Social media and severe weather events


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Which role for social media during severe weather events? A case study of italian Twitter-sphere during an heat-wave (April 2011): semantic analysis and associative maps.

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  • weather channel app is the fifth most downloaded app of all time -
  • An associative map could be done by the level of significance for the linear association calculated between a generic time vector (DKTN) and a time coupled gridded data stack (Weather time consecutive layers). The significant impacted areas emerges geographically where the social unidimensional signal is coherent and linearly correlated with the correspondent time vector of pixel/box values inside the grid. Tree significance levels are generally considered in function to the p.value of null significance statistical test (NO:p >0.1 Weak :p >0.05 Strong : p<0.05)
  • Method 's strength is due to the validity of assumption that exist a linear association between weather process and " verbosity " concerning words semantically linked to weather sensations felt by people. This is the basis of geographical matching between social streams and weather data. Method 's gain is also the overcoming of the bias due to "false tweet" and population density lackness.
  • Social media and severe weather events

    1. 1. Social Media andsevere weather events: an heat wave footprint on Twitter Disaster 2.0 - MasterClass Bruxelles 17/01/2013 Valentina Grasso - Alfonso Crisci -
    2. 2. The 5 C of Social Media• contents (UGC)• conversation• connection• collaboration•A community behaviour- big lens on human- Extract useful information from Big Data
    3. 3. SM e weather servicesPlenty of weather content on SM and mobile APP- weather is a common conversation topic- personalization of weather forecast- local dimension- weather is a special case of "emergency" issue
    4. 4. Weather as emergency issuemain features•FREQUENT: vs to other emergencies #fires•FAMILIAR: people deal with weather daily #earthquake•PREDICTABLE: important for warnings #chemical•LOCATED: specific spatial and temporal dimension #nuclear #disaster #health #terrorism who when where
    5. 5. Building resilientcommunitiesWeather as an operational context where community may increase "resilience" attitude.In emergency "behaviours" modulate "impacts" on society.If Im aware and prepared I act responsibly.US tornado warning:people get used to "weatherwarnings" and they learnt to beproactive in protection.
    6. 6. Changing climate - changingawareness In Italy and Europe in the last 10 years climate change made us more exposed to extreme weather events - "preparedness" Tornado hits: US - Italy 1999-2009 Geographical spreading and magnitude of events are important for awareness
    7. 7. Working on Italian Twitter-sphereWeather 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
    8. 8. Heat wave as a good caseEmergency as consequence of "behaviour"Communication is key: "how to act"
    9. 9. 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:
    10. 10. 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 vector •institutional stakeholders(DNKT) and a time coupled •EM communitiesgridded data stack •media agents•spatial associative map•semantic analysis Twitter Targetstream: Detect areas where its worth- clustering focusing attention, also for- word clouds communication purpose.
    11. 11. 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)
    12. 12. 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"
    13. 13. 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)
    14. 14. 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.
    15. 15. A question of shape weather phenomena and peak social/communication streams as "analogue" time delayed information wavesstart decline time
    16. 16. Associative maps fits well Urban maximum T° over 28 C° on 9 Aprilwhere & when
    17. 17. 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 daysClustering associated termsTerm frequency ranking comparison- Hashtag Word Clouds heat daysheat days vs no-heat days R Stat 15.2 Packages used: tm (Feinerer and Hornik, 2012) & wordcloud (Fellows , 2012)
    18. 18. terms WordCloudssete) (excluded key-tag caldo-afa- no-heat days heat days
    19. 19. Terms association clustering heat days no heat days"heat" is THE conversation topic "heat" is marginal to the conversation topic
    20. 20. heat days
    21. 21. 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°
    22. 22. Hashtags WordClouds no-heat days heat days
    23. 23. 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 researches on "social media contribution to situational awareness during
    24. 24. conclusions- Methodology for a social "x-rays" of a weather event: Twitter 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.
    25. 25. Reproducible R codeGithub Master class socialsensing Code & Data Recipes in
    26. 26. #thanksContacts:Valentina Grassomail: grasso@lamma.rete.toscana.itTwitter: @valenitnaCode and data Alfonso