SOCIALMEDIA&POLICING:ComputationalApproachesto

EnhancingCollaborativeActionbetweenResidentsandLawEnforcement

Niharika SachdevaPhDThesisDefenseTCSResearchScholarniharikas@iiitd.ac.in

WhoamI?� Ph.D.student� SeniorResearchScientist@PhilipsResearch,India� TCSResearchScholar� Doneworkincomputermediatedcommunicationandusablesecurity(HCI)

� Researchinterests� Collaborationandcommunication� MachineLearning� Humancomputerinteraction� Usablesecurityandprivacy

2

IndiaisBiggestPoliceDepartment

3

238PoliceOfficersper100,000

129PoliceOfficersper100,000

327PoliceOfficersper100,000

WhichisIndia?SouthAfrica?USA?

4

MostOverworked– NeedHelp!

CollaboratingwithResidents

One– waycommunication

5

Two– waycommunication

Asynchronous,RemoteandPublicplatformforInteraction

NeedforImprovedCollectiveActionandAccountability

HowaboutInteractingwithPoliceonOSM?

6

� HowmanyofyouareonFacebook/Twitter?� Howmanyofyouknowaboutsocialmediapolicepages/accountsorusethemtointeractwithpolice?

NewMediatoStayConnected

7

ThreeDimensionsforSuccessfulCollaboration

High

Moreaccurateanalytical andmodelingtools

Low

High

High

Morepeople involved

Moredata available

PoliceResident

Charalabidis, Yannis, and Sotirios Koussouris, eds. Empowering open and collaborative governance: Technologies and methods for online citizen engagement in public policy making. Springer Science & Business Media, 2012.

8

Challenges:SuccessfulCollaboration� Identifyinghowsocialmediacansupportday-to-dayinteractionbetweenpoliceandresidents

� AnalyzingandExtractingmeaningfulandactionableinformationfromenormousdata� Unstructuredandunconstrained� Inferringactionableinformation� Quantifyingbehavior(emotionsandlinguisticattributes)

� Maintaining responsiveness toresidents� Promptnessandtimelyactionbypoliceonsocialmedia� Engagingwithpeople

9

High

Moreaccurateanalyticalandmodelingmethods

Low

High

High

Morepeople involved

Moredataavailable

Challenges:SuccessfulCollaboration� Identifyinghowsocialmediacansupportday-to-dayinteractionbetweenpoliceandresidents

� AnalyzingandExtractingmeaningfulandactionableinformationfromenormousdata� Unstructuredandunconstrained� Inferringactionableinformation� Quantifyingbehavior(emotionsandlinguisticattributes)

� Maintaining responsiveness toresidents� Promptnessandtimelyactionbypoliceonsocialmedia� Engagingwithpeople

10

High

Low High

Morepeople involved

Moredataavailable Moreaccurateanalytical

andmodelingmethods

High

Challenges:SuccessfulCollaboration� Identifyinghowsocialmediacansupportday-to-dayinteractionbetweenpoliceandresidents

� AnalyzingandExtractingmeaningfulandactionableinformationfromenormousdata� Unstructuredandunconstrained� Inferringactionableinformation� Quantifyingbehavior(emotionsandlinguisticattributes)

� Maintaining responsiveness toresidents� Promptnessandtimelyactionbypoliceonsocialmedia� Keepengagingwithpeople

11

High

Low High

Morepeople involved

Moredataavailable Moreaccurateanalytical

andmodelingmethods

High

Challenges:SuccessfulCollaboration� Identifyinghowsocialmediacansupportday-to-dayinteractionbetweenpoliceandresidents

� AnalyzingandExtractingmeaningfulandactionableinformationfromenormousdata� Unstructuredandunconstrained� Inferringactionableinformation� Quantifyingbehavior(emotionsandlinguisticattributes)

� Maintaining responsiveness toresidents� Promptnessandtimelyactionbypoliceonsocialmedia� Engagingwithpeople

12

High

Moreaccurateanalyticalandmodelingmethods

Low

High

High

Morepeople involved

Moredataavailable

CoreThesisQuestion

Howcansocialmediaplatformsbeutilizedtosupport,analyze,

andenhanceday-to-daycollaborativeinteractionbetween

policeandresidentsusingcomputationalmethods?

13

Contributions� Identifyneedforsocialmediasupport incollaborativepolicing

� Quantifyandmineunstructureddatatoanalyze communicationattributesand

actionableinformation

� Proposeamethodtoenhance collaboration

� Policeresponsivenessusingrequest–responsedetectionframeworkandpolice

responsequantificationtoresidents

� Imagesretrievalarchitectureforimprovedcollectiveactionusingusergenerated

content

14

Contributions� Identifyneedforsocialmediasupport incollaborativepolicing

� Quantifyandmineunstructureddatatoanalyze communicationattributesand

actionableinformation

� Proposeamethodtoenhance

� Policeresponsivenessusingrequest–responsedetectionframeworkandpolice

responsequantificationtoresidents

� Imagesretrievalarchitectureforimprovedcollectiveactionusingusergenerated

content

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ResearchProblem

InitialCoding

AdvancedMemo

TheoreticalSamplingnewdata

Integratingfordimensions

NeedforSupport:RequirementElicitation� Whatopportunities socialmediaoffersforsupportingcollaboration?

� Whatchallenges policeandresidentscanfacewhileadoptingsocialmediaforcollaboration?

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• 17Interviews• 200Surveys

• 20Interviews• 402Surveys

Multi-stakeholder& MixedMethod

LimitedGroundedTheoryApproach

Why Which ForWhom Challenges

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EDUCATEDYOUNGANYONE

NeedforSupport:RequirementElicitation

PoliceOfficers ResidentCommunities

CollaborativePlatform

CollaborativeEcosystemandActors

InteractionLayer

MeaningfulInformation

Acknowledgement/ResponseSystem

VerificationandCredibilityAssessment

Lessons

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Contributions� Identifyneedforsocialmediasupport incollaborative

policing

� Quantifyandmineunstructureddatatoanalyze

communicationattributesandactionableinformation

� Proposeamethodtoenhance

� Policeresponsivenessusingrequest–responsedetection

frameworkandpoliceresponsequantificationtoresidents

� Imagesretrievalarchitectureforimprovedcollectiveaction

usingusergeneratedcontent19

High

Low High

Morepeople involved

Moredataavailable Moreaccurateanalytical

andmodelingmethods

High

QuantifyingInteraction� Exploringthefeasibilityofsocialmediainquantifyingattributesof

communication

� Identifyingbehavioralattributeslikeaffectiveexpression,engagementand

socialandcognitiveresponseprocesses

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ResidenttoResident

ResidenttoPolice

PolicetoResident

PolicetoPolice

MixedMethodApproach:DataCollection

21

85PublicandofficialPoliceDepartment

Averageage3years(from2010– April2015)

47,474wallpostsand85,408statusupdates

DTw/≥1Comment P&C CTotalDT

85,408

47,474

46,845

24,984

5,519

17,196

41,326

7,788

PP&C

RP&C

PC

CC

QuantitativeData QualitativeAnalysis

MixedMethodApproach:DataCollection

22

85PublicandofficialPoliceDepartment

Averageage3years(from2010–April2015)

47,474wallpostsand85,408statusupdates

DTw/≥1Comment P&C C

TotalDT

85,408

47,474

46,845

24,984

5,519

17,196

41,326

7,788

PP&C

CP&C

PC

CC

QuantitativeData

1600commentson255posts

Posts&Comments

Collectedpublicposts,21July- 21Aug2014

QualitativeAnalysis

QuantifyingInteractionforMeaningfulInformation� ContentClusterIdentification

� Natureofcontentandtopics

� EmotionalExchange Quantification

� Natureofemotionsandaffectiveexpression

� CognitiveandSocialOrientationQuantification� Typeoflinguisticattributesthatcharacterizecognitiveandsocialorientation

� Engagement (Response)Quantification� Quantityandnatureofengagement

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MixedMethodApproach:Methods

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Topics

• Unigram (N) GramAnalysis• K-meansClusters withK-means++seeding

Emotional

• Valence• Arousal

Socialandcognitive

• InterpersonalFocus• SocialOrientation• Cognition

Engagement

• No.ofpoliceandresidentwhocommentinDTs• DistinctcitizenswhocommentinDTs• Shannon’sWienerDiversityindex• Averageno.of likesandcomments

LIWCandAnewDictionary

LIWCDictionary

Quantitative Data ThematicInductiveAnalysis

MixedMethodApproach:Methods

25

Topics

• Unigram (N) GramAnalysis• K-meansClusters withK-means++seeding

Emotional

• Valence• Arousal

Socialandcognitive

• InterpersonalFocus• SocialOrientation• Cognition

Engagement

• No.ofpoliceandresidentwhocommentinDTs• DistinctcitizenswhocommentinDTs• Shannon’sWienerDiversityindex• Averageno.of likesandcomments

LIWCandAnewDictionary

LIWCDictionary

QuantitativeData ThematicInductiveAnalysisValidateandCharacterize

Typeofsub-topicsinResidentsPosts

Direct/IndirectConcerns+

Styleofcommunication

TypeofEngagement

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Unigram Freq. Unigram Freq.

rules 0.015 safety 0.012safety 0.014 following 0.011

violations 0.014 notice 0.010challans 0.011 prosecuted 0.009please 0.011 movement 0.008citizens 0.01 complaint 0.008

Focusonadvisories,thestatusofdifferentcasesbeinginvestigated

(MannWhitneyUtest,p<.05,z=−3.57)

Mostpoststendtorequestpolicetotakeactionontheircomplaints

Unigram Freq. Unigram Freq.

please 0.026 people 0.022take 0.021 please 0.02action 0.019 one 0.019people 0.019 take 0.016one 0.019 action 0.015time 0.017 time 0.015

HigherReferenceto“people”

QuantifyingInteractionforMeaningfulInformation

K-Means++Seeding:ClustersofTopics

� Policeinitiateddiscussionsaremorefocusedthancitizeninitiated.

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Awarenessdrive/safetycampaigns

Prosecuted/actiontakenreports

Advisoriesonsituations

Newspaperarticles

Citizentipsandcomplaints

Neighbourhood problems

Missingpeople

Appreciation

QuantifyingInteractionforMeaningfulInformation

QuantifyingInteraction� TopicIdentification

� Natureofcontentandtopics

� EmotionalExchange Quantification

� Natureofemotionsandaffectiveexpression

� CognitiveandSocialOrientationQuantification� Typeoflinguisticattributesthatcharacterizecognitiveandsocialorientation

� EngagementQuantification� Quantityandnatureofengagement

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� Negativesentimenthigherinresidentinitiatedthreadsthanpolice

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CP&C CC

Avg Std.dev Avg Std.devNA 0.021 0.03 0.018 0.04Anx 0.001 0.01 0.003 0.02Anger 0.006 0.02 0.005 0.02Arousal 4.4 1.74 3.9 2.16

16.67%higherinCP&C

12.82%higherinCP&C

Higherarousalandnegativeaffecttobemarkersofsensitisationbecauseofcrime!

Cp&c CcAvg Std.dev Avg Std.dev

NA 0.021 0.03 0.018 0.04Anx 0.001 0.01 0.003 0.02Anger 0.006 0.02 0.005 0.02Arousal 4.4 1.74 3.9 2.16

200%higherinCc

(Mann-WhitneyU, p<.01)

(Mann-WhitneyUp<.01)

MeaningfulInformationQuantification:Emotions

� Discussionthreadsinvolvingjustthecitizensarehighlyself-attentionfocused

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Likelycitizensmostlyexpresstheirownconcernsthattheyfacewithothers

CP&C CC

ppron 0.062 0.059 0.045 0.056i 0.008 0.017 0.014 0.033

shehe 0.002 0.01 0.003 0.003they 0.005 0.013 0.008 0.008

75%More

I havelivedintheUKandallthetimeIhaveneverheardanyonehonking.…. ifIseeanyonewhodon'tcomply?

(UTestp<.01,z=−16.02)

MeaningfulInformationQuantification:SocialandCognitiveOrient.

Self-focused

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MyVehicleKA-02-HW-3183whitecolorHondaDiowasstolenfromKadamba Hotel(NearModiHospital),RajajiNagar onFriday(25th July)eveningbetween6:30-7:45PM.Pleasehelpintracingmyvehicle.

DearBCP,though IstayatJPNagar,butbeingpartofKSFCLayoutRWA(Banaswadi Policestation),IgottoknowthattherearefrequentproblematKSFCLayoutnearBBMPHall...….

Accountability:MeaningfulInformation

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WordTreevisualizationsofpostsinwhichresidentsquestioned policeusingthewordwhy.

QuantifyingInteraction� TopicIdentification

� Natureofcontentandtopics

� EmotionalExchange Quantification

� Natureofemotionsandaffectiveexpression

� CognitiveandSocialOrientationQuantification� Typeoflinguisticattributesthatcharacterizecognitiveandsocialorientation

� EngagementQuantification� Quantityandnatureofengagement

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EngagementQuantification� ContentGeneration

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Police+Citizens 55,028 1,79,176 17,124 12,630

CitizensOnly 54,982 1,79,176 17,081 12,630

Entropy 4.39 4.96 3.23 3.6

Police Resident

26%lower

10.28%lower

Lowerentropy: largenumberofcommentsarepostedbyasmallnumberofcitizensandpolice

Engagement(Response)Type

35

Ignored(#83)

Acknowledged (21.3%)

Reply

FollowUp(10%)

DearX,Wewilltakeallpossiblelegalmeasuresinthisregard.Thankyou.

DearX,Pleaseprovide thepolicestationdetails.Thankyou.

[Receivednoreply]

DearX,Thisposthasbeenforwarded toappropriatePoliceStation….

DearX,Pleaselodgeacomplaintatyournearestpolicestationwiththedetails…..

44.3%

22%172posts

Lessons

36

ResponsivenessAccountability

QuantifyAnd

Extract

NeedforEnhancingPoliceResponsiveness

Contributions� Identifyneedforsocialmediasupport incollaborativepolicing

� Quantifyandmineunstructureddatatoanalyze communicationattributesand

actionableinformation

� Proposeamethodtoenhance

� Policeresponsivenessusingrequest–responsedetectionframeworkandpolice

responsequantificationtoresidents

� Imagesretrievalarchitectureforimprovedcollectiveactionusingusergenerated

content

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ServiceableRequests

“Amessagethatsolicitsaresponseinaformofanactionorinformation

fromthepolice”

38TucsonPolice.2016.CallsforService.https://www.tucsonaz.gov/police/terms.(May2016).

Low

High

High

High

Morepeopleinvolved

Moredataavailable

Serviceablev/sNon-ServiceablePosts

39

NeedmoreInformation

Forward

GiveSolution

Copsdrivingwrongside[ofroad]nearXXXhotel..whatactionwillbetakenagainstthem

Date:4/11/2015(Wednesday),Time:10:17pm,Number:[withheld],Location:[withheld],Violations:Crossinglinebywaytoomuchobstructingthevehicleswhichwerecomingfrom[withheld]entrancelaterhejumpedthesignal.

Admin!!CanUExplaintomerulesandregulationsfortransferringvehiclefromChennaitoBangalore?

IgnoredXXXshared NowThisFuture's video.21Februaryat10:07 · BENGALURUCITYPOLICE Interestingpieceofhandgun bullet-proof shieldindevelopment.

Acknowledge ChennaiCityTrafficPolice:ahumblesalutefromafellowChennaiite forthecommendablejobinsuchrains!!

ResearchQuestions:Serviceability� RQ1:Whatattributesdifferentiates

� Serviceablepostsfromnon-serviceablerequestsand� Sub-typesofserviceablerequestsw.r.t contentcharacteristicssuchaslinguisticandemotionalattributes?

� RQ2:Howdoespoliceresponsetimevarybetweenserviceableandnon-serviceablepostsmadeonsocialmedia?

� RQ3:Canmachinelearningtechniquesbeusedtoautomaticallyidentifyserviceablerequests� Canwefurtherclassifythemintodifferentsub-typesusingpostcharacteristics(contentandmetadata)?

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Dataset85PublicandofficialPoliceDepartment

22,213wallposts

1000PostsannotatedbyPolice

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PostType #Posts Likes Comments

ServiceablePosts

Forward 286 1383 661

GiveSolution 88 183 121

Thanks 72 1288 63

NeedMoreInfo. 104 1245 258

Total 550 4099 1103

Non-Serviceable

Total 113 316 32

0.77agreementusingFleissAgreement

AttributesEmotion(Alchemy&

LIWC)

States: Anger,disgust,fear,joy,sadnessValence:Positive,negative,Anxiety

Cognitive(LIWC)

CognitiveMechanism: Tentativeness andDiscrepancy

Inter-Personal(LIWC)

1stpersonsingular&plural,2ndperson,3rdpersonsingular& plural,andimpersonalpronouns

Linguistic(LIWC)

Objectivity,Tenses,LexicalDensity&Parts-Of-Speech.

QuestionAsked

(Heuristics)

who,how,why,what,where,whomandcontaininga“?”

Entities(Alchemy)

people,companies,organizations,cities,geographicfeatures,facility,dateand time

42

Attributes� TopTopicsServiceableandNon-ServiceablePostsusingLDA

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LDAtopic VocabularyTrafficcongestion Traffic,road,signal,bus,people, turn,jamSharedphotoswebsites

com,www,facebook,https,videos,traffic,http,type,old,photos,job

Appreciation signal,great,good,taking,act,actionQuestionposed asked,rules,vehicle,sir,said,car,know, whatPlaces Telangana,state,hyderabad,city,nagar,Finesissued Challan[finecharged],violation,documentsCybercrime Police,city,cyber,crimenampally,complaint,

better,safe

Attributes� Non-negativeMatrixFactorization(NMF)fordetectingcloselyconnectedtopicsinsub-types

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NMFtopic VocabularyPoliceincorrectdecision

Police,asked,said,constable,taken,publicwrong,driving,pay,vehicle,come,way

Awareness Don’t, mobile,rules, need,people,let,share,helmet,circle,

Dangerousdrivingcomplains

wrong,dangerous,action,driving,turn,goingjunction

Finesissued Vehicle,challan[finecharged],number,violationfine,documents,driving,guys,stopped,pay

Parkingissues Parking,people,bus,stop,parked,time,action

UsedFrobeniusNorm

RQ1:AttributesDefiningServiceability� Serviceablerequestsshowsignificantlyhighervalueofnegativeemotionalstates

45

Serviceable Non-Serviceable

Avg Std.dev Avg Std.dev Man.Anger 0.15 0.13 0.13 0.17 -3.43**Disgust 0.34 0.25 0.23 0.27 -3.88**Fear 0.24 0.21 0.15 0.18 -6.09**Sad 0.11 0.10 0.10 0.14 -5.45**

+15.38%

+60%

Presumably,emotionalstatesareexperiencedduetodistresscausedbecauseofencounterswithlawandordersituation.

RQ1:AttributesDefiningServiceability� Serviceablerequestsshowsignificantlyhigheruseof1stpersonsingularpronouns� highlyself-attention

46

Serv. Non-Serv. Frwd Give Thanks Need

1stpersonSingular**

Avg. 1.68 1.54 1.61 2.56 0.70 1.80

Sd. 2.96 9.50 2.45 3.54 2.36 3.77

Iamjustworried ifHyderabadTrafficPolice[HTP]makesthingsworselikealways

RQ1:AttributesDefiningServiceability� ServiceablepostsshowedhigherObjectivity

47

Serv. Non-Serv. Frwd Give Thanks NeedObjectivity** Avg 2.86 2.04 +40% 3.47 2.64 2.07 1.9

Sd 2.84 2.63 3.16 2.8 2.6 1.29

Serv. Non-Serv. Frwd Give Thanks Need

PastTense Avg 1.75 0.81 1.88 1.68 0.78 2.14

Sd 2.99 2.87 2.86 3.55 2.13 3.23

Serviceablepostscontainfactualinformationonwhichthepolicecanactupon.

� ServiceablepostsshoweduseofPasttense

Copsweredrivingonthewrongsidenear[withheld]hotel..whatactionwastakenagainstthem?

ResearchQuestions:Serviceability� RQ1:Whatattributesdifferentiates

� Serviceablepostsfromnon-serviceablerequestsand� Sub-typesofserviceablerequestsw.r.t contentcharacteristicssuchaslinguisticandemotionalattributes?

� RQ2:Howdoespoliceresponsetimevarybetweenserviceableandnon-serviceablepostsmadeonsocialmedia?

� RQ3:Canmachinelearningtechniquesbeusedtoautomaticallyidentifyserviceablerequests� Canwefurtherclassifythemintodifferentsub-typesusingpostcharacteristics(contentandmetadata)?

48

RQ2:PoliceResponseTime� SurvivalTime

� Timeuntil theeventofinterestoccurs

� CensoringEvent� Postswhichdidnotreceiveareplyduringourobservationperiod

� SurvivalProbability� Probability thatapostsurviveslonger thansomespecifictime(t)givenbysurvivalfunctionS(t) i.e.itdoesnotreceiveareply

49

TotalN N ofEvents Censored %Censored

Frwd 286 182 104 34.60

Give 88 53 35 39.80

Thanks 72 5 67 93.10

Need 104 60 44 42.30

Serv. 550 300 250 45.50

PoliceresponsesaremaximumforForwardSub-typeposts

RQ2:PoliceResponseTime

50

MeanEst. Sd.Error MedianEst.

Sd.Error

Frwd 1062.52 82.22 21.33 2.05

Give 1064.37 138.08 20.43 8.45

Thank 2693.14 86.42 -- --

Need 1136.31 127.48 28.26 10.45

Serv. 1326.94 61.23 33.33 --

statisticallysignificantdifferencebetweenallfour sub-typesLogRank(Mantel-Cox)test(χ2=57.03,df=3,p<0.005).

PolicereplytopoststhatcanbegivensolutionimmediatelyfollowedbyForward

� KaplanMeierEstimator

ResearchQuestions:Serviceability� RQ1:Whatattributesdifferentiates

� Serviceablepostsfromnon-serviceablerequestsand� Sub-typesofserviceablerequestsw.r.t contentcharacteristicssuchaslinguisticandemotionalattributes?

� RQ2:Howdoespoliceresponsetimevarybetweenserviceableandnon-serviceablepostsmadeonsocialmedia?

� RQ3:Canmachinelearningtechniquesbeusedtoautomaticallyidentifyserviceablerequests� Canwefurtherclassifythemintodifferentsub-typesusingpostcharacteristics(contentandmetadata)?

51

Formulation

52

RQ3:AutomaticClassificationPerformance:Costweights� Ten-foldCrossValidationPerformanceofdifferentalgorithmstocorrectlyidentifyserviceableposts.

� Contentattributessuchasemotionsandlinguisticattributesarehighlypredictiveofserviceablepostsinadditiontobag-of-wordsmodel

53

Algorithm Recall F1 Accuracy

RF 0.97 0.85 0.87

LR 0.82 0.77 0.76

ADT 0.96 0.80 0.86

DT 0.84 0.78 0.77

GBC 0.94 0.83 0.84

Attributes R2 Deviance

Emotion 0.23 437.88

Linguistic 0.19 401.83

Bag-of-words 0.53 260.07

� +Model1� Explains15.6%of thevariance� Reducesdeviancesignificantlyto1,127.58(178.14less).� Betterpredictorsaresadness,fear,andjoy.

� +Model2� Explains20%ofthevariance� 1stpersonsingularpronouns havestatisticallysignificant

54

RQ3:AutomaticClassificationPerformance

� +Model3� Explains26.3%of thevariance� Reducesdeviancesignificantlyto984.92.� Nothelpful:Tenses(presentandfuture) andlexicalterms(verbsandadverbs)

� +Model4� Explains32.4%of thevariance&devianceis902.79i.e.82.13less� Reliablepredictors:question, date,time,andentitycount� Topicdoesnothelpmuch

