Smerst2013 crisci warwick Mapping impacts of severe waether events troughout social media semantized streams.

1. Social Media and Social Media and Semantic Technologies
in Emergency Response
severe weather events:
University of Warwick Coventry
mapping the footprint
15-16 April 2013
Alfonso Crisci - a.crisci@ibimet.cnr.it
Valentina Grasso - grasso@lamma.rete.toscana.it

2. Social Media
Weather

3. Weather severe events
as emergency issue in spacetime imply a WWW
Who
When
Where
…..as a reality Web

4. Towards resilient communities means to be ctizen
aware &
prepared

5. Changing climate means changing awareness
Imply the reframing in:
Prepardness & Response
Geographical spreading and
magnitude of events
are important
for awareness

6. Social media and SEO are the
information web rivers available.
Are they useful or not?
That is the question ( W. Shakespeare).

7. A question of time event shape
weather phenomena and
peak social/communication streams
as "analogue" time delayed
information waves
start decline
time

8. …..and geography

9. Local dynamic type warping
means to be explore the
Time coherence between
real physical process
[ or its mathematical representation!!!!]
& information flows

10. In a multidimesional space or
better in every time-varying
systems ( as the atmosphere or
as the “WEB information seas” )
some structures ever could be
detected.
Lagrangian coherent structures (LCS)
well known
in ecology
Uncovering the Lagrangian Skeleton of Turbulence and fluid dynamics
Marthur et al.
Phys Rev Lett. 2007 Apr 6;98(14):144502.
Epub 2007 Apr 4.
When two or more time-varying systems
are connected a supercoherence could be
detected if processes are linked.

11. The link structure
between SM and weather
could be done
hypothetically by a
opportune Hierarchy
model (Theory of middle-
number systems
Weinberg 1975).
Social media and weather
relationships are surely
an Organized Complexity.
Many parts to be
deterministically
predicted, too few to be
statistically forecasted.
Agent-Based Modeling of Complex Spatial
Systems
http://www.ncgia.ucsb.edu/projects/abmcss/
May Yuan, University of Oklahoma

12. To overcome this kind of complexities
a 5-point :
road map
• Identify a 1-dimensional time flux of information from SM’s
world
• Detection of every local statistical linear association of this one
in a parametric –physical- spacetime representation ( time
spatial grid of data).
• Mapping the significance in classes previously determined.
• Pattern verification with observations.
• Semantics and textual mining confirms.

13. Heat wave as a good case
severe weather event
Emergency as consequence of "behaviour“.
Awareness is linked to “perception”.

14. Weather event: early heat wave on 5-7 April 2011
Research objectives
• investigate time/space
coherence between the
event extension and its
social footprint on Twitter
• semantic analysis of
Twitter stream on/off
peaks days

15. Severe weather definition
Heat wave: it's a period with persistent T°
above the seasonal mean. Local definition
depends by regional climatic context.
Severe weather
refers to any dangerous
meteorological phenomena
with the potential to cause
damage, serious social
disruption, or loss of
human life.[WMO]
Types of severe weather
phenomena vary,
depending on the latitude,
altitude, topography, and
atmospheric conditions.
Ref:
http://en.wikipedia.org/wiki/Severe_weather

16. Target and Products
Consorzio LaMMA - CNR Ibimet developed a methodology and a set of
products to quantitative evaluate the social impact of weather related
events.
Products: Stakeholders:
• DNKT metric • forecasters
• association of the time • institutional stakeholders
vector (DNKT) and a time • EM communities
coupled gridded data stack
• media agents
• spatial associative map
• semantic analysis Twitter Target
stream:
Detect areas where it's worth
- clustering
focusing attention, also for
- word clouds communication purpose.

17. Data used
Heat wave period considered (7-13 April 2011)
Social
- Using Twitter API key-tagged (CALDO-AFA-SETE)
6069 tweets collected through geosearch
service for italian area.
- Retweets and replies included (full volume stream)
Climate & Weather (7-10 April 2011)
- Urban daily maximum T°
- Daily gridded data (lon 5-20 W lat 35-50)
WRF-ARW model T°max daily data (box 9km)

18. Twitter metric
DNKT - "daily number of key-tagged tweets"
*
*
*
DNKT shows time coherence with daily profiles of areal averaged temperature
*Critical days identified as numerical neighbour of peaks (7-8-9-April):
social "heaty days"

19. Geographic associative maps
Semantic based social stream in
1D * time space (DNKT)
Weather informative
layers in 2D time* space
Linear
Association
Statistically Geographic
based Associative
Verifier Map
by pixel (2D space)

20. Impacted areas
This is not a Twitter map
It's a weather map at
X-rays:
Twitter stream
is used as a
"contrast medium"
to visualize impacted
areas.

21. Associative maps patterns fits
Urban maximum T°
over 28 C° on 9 April
where & when

22. Semantic analysis
- Corpus creation
DNKT classification by heat-wave peak days:
heat days ( 7-8-9 April) no-heat days (6-10-11
April).
- Terms Word Clouds (min wd frequency>30)
heat days vs no-heat days
Clustering associated terms
Term frequency ranking comparison
heat days
- Hashtag Word Clouds
heat days vs no-heat days
R Stat 15.2 Packages used:
tm (Feinerer and Hornik, 2012) & wordcloud (Fellows , 2012)

23. terms WordClouds (excluded key-tag caldo-afa-
sete)
heat days
no-heat days

24. Terms association clustering
heat days no heat days
"heat" is THE conversation topic "heat" is marginal to the conversation topic

25. heat days

26. Terms frequency ranking
no heat N=2608 heat N=3461
oggi 6.0% oggi 8.3% 1°
sole 5.5% troppo 7.7% 2°
troppo 4.1% sole 5.9% 3°

27. Hashtags WordClouds
heat days
no-heat days

28. Semantic: some results
On peak days:
- widening of lexical base during "heat critical days" - heat as a
conversation topic
- ranking of terms (i.e.:adjectives as "troppo"!) is useful to detect change in
communication during climatic stress
- geographic names appears in terms and hashtags wordsets ("#milano" !).
This fits with recent advances on "social media contribution
to situational awareness during emergencies".

29. SNA of keytagged social media streams
Snow events
#firenzeneve
Begin 10 feb 2013
The Graph metrics of SM streams are dynamics.
The graph centrality analisys of Media and Istitutions
may provide very useful parameters
forWeather Event follow-up.
End 11 feb 2013

30. conclusions
- Methodology for a social "x-rays" of
a weather event: semantic social
media stream as a "contrast
medium" to understand the social
impact of severe weather events
- Methodology social geosensing is able
to map severe weather impacts and
overcome the weakening in
geolocation of social messages and
eliminate the bias due to "social
fakes".
Weather as a key emergency context where it's worth working on
community resilience - also with the help of social insightful contents.

31. Reproducible R code
Github Master class socialsensing Code & Data
https://github.com/alfcrisci/socialgeosensing.git
Wiki Recipes in
https://github.com/alfcrisci/socialgeosensing/wiki

32. #nowquestions
(slowly please if is possible)
www.lamma.rete.toscana.it
www.ibimet.cnr.it

33. #thanks
Contacts:
Alfonso Crisci & Valentina Grasso
mail: grasso@lamma.rete.toscana.it
a.crisci@ibimet.cnr.it
Twitter: @valenitna @alfcrisci
Code and data Alfonso Crisci
alfcrisci@gmail.com
www.lamma.rete.toscana.it
www.ibimet.cnr.it