studies the impact of coronavirus in the poopulation of Paris, France from April 23 to June 18, using Text Mining approach, processing data collected from Social Network and using trends related of searching. First finding is a decreasing pattern of publications/interest, and second is related to health crisis and economical impact generated by coronavirus.",

keywords = "Coronavirus, Covid-19, Data mining, Data science, Europe, France, Infodemiology, Infoveillance, Natural Language Processing, Pandemic, Paris, People behaviour, Public Health, Sars-cov2, Social Networks, Text Mining, Twitter",

author = "\{Chire Saire\}, \{Josimar Edinson\} and \{Oblitas Cruz\}, \{Jimy Frank\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 International Computer Symposium, ICS 2020 ; Conference date: 17-12-2020 Through 19-12-2020",

year = "2020",

month = dec,

doi = "10.1109/ICS51289.2020.00056",

language = "English",

series = "Proceedings - 2020 International Computer Symposium, ICS 2020",

pages = "242--246",

booktitle = "Proceedings - 2020 International Computer Symposium, ICS 2020",

}

Chire Saire, JE & Oblitas Cruz, JF 2020, Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach. in Proceedings - 2020 International Computer Symposium, ICS 2020., 9359027, Proceedings - 2020 International Computer Symposium, ICS 2020, pp. 242-246, 2020 International Computer Symposium, ICS 2020, Tainan, Taiwan, Province of China, 17/12/20. https://doi.org/10.1109/ICS51289.2020.00056

Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach. / Chire Saire, Josimar Edinson; Oblitas Cruz, Jimy Frank.
Proceedings - 2020 International Computer Symposium, ICS 2020. 2020. p. 242-246 9359027 (Proceedings - 2020 International Computer Symposium, ICS 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach

AU - Chire Saire, Josimar Edinson

AU - Oblitas Cruz, Jimy Frank

PY - 2020/12

Y1 - 2020/12

N2 - The fast spreading of coronavirus name covid19, generated the actual pandemic forcing to change daily activities. Health Councils of each country promote health policies, close borders and start a partial or total lockdown. One of the first countries in Europe with high impact was Italy. Besides at the end of April, one country with a shared border was on the top of 10 countries with more total cases, then France started with its own battle to beat coronavirus. This paper studies the impact of coronavirus in the poopulation of Paris, France from April 23 to June 18, using Text Mining approach, processing data collected from Social Network and using trends related of searching. First finding is a decreasing pattern of publications/interest, and second is related to health crisis and economical impact generated by coronavirus.

AB - The fast spreading of coronavirus name covid19, generated the actual pandemic forcing to change daily activities. Health Councils of each country promote health policies, close borders and start a partial or total lockdown. One of the first countries in Europe with high impact was Italy. Besides at the end of April, one country with a shared border was on the top of 10 countries with more total cases, then France started with its own battle to beat coronavirus. This paper studies the impact of coronavirus in the poopulation of Paris, France from April 23 to June 18, using Text Mining approach, processing data collected from Social Network and using trends related of searching. First finding is a decreasing pattern of publications/interest, and second is related to health crisis and economical impact generated by coronavirus.

KW - Coronavirus

KW - Covid-19

KW - Data mining

KW - Data science

KW - Europe

KW - France

KW - Infodemiology

KW - Infoveillance

KW - Natural Language Processing

KW - Pandemic

KW - Paris

KW - People behaviour

KW - Public Health

KW - Sars-cov2

KW - Social Networks

KW - Text Mining

KW - Twitter

UR - https://www.scopus.com/pages/publications/85102193520

U2 - 10.1109/ICS51289.2020.00056

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M3 - Conference contribution

AN - SCOPUS:85102193520

T3 - Proceedings - 2020 International Computer Symposium, ICS 2020

SP - 242

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BT - Proceedings - 2020 International Computer Symposium, ICS 2020

T2 - 2020 International Computer Symposium, ICS 2020

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ER -

Study of Coronavirus Impact on Parisian Population from April to June using Twitter and Text Mining Approach

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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