Back

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

Chire Saire, J. E.; Oblitas Cruz, J. F.

2020-08-18 public and global health
10.1101/2020.08.15.20175810 medRxiv
Show 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.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
PLOS ONE
● 5266 papers in training set
Top 13%
13.2%
Similar papers in this journal
2
JMIRx Med
◐ 32 papers in training set
Top 0.1%
11.3%
Similar papers in this journal
3
International Journal of Environmental Research and Public Health
○ 128 papers in training set
Top 0.7%
5.6%
Similar papers in this journal
4
Scientific Reports
○ 3612 papers in training set
Top 14%
5.6%
Similar papers in this journal
5
Frontiers in Public Health
○ 148 papers in training set
Top 1%
4.4%
Similar papers in this journal
6
JMIR Formative Research
◐ 33 papers in training set
Top 0.4%
3.5%
7
PeerJ
◐ 308 papers in training set
Top 3%
2.7%
8
Journal of Public Health
◐ 24 papers in training set
Top 0.2%
2.7%
9
Cureus
○ 68 papers in training set
Top 2%
2.7%
50% of probability mass above
10
Disaster Medicine and Public Health Preparedness
◐ 16 papers in training set
Top 0.2%
2.2%
11
JMIR Public Health and Surveillance
◐ 45 papers in training set
Top 0.4%
2.2%
12
F1000Research
○ 88 papers in training set
Top 0.9%
2.2%
13
Heliyon
○ 152 papers in training set
Top 2%
2.2%
14
Epidemiology and Infection
◐ 89 papers in training set
Top 1%
1.8%
15
Biomedical Signal Processing and Control
○ 22 papers in training set
Top 0.3%
1.8%
16
Journal of Medical Internet Research
◐ 87 papers in training set
Top 2%
1.5%
17
Virology Journal
○ 32 papers in training set
Top 0.3%
1.5%
18
Sustainability
○ 10 papers in training set
Top 0.2%
1.4%
19
Risk Management and Healthcare Policy
○ 10 papers in training set
Top 0.3%
1.2%
20
PLOS Digital Health
● 106 papers in training set
Top 3%
1.2%
21
Public Health
○ 36 papers in training set
Top 0.6%
1.1%
22
Chaos, Solitons & Fractals
○ 32 papers in training set
Top 0.7%
1.0%
23
BioMed Research International
○ 28 papers in training set
Top 2%
1.0%
24
BMC Infectious Diseases
○ 133 papers in training set
Top 4%
1.0%
25
BMC Bioinformatics
○ 457 papers in training set
Top 5%
1.0%
26
Virus Research
○ 37 papers in training set
Top 0.6%
1.0%
27
Frontiers in Medicine
○ 120 papers in training set
Top 3%
0.9%
28
Annals of Translational Medicine
○ 18 papers in training set
Top 0.4%
0.9%
29
IEEE Access
● 35 papers in training set
Top 1%
0.9%
30
Life
○ 29 papers in training set
Top 0.8%
0.9%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.