Smerst2013 crisci warwick


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

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Smerst2013 crisci warwick

  1. 1. Social Media and Social Media and Semantic Technologies in Emergency Responsesevere weather events: University of Warwick Coventrymapping the footprint 15-16 April 2013 Alfonso Crisci - Valentina Grasso -
  2. 2. Social Media Weather
  3. 3. Weather severe events as emergency issue in spacetime imply a WWW Who When Where … a reality Web
  4. 4. Towards resilient communities means to be ctizen aware & prepared
  5. 5. Changing climate means changing awarenessImply the reframing in: Prepardness & Response Geographical spreading and magnitude of events are important for awareness
  6. 6. Social media and SEO are theinformation web rivers available.Are they useful or not?That is the question ( W. Shakespeare).
  7. 7. A question of time event shape weather phenomena and peak social/communication streams as "analogue" time delayed information wavesstart decline time
  8. 8. …..and geography
  9. 9. Local dynamic type warping means to be explore the Time coherence betweenreal physical process [ or its mathematical representation!!!!] & information flows
  10. 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 ecologyUncovering the Lagrangian Skeleton of Turbulence and fluid dynamicsMarthur 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. 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 SpatialSystems Yuan, University of Oklahoma
  12. 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. 13. Heat wave as a good casesevere weather event Emergency as consequence of "behaviour“. Awareness is linked to “perception”.
  14. 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. 15. Severe weather definitionHeat wave: its a period with persistent T°above the seasonal mean. Local definitiondepends 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:
  16. 16. Target and ProductsConsorzio 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 its worth - clustering focusing attention, also for - word clouds communication purpose.
  17. 17. 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)
  18. 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. 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. 20. Impacted areas This is not a Twitter map Its a weather map at X-rays: Twitter stream is used as a "contrast medium" to visualize impacted areas.
  21. 21. Associative maps patterns fits Urban maximum T° over 28 C° on 9 Aprilwhere & when
  22. 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. 23. terms WordClouds (excluded key-tag caldo-afa-sete) heat days no-heat days
  24. 24. Terms association clustering heat days no heat days"heat" is THE conversation topic "heat" is marginal to the conversation topic
  25. 25. heat days
  26. 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. 27. Hashtags WordClouds heat days no-heat days
  28. 28. Semantic: some resultsOn 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. 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. 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 its worth working oncommunity resilience - also with the help of social insightful contents.
  31. 31. Reproducible R codeGithub Master class socialsensing Code & Data Recipes in
  32. 32. #nowquestions(slowly please if is possible)
  33. 33. #thanksContacts:Alfonso Crisci & Valentina Grassomail: a.crisci@ibimet.cnr.itTwitter: @valenitna @alfcrisciCode and data Alfonso