Socialmediaandcrisismanagement

ChiaraFrancalanci

WSIS,GenevaMay5,2016

Socialmedia

•  RepresentfastandeffecBvecommunicaBonchannels.

•  Allowtheunsolicitedexpressionofpersonalviews.

•  ProvideanopendisplayofconnecBons.•  EnablesocialmediaanalyBcs,withavarietyofapplicaBons.

•  Enablecrowdsourcing.

Asocialmediaparadigm:Crowdsourcing

•  ReferencemodeltoidenBfyandmanagesharedissues

•  SoluBonsdelegatedtothecrowd•  Trustonthe«wisdomofthecrowd»

SocialnetworksrepresentthelargestglobalcommuniBes.Canweleveragetheirstrengths?

TORCIAProject–ObjecBves

TodesignaplaUormthatsupportsthereal-BmeaccesstoTwiWerinformaBon,byselecBngdependableposts, by spreading importantmessages, andby allowingthecooperaBonbetweeninsBtuBonsandthecrowd.InnovaBvetechnologymodules:1)  asemanBcenginethathelpsinformaBonmanagementandprovidesalarms

andtriggers.2)  amobile app that represents the virtual cooperaBon environment (under

development)

RecentusecasesFloodsinSardegna–Sept.2013:•  Over30Ktweetsontheflood•  UseofsocialnetworkstogetrealBme

informaBon(#allertameteoSAR)•  Useofsocialnetworkstocoordinate

recoveryacBviBes(whohasroomfordisplacedpeople)

Emergenza24:•  UseofSocialNetworksfor

EmergencyManagement•  LimitedtoTwiWerwithprecise

guidelines(#Emergenza24)

13911

73696079 6237

105709714 9278

8130

11999

7245

37392449 2360

57114973 4724

3725

6530

0

2000

4000

6000

8000

10000

12000

14000

16000

# T

wee

t

10 day intervals

# Tweet totali

# Tweet classificati

VolumesinItalianfromDec.2012toFeb.2013•  AveragevolumesinItalian:40.000tweet/month•  OnlyhalfofthepostsarerelatedtofloodsaderdisambiguaBon•  VolumesinEnglisharetenBmeshigher

SocialmedianalyBcs–volumesofbuzz

Postsusefulduringtheresponsephasearepredominant

0

1000

2000

3000

4000

5000

6000

7000

8000

Num

ber

of t

wee

ts c

lass

ified

10 day intervals

Preparedness

Response

Recovery

Mitigation

SocialmediaanalyBcs–topicsandphases

TesBng–Sardiniaflood11/18/2013

OtherExamples:Predictingsaleswithsentiment(telefonino.net)

Weeksbetween1/1/2012and30/4/2012.Overlappingyaxes.

Other examples: predic1ng financial risk with sensi1ve news

Service:dailyfeedofnewswithkeyinformaBonextractedaccordingtoapredefinedformatSources:ItalianlocalnewspapersandlocalnewssitesSubjects:newsaboutcompaniesandanykindofcommercialacBvity.Thecompany’snameisextractedsemanBcallyfromthenews.Thatis,thecompany’snameisNOTextractedbymatchingthenewsagainstadatabaseofcompanynames.Typeofnews:weprovidenewswherethecompanyisassociatedwithcriBcaleventssuchasfurto,incendio,multa,cassaintegrazione…(200+typesofcriBcaleventavailable)RaBoofsignificantnews:foreachsignificantnewsprovidedinthefeed,about1000newsareanalyzed(raBo=1/1000)

Predic1ng financial risk Sensi1ve news: Example

SESTO-UnincendiosièsviluppatonellanoWeinuncapannoneinviaFerminellazonadell'Osmannoro,aSestoFiorenBno.LefiammehannointeressatometàdellastruWura,circa1.000metriquadraB,cheospitaunadiWadipelleWeriagesBtadaunciWadinocinese.IlrogohacausatoilcrollodelteWo.Ivigilidelfuoco,intervenuBcondieciautomezzietrentauominidaFirenzeePrato,sonoancorasulposto:lefiammealmomentosonosoWocontrollo.Secondoquantoemerso,ilrogo,divampatoquandononc'eranessunoall'internodelladiWa,sarebberoscaturitepercauseaccidentali,forseuncortocircuito.Inbaseadunaprimavalutazionedeivigilidelfuoco,cheancoranonsonoentraBnellastruWuraacausadellealtetemperature,circametàdelcapannone,dovehasedeunacasaeditrice,nonsarebbestatainteressatadallefiamme,chesarebberostatecontenutedaunmurotagliafuoco.27novembre2012.

streetaddresscriBcaleventcity

typeofcompany

date

•  ThesemanBcengineextractstheelementsavailableinthenews

•  Intheexample,thecompanynameisnotreportedinthenews

•  Thecompanynameisobtainedinthesubsequentmatchingphase,byqueryingdatabasesofItaliancompanies,providingtheelementsextractedfromthenewsassearchkeys

•  Theresultofthematchingphaseis:•  Companyname:ChengXiangS.r.L.•  Address:ViaEnricoFermi50/52,50019

SestoFiorenBno(FI)•  Otherdata,suchastheParBtaIva

numberarealsoprovided

Socialmediaandsecurity

•  OpportuniBesaremaximizedifusersareregisteredandtheironlineidenBtyisknownacrossdifferentsocialmedia

•  Inothercontexts,registraBonisnotpossible,butwhensecurityisthegoalpre-registraBonseemsaviablesoluBon

•  Pre-registraBonwouldenabletheanalysisofsocialmediainformaBontotraceuserbehaviorandidenBfybehavioralpaWernsthatarecorrelatedwithspecificsecuritythreats.

•  PreviousexperienceinthesefieldsshowsthatsocialmediaareavalidcomplementtomoretradiBonalsourcesofinformaBon.

AneedforasemanBcmodelThebasicbuildingblocksofasemanBcmodelforsocialmediaanalyBcsare:•  A“securityissue”whichistheprimarytopicofthemodel,and

“categories”whicharesub-topicsunderthatissue.Forexample,“recruitment”couldbeacategoryundertheissueof“terrorism”.

•  DefiniBonsofenBBesthatarespecifictothesecurityissue,includingpropernames(e.g.espionage,organizedcrime,criminalgang,orISIS).

•  Mappingofthelanguage,expressionsandtypicalsyntacBcstructureusedtoindicatespecificacBonsoropinions(inmanycase,alternaBvemeaningsareassociatedwithmainstreamconversaBonpaWern,afocusonthespeakerhelps).

•  BuildataxonomyforsenBmentanalysis(e.g.“makethempay”).

Conclusions

•  SocialmediaanalyBcsraiseabigdataissue•  MulBplesocialmediashouldbeconsideredandtheidenBtyofpeopleshouldbetracedacrossmedia

•  DomainexperBseiskeytobuildasemanBcmodel

•  PredicBveanalyBcsrequirehistoricaldataoverextendedperiodsofBme