The document discusses using social media data from Twitter during extreme events. It analyzes tweets related to earthquakes in central Italy and precipitation in Italy. For earthquakes, it finds that users tweet more when near the epicenter but in non-critical areas, and that population density impacts tweet volume with more from big cities. Analyzing such user-generated content can aid understanding perceptions during disasters. For precipitation, it finds more tweets about rain near the Alps where official reports say rain is heavier in summer. Future work could compare behavior across seasons.

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!