Contextual Consent: Ethical Mining of Social Media for Health Research

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by  Chris Norval

Contextual Consent: Ethical Mining of
Social Media for Health Research
Chris Norval Tristan Henderson
SchoolofComputerScience SchoolofComputerScience
UniversityofStAndrews UniversityofStAndrews
StAndrews,Fife,UK StAndrews,Fife,UK
[email protected] [email protected]
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1 ABSTRACT college students [24]. Such findings can help researchers and
0
practitionersidentifyimportantmarkersinhealthandinsociety.
2 Socialmediaarearichsourceofinsightfordatamininganduser-
Despite these advantages, those who created the social data in
centredresearch,butthequestionofconsentariseswhenstudying
n questionarenotalwaysinformedabout theprocess. Whilesome
such data without the express knowledge of the creator. Case
a researchers outline clear methods of obtaining informed consent
studiesthatminesocialdatafromusersofonlineservicessuchas
J
when conducting research on social media data (e.g. [24]), only
Facebook and Twitter are becoming increasingly common. This
6 around5%oftheliteraturethatusesuchdatadescribetheirconsent
has led to calls for an open discussion into how researchers can
2 procedures[12].
bestusethesevastresourcestomakeinnovativefindingswhilestill
Some have argued that social data posted online are freely
respecting fundamental ethical principles. In this position paper
] availableforresearch[9]; however, othersarguethatjustbecause
C wehighlightsomekeyconsiderationsforthistopicandarguethat
personal information is made available online does not mean it
the conditions of informed consent are often not being met, and
H
is appropriate to capture and analyse [2, 4, 11, 35]. Conway
that using social media data that some deem free to access and
. and O’Connor argue that the potential challenge to privacy
s analysemayresultinundesirableconsequences,particularlywithin
occurs “not in the reading or accessing of individual materials
c thedomainofhealthresearchandothersensitivetopics. Weposit
[ that successful exploitation of online personal data, particularly (publicly available as they are), but rather in the processing and
for health and other sensitive research, requires new and usable disseminationofthosematerialsinawayunintended"[4].
1 Whileitisimportantforsuchresearchusingsocialmediadata
methodsofobtainingconsentfromtheuser.
v
tocontinue,theinformedconsentofallparticipantsisanimportant
5
milestonetostrivefor. Firstly,consentdecisionsarestronglytied
6 Keywords
to the expected audience [19, 22]. Research has suggested that
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ethics;privacy;health;consent;socialmedia users have differing online behaviour based on their perceived
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audience, including more self-censorship [5, 32], and sharing
0
1. INTRODUCTION smaller proportions of both positive and negative emotions [3]
.
1 whentheperceivedaudienceismoresparse,andlesscontrolled.
Socialmediaservices,suchasFacebookandTwitter,areanear
0 Secondly, research into privacy settings on social media have
ubiquitous part of people’s lives. Facebook, for example, boasts
7 suggested that most users may be significantly oversharing what
over 1.7 billion monthly active users [7]. As a result we are
1 isintended[18,20].Whenanalysingsocialmediadataonline,data
increasingly sharing more and more data about our lives online:
v: 68%ofUK-basedonlineadultslookatsocialmediasitesorapps canbetakenfromindividualswhoareunawarethatitisaccessible,
i each week, 35% upload photos or videos, 28% share links to andprivacyviolationsmaybeoccurring.Thelikelihoodofprivacy
X violationsincreaseswiththenumberofpeopleinvolved,withmany
websites or online articles, and 24% contribute comments to a
r websiteorblog[29]. suchstudiesindiscriminatelyscrapingthedataofhugenumbersof
a individuals[2],ornotevenreportinghowtheygatherdata[12].
The vast quantities of personal data shared through such
And finally, participants may find it intrusive to discover that
platforms can offer new insights into important areas of
theirdataisusedinawaywhichisoutsidetheboundariesofwhat
research [2]. Academics from various fields have capitalised
wasoriginallyexpected[4,19]. AnexampleofthisisSamaritans
on these data, leading to the comprehension of complex social
Radar, a well-intentioned Twitter app designed to monitor the
behaviours and trends in society. Within a health context
tweetsoftheuser’scontactsforpotentiallysuicidalmessages[16].
alone, such social data has been found to act as a predictor
This app received criticism from the Twitter community over
for depression [25, 30], suicide risk factors [6, 13], mood
privacyconcerns,andwasshutdowndaysafterlaunching.
changes [15], flu outbreaks [17] and problem drinking in US
Therefore,withallpreviouspointsconsidered,participantsmay
besharing moresensitiveandemotional content withafarwider
audience than intended, they may not have otherwise provided
consentfortheirdatatobestudied,andwouldlikelyfindaviolation
of this to be intrusive. This suggests a disconnect between the
researcher andtheparticipant; asituationinwhichtheautonomy
of the participant should be respected. Safer and more explicit
methodsofconsentarethereforenecessarytobestprotecttheneeds
ofpotentialparticipants,whileensuringthatwecanmakethebest
useofthevastquantitiesofsocialmediadataforhealthresearch. neededtomeettheethicalandlegalrequirementsforconsentwhile
Inthispaper, wetakethepositionthatnew formsof obtaining accomodatingthedynamicnatureofmodernresearch[14].
meaningful consent are necessary for successful online health Some of these concerns have been echoed by Morrison et
research.Weoutlinecurrentmethodsofobtainingconsent,discuss al., who found that few participants were aware of participating
new potential methods that seek to mitigate these problems, and in an academic trial, as specified in the Terms and Conditions
outlinesomeofthechallengesthatneedtobeovercomeforsucha document of a mobile application. Further means of informing
methodtobedeveloped. usersofresearchers’intentionstocollectandanalyseuserdataare
necessarytobehaveinanethicalmanner[26]. Suchfindingscall
intoquestionthesuitabilityofbroad,one-offmethodsofcollecting
2. BACKGROUND
consent when dealing with online research. As Steinsbekk et
Informedconsent,adeclarationthattheparticipantunderstands al. articulate, at the core of the debate is “what it means to
the consequences of participating in the study, is widely seen be ‘adequately informed’ and whether giving consent based on
as fundamental to conducting research with human participants broaderpremisesisvalidornot"[34].
[21]. From the point of view of the researcher, it is seen as
fulfilling ethical responsibilities with regards to the protection of 2.3 Dynamicconsent
data, privacy, and autonomy of the participant [26]. While there Researchershaveinvestigateddifferent mechanisms forcoping
appears to be broad agreement when it comes to interventional with the above issues, such as using dynamic consent [14, 27,
research,thesubjectofwhetherinformedconsentisnecessaryina 34]. Dynamic consent is a framework for allowing participants
studyofonlinecommunicationsisthesubjectofdebate[9,33].As togrant access totheir personal datatoresearchersinaway that
aresult,newmodelsforconsenthavebeenproposed[11,14]. theycancontrol. Forexample,aparticipantmaydecidetorevoke
access to their data, or customise their opt-in/opt-out preferences
2.1 User attitudes and theneed forconsent
forparticipatinginresearch.
Studies into user attitudes are often subject to selection bias, Kayeetal. arguethat,forparticipants,someoftheadvantages
given that participants have opted in and are therefore not of dynamic consent include the ability to easily consent to new
necessarilyrepresentativeofthewiderpopulation. Despitethis,it projects, alterconsentpreferencesinrealtime,findouthowtheir
isworthconsultingtheviewpointsofthosewhomaybepotentially datahasbeenused,andtosetpreferencesabouthowtheyarekept
affected by such research, and how they approach the trade-off informed. Researchersaresaidtogainmoreengagedparticipants,
between societal benefit and personal intrusion. Mikal et al. streamlinedrecruitment,improvedpublictrust,andtheknowledge
investigateduserattitudestowardtheanalysisofsocialmediadata thattheirresearchconformstohighlegalstandards[14].
forresearch,discovering“equivocalfindings"intheliterature[23]. While researchers have outlined the potential benefits of
Mikal et al. asked participants about their expectations of dynamic consent, Steinsbekk et al. dispute these, arguing that
privacy with regard to monitoring depression at the population a convincing case has not been made. Their criticisms of
level, finding that most were accepting of it [23]. This was, dynamicconsentincludemorefrequent(andthereforemoretrivial)
however, largely conditional on the analysis being conducted in requests for re-consent, and an increased risk of the relationship
an aggregated and anonymised way, with no way of targeting a between researcher and participant breaking down due to unmet
particular individual. This is echoed by Conway and O’Connor, expectations and a lack of reciprocity [34]. The authors argue
who argue the need for a differentiation between automatic that “broad consent combined with competent ethics review and
identificationattheindividual levelandatthepopulationlevelin an active information strategy is a more sustainable solution".
ordertoprotecttheprivacyofthoseinvolved[4]. Hutton and Henderson have also raised issues with dynamic
Mikal et al. found that “many respondents felt as though a consent, arguing that frequent requests for consent can lead to a
failuretoprotectonlinedataconstitutedconