Slides for EO Open Science

1. 1 Dept. of Systems Engineering & Computer Science, Universidad del Norte, Colombia
2 Dept. of Civil and Environmental Engineering, Politecnico di Milano, Como Campus, Italy
3 Transport and ICT Global Practice, The World Bank
User Generated Data During Extreme
Events
Mayra Zurbarán1, Maria A. Brovelli2, Danilo Ardagna2,
Mattia Manara2, Mark Iliffe3

2.  Social media APIs allow access to rich sources of user generated content
 This serves for manyfold applications, specially when it is geo-referenced
data, e.g.:
 Spotting popular locations
 Different activities within a city
 Relevant local news
 Sentiment analysis
 Monitoring extreme events
 This was done in Italy using the Twitter Streaming API for the following
events:
 Monitor precipitation in the country
 Earthquake detection
 Script openly available at: https://github.com/mazucci/geocollect

3.  The Twitter Search API:
• Size limited response per
request
• Needs to reconnect on every
query
• Restricted requests over
time; limited at 180 queries
per 15 min window
 Available at:
https://dev.twitter.com/rest/public/
search
 The Twitter Streaming API:
• Needs only one request and the
connection remains
• Low latency access up to an
estimated 1% of all twitter data
• Parametrized queries get higher
percentage of the full response
• Optimized traffic due to less
connection attempts
 Available at:
https://dev.twitter.com/streaming/over
view
 For Italy around 30,000 geo-referenced tweets are collected daily
through the Streaming API

4. The Twitter Search API: The Twitter Streaming API:

5. The web framework for
perfectionists with
deadlines
&
Streaming response
&
Retrieves Saves
RequestCSV to process
DatabaseApplication

6. EARTHQUAKE IN CENTRAL ITALY
Case Study:

7. Hashtag Appered First At Number of Tweets
#Italy 03:40 59
#terremoto 03:40 287
#earthquake 03:40 149
#sismo 03:41 75
#Roma 03:41 5
#terremotoRoma 03:41 2
#quake 03:56 3
#Norcia 04:09 5
#terremotoItalia 8:38 7
First reported tweet after the earthquake at aprox. 250 km from the
epicentre:
2 minutes after the official
time of major earthquake
(Mag: 6,0) at 3:36 AM
Popular Hashtags

8. 25
99
44
12
18
82
49
14
0
20
40
60
80
100
120
140
160
180
200
Asking help Comunicating the earthquake Describing fellings, deaths, injuries
and damage
Offering help
# terremoto #earthquake

9. A total of 498 tweets were related to the earthquake on Aug 24th
0
10
20
30
40
50
60
70
80
90
Roma
Perugia
Rieti
Ragusa
Savona
AscoliPiceno
Palermo
Fermo
Ancona
Napoli
Teramo
Chieti
Macerata
Milano
Firenze
Torino
Modena
Salerno
Pescara
Cosenza
Rimini
Genova
Forli'-Cesena
Pistoia
Terni
Viterbo
Venezia
Tweet Count by Provinces (IT)
Count

10. 0
5
10
15
20
25
30
35
40
45
50
salvobracchitta
everyEarthquake
TgrMarche
amorlupi
USGSted
ivanamilcar
cinziaspoletini
PikkolaMartyy90
alepuntato
gio4video
tiakatty
ciriolo555
jdpromotions
mariposa_k91
grabmyhandsbykv
MarinaDeMattei1
francesco_music
Nikielin
AbbateCaterina
PizzaPreacher
BibiBeti
niclapiepoli
Me_Marina
stefasorre
kikkadimauro
FiorellaEats
ItchyTeethUK
passeritina
flaviabrc
SilviaBiba85
CarmeloGraceff2
Luciana57923454
Jacksoniana094
GianlucaTeseo1
maurosalza
FatCatAnna
walshyfire
Fidas_Cadit
austenliam
carlossalazarac
ilario82
past_roby
FraMarabese
Nottesenzaluna1
fabiomarelli
Manilanazzaro
deej46
georgianwineman
mariaielpo504
Count of Tweets by Users
tweets
Most users tweet less than 3 tweets related to the earthquake on Aug 24th

11. From 00 AM to 10 AM

12. *Values calculated using the Kernell formula by Silverman (1986, p. 76, equation 4.5)
Data: A total of 499
earthquakes reported
from Aug 17th until Aug
24th of 2016.
Source: INGV -
http://www.ingv.it/it/

14. PRECIPITATIONS IN ITALY
Case Study:

15. Keywords to include while processing:
Temporale, acquazzone, diluvio, alluvione

16.  During extreme events, in our case study of earthquakes in central
Italy:
 Users tweet more when they are near the epicentre but on non
critical areas
 Population density impacts the content generation: There are
more tweets from big cities
 Aggregating user-generated content aids in understanding user’s
perceptions during extreme events e.g. EMSC - http://www.emsc-
csem.org
 The precipitations study shows that are more tweets regarding rain
near the Alps Area where it rains more during summer time
according to official reports
 For Future Work it is interesting to supervise the behavior during
different seasons

17. ANY QUESTIONS?
Thank you!