55

RQ3:AutomaticClassificationPerformance

ServiceabilityPlugin

56

Contributions� Identifyneedforsocialmediasupport incollaborativepolicing

� Quantifyandmineunstructureddatatoanalyze communicationattributesand

actionableinformation

� Proposeamethodtoenhance

� Policeresponsivenessusingrequest–responsedetectionframeworkandpolice

responsequantificationtoresidents

� Imagesretrievalarchitectureforimprovedcollectiveactionusingusergenerated

content

57

Challengesinsimilarimagesretrieval

58

Imageprocessing

1.Scaling

2.Cropping

3.Stitching

4.Multiple

59

DataCollectionSampleimages Event Total

imagesSimilarimages

Dissimilarimages

CharlieHebdo 568 118 450

KulkarniInk 1,905 354 1,551

InsultsHanuman 664 277 387

ShaniShingnapur 180 70 110

RamRahim 408 97 311

Imagefeaturesforsimilarity

1. Hand-CraftedFeaturesa. 3D-colourhistogramb. Daisyfeaturesc. ORB(OrientedFASTRotatedBRIEF)featuresd. ImprovedORB(ORB+RANSAC)

60

2.TrainableFeaturesa. DeepCNN(ConvolutionNeural

Network)

AlecRadford,LukeMetz,andSoumith Chintala.Unsupervisedrepresentationlearningwithdeepconvolutionalgenerativeadversarialnetworks.

OriginalImage

ModifiedImage

ImprovedORB

(%accuracy)

CNN(%accuracy)

Modification

93.7 98.3 Scaled,stitchedimage,addedtext,cropped

77.4 93.8 Cropped,stitched,text

added

84.1 99.4 Scaled(7.4✕ 5.2)

61

Competingonmodifiedimages

PicHunt

62

Contributions� Identifyneedforsocialmediasupport incollaborativepolicing

� Quantifyandmineunstructureddatatoanalyze communicationattributesand

actionableinformation

� Proposeamethodtoenhance collaboration

� Policeresponsivenessusingrequest–responsedetectionframeworkandpolice

responsequantificationtoresidents

� Imagesretrievalarchitectureforimprovedcollectiveactionusingusergenerated

content

63

Whyitmatters?� ProposeaData-DrivenTechniquetocomplementOverworkeddepartments

� Detectingpoststhatshouldelicitpoliceresponsemakesocialmediastreamsmorelistenableforresident’sconcerns

� Helppoliceimprovepolicingandresponsiveness� Takingcognizanceofprominentconstituents’concernandunsaferegionscanhelppoliceplantheirresourcesbettertoprovideimprovedsafety

� Measureresident’sreactionsinafine-grainedmanner� Information(e.g.,emotionsandinterpersonalattributes)improvetheunderstandingfromfactualinformationtoamorenuancedunderstandingofpsychological.

64

TechnologicalAdvancement� Designingearlywarningsystemsthatindicate:

� Needforemotional&socialsupportneedstoenhancepoliceresponsetoresidentsexperiencingsafetyissues.

� Afeedbacksystemonsocialmediaplatformsthatcomplementslackofphysicalsignalsofcommunication� informsaboutthelikelytimedurationtorespondtoservicerequest

� Senseandrecordthereactionsofcitizensandsharetheserecordswithdecisionmakers� Taketimelymeasuresandgainbetterinsights

65

Limitations� CulturalLimitation

� Stronghistoryofcommunitypolicingmaybehelpful

� RestrictedModality� Onlytextbasedserviceabilitydetection� Considerothermodalitiessuchasvideos,imagesetc.

� UrbanandSub-urbanResidentCommunities� Ruralareasmayhavedifferentneeds

� Causality� Analysisisbasedoncorrelations

66

67

Acknowledgement� Googlefortravelsupport

� TataConsultancyServicesforfundingthethesiswork

� Allparticipantsandpoliceofficerswhohelpedusinvariousstagesofthethesis

� Mycollaborators– specialthankstoDr.Nitesh Saxena (UAB),Dr.MunmunDeChoudhury(GaTech),Dr.IuliaIon(Google)

� MonitoringCommitteeDr.RahulPurandare,Dr.Sambuddho Chakravarty,Dr.Amarjeet Singh

� Dr.AditiGupta,Dr.Paridhi Jain,SiddharthaAsthana,PrateekDewan,AnupamaAggarwal,Srishti Gupta,Rishabh Kaushal,Anuradha Gupta

� Shrey Bagroy,Sonal Gupta,DivamGupta,Megha Arora,IndiraSen,NehaJawalkar,Bhavana Nagpal,TusharGupta,Vedant Swain

� MembersofCybersecurityEducationandResearchCentre(CERC)andPrecog whohavegivenuscontinuedsupportthroughouttheproject

� MyFamily68

Acknowledgement� Dr.Aaditeshwar Seth� Dr.CarlosCastillo� Dr.MauraConway� Dr.Ponnurangam Kumaraguru

69

Publications� PeerReviewedConferences

� Sachdeva,N.andKumaraguru,P.Online SocialMedia- Newfaceofpolicing?ASur- veyExploringPerceptions, Behavior,Challenges

forPoliceFieldOfficersandResidents.Acceptedat18thInternationalConferenceonHuman-Computer Interaction(HCII),2016.

� Sachdeva,N.andKumaraguru,P.Derivingrequirementsforsocialmediabasedcom- munitypolicing:insights frompolice.Accepted

atACM16thInternationalDigitalGovernmentResearchConference(dg.o 2015), 2015.

� Sachdeva,N.andKumaraguru,P.Online SocialNetworksandPoliceinIndia- Under- standingthePerceptions, Behavior,Challenges.

AcceptedattheEuropeanConferenceonComputer-Supported CooperativeWork (ECSCW), 2015.

� Sachdeva,N.andKumaraguru,P.Characterising BehaviorandEmotionsonSocialMediaforSafety:ExploringOnline

CommunicationbetweenPoliceandCitizens.Acceptedat30thBritishHumanComputer InteractionConference(HCI)2016.

� Goel,S.,Sachdeva,N.,Kumaraguru,P.,Subramanyam,A.,andGupta,D.PicHunt:SocialMediaImageRetrievalforImprovedLaw

Enforcement.Acceptedat8thInternationalConferenceonSocialInformatics.2016.

� Sachdeva,N.,andKumaraguru,P.SocialNetworksforPoliceandResidentsinIndia:ExploringOnline Communication forCrime

Prevention.AcceptedattheACM16thAnnualInternationalConferenceonDigitalGovernmentResearch(dg.o),2015.[Bestpaper

award].

� Sachdeva,N.,andKumaraguru,P.CallforService:CharacterizingandModelingPoliceResponse toServiceableRequestson

Facebook.AcceptedattheACMConferenceonComputer-SupportedCooperativeWorkandSocialComputing (CSCW), 2017.70

Publications� Peer-reviewedConferencePapers

� Lamba, H.,Bharadhwaj,V.,Vachher,M.,Agarwal,D.,Arora,M.,Sachdeva,N.,Ku-maraguru,P.FromCameratoDeathbed:Understanding

DangerousSelfiesonSocialMedia.11thInternationalConferenceonWebandSocialMedia(ICWSM),2017

� Mohamed,M.,Sachdeva,N.,Georgescu,M.,Gao,S.,Saxena,N.,Zhang,C.,Kumaraguru,P.,VanOorschot,P.,andChen,W.AThree-Way

InvestigationofaGame-CAPTCHA:AutomatedAttacks,RelayAttacksandUsability.Accepted at9thACMSymposiumonInformation,Computer

andCommunicationsSecurity(ASIACCS),2014.

� Sachdeva,N.,Saxena,N.,andKumaraguru,P.OntheViabilityofCAPTCHAsforUseinTelephonySystems:AUsabilityFieldStudy.16th

InformationSecurityConferenceNovember2013inDallas,Texas(ISC),2013.

� Sachdeva,N.,Saxena,N.,andKumaraguru,P.OntheViabilityofCAPTCHAsforUseinTelephonySystems:AUsabilityFieldStudy[Poster].

(APCHI)2013

� Ion,I.,Sachdeva,N.,Kumaraguru,P.,andCapkun,S.Homeissaferthanthecloud!privacyconcernsforconsumercloudstorage.InSymposium

onUsablePrivacyandSecurity(SOUPS)(2011).

� JournalPapers

� Manar Mohamed,SongGao,Niharika Sachdeva,Nitesh Saxena,Chengcui Zhang,Pon- nurangam Kumaraguru andPaulvanOorschot.Onthe

SecurityandUsabilityofDynamicCognitiveGameCAPTCHAs.InJournalofComputerSecurity(JCS),2017.

71

Thankyou!niharikas@iiitd.ac.in

http://precog.iiitd.edu.in/