smerst2013 crisci warwick
DESCRIPTION
Smerst2013 crisci warwick Mapping impacts of severe waether events troughout social media semantized streams.TRANSCRIPT
Social Media and severe weather events: mapping the footprint
Social Media and Semantic Technologies
in Emergency Response
University of Warwick Coventry
15-16 April 2013
Alfonso Crisci - [email protected]
Valentina Grasso - [email protected]
Social Media
Weatherversus
Weather severe events
Where
WhoWhen
as emergency issue in spacetime imply a WWW
…..as a reality Web
aware & prepared
Towards resilient communities means to be ctizen
Changing climate means changing awareness
Imply the reframing in:
Prepardness & Response
Geographical spreading and magnitude of events are important for awareness
Social media and SEO are the information web rivers available.
Are they useful or not?That is the question ( W. Shakespeare).
A question of time event shape
start
peak
decline
weather phenomena and social/communication streams as "analogue" time delayed information waves
time
…..and geography
real physical process
& information flows
Local dynamic type warping means to be explore the
Time coherence between
[ or its mathematical representation!!!!]
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.
Uncovering the Lagrangian Skeleton of TurbulenceMarthur et al.Phys Rev Lett. 2007 Apr 6;98(14):144502. Epub 2007 Apr 4.
Lagrangian coherent structures (LCS)well known in ecology and fluid dynamics
When two or more time-varying systems are connected a supercoherence could be detected if processes are linked.
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 Systemshttp://www.ncgia.ucsb.edu/projects/abmcss/ May Yuan, University of Oklahoma
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.
Heat wave as a good case
severe weather eventEmergency as consequence of "behaviour“.
Awareness is linked to “perception”.
Weather event: early heat wave on 5-7 April 2011
• investigate time/space coherence between the event extension and its social footprint on Twitter
• semantic analysis of Twitter stream on/off peaks days
Research objectives
Severe weather definitionHeat 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
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.
Stakeholders: • forecasters
• institutional stakeholders
• EM communities
• media agents
Products: • DNKT metric
• association of the time vector (DNKT) and a time coupled gridded data stack
• spatial associative map
• semantic analysis Twitter stream:
- clustering
- word clouds
Detect areas where it's worth focusing attention, also for communication purpose.
Target
Data usedHeat 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)
Twitter metric
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"
DNKT - "daily number of key-tagged tweets"
*
**
Geographic associative maps
Semantic based social stream in 1D * time space (DNKT)
Weather informative layers in 2D time* space
LinearAssociation Statisticallybased Verifierby pixel
Geographic Associative Map (2D space)
Impacted areas
It's a weather map at X-rays: Twitter stream is used as a "contrast medium"to visualize impacted areas.
This is not a Twitter map
Associative maps patterns fits
Urban maximum T° over 28 C° on 9 April
where & when
Semantic analysis
- Corpus creationDNKT 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
- Hashtag Word Clouds heat days vs no-heat days
R Stat 15.2 Packages used: tm (Feinerer and Hornik, 2012) & wordcloud (Fellows , 2012)
heat days
terms WordClouds (excluded key-tag
caldo-afa-sete)
heat days no-heat days
Terms association clustering
heat days no heat days
"heat" is THE conversation topic "heat" is marginal to the conversation topic
heat days
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°
Hashtags WordCloudsheat days no-heat days
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".
Semantic: some results
Snow events
SNA of keytagged social media streams
Begin 10 feb 2013
End 11 feb 2013
The Graph metrics of SM streams are dynamics.
The graph centrality analisys of Media and Istitutions may provide very useful parametersforWeather Event follow-up.
#firenzeneve
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.
Reproducible R code
Github Master class socialsensing Code & Data
https://github.com/alfcrisci/socialgeosensing.git
Wiki Recipes in
https://github.com/alfcrisci/socialgeosensing/wiki
#nowquestions(slowly please if is possible)
www.lamma.rete.toscana.itwww.ibimet.cnr.it
#thanksContacts:Alfonso Crisci & Valentina Grassomail: [email protected] [email protected]
Twitter: @valenitna @alfcrisci
Code and data Alfonso Crisci [email protected]
www.lamma.rete.toscana.itwww.ibimet.cnr.it