Disaster 2.0 - MasterClass Bruxelles 17/01/2013

Social Media and severe weather events: an heat wave footprint on Twitter

Valentina Grasso - grasso@lamma.rete.toscana.it

Alfonso Crisci - a.crisci@ibimet.cnr.it

The 5 C of Social Media

•contents (UGC)

•conversation

•connection

•collaboration

•community- A big lens on human behaviour - Extract useful information from Big Data

SM e weather services

Plenty 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

Weather as emergency issue main features

•FREQUENT: vs to other emergencies

•FAMILIAR: people deal with weather daily

•PREDICTABLE: important for warnings

•LOCATED: specific spatial and temporal dimension

wherewho

#fires#earthquake#chemical #nuclear #disaster#health#terrorism

when

Weather as an operational context where community may increase "resilience" attitude.

In emergency "behaviours" modulate "impacts" on society.

If I'm aware and prepared I act responsibly.

US tornado warning:

people get used to "weather warnings" and they learnt to be proactive in protection.

Building resilient communities

Changing climate - changing awareness

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

Weather event: early heat wave on 5-7 April 2011

Working on Italian Twitter-sphere

• 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

Heat wave as a good case

Emergency as consequence of "behaviour"

Communication is key: "how to act"

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

A question of shape

start

peak

decline

weather phenomena and social/communication streams as "analogue" time delayed information waves

time

Associative maps fits well

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 researches on "social media contribution to situational awareness during emergencies".

Semantic: some results

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 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

#thanksContacts:Valentina Grassomail: grasso@lamma.rete.toscana.it

Twitter: @valenitna

Code and data Alfonso Crisci a.crisci@ibimet.cnr.it

www.lamma.rete.toscana.itwww.ibimet.cnr.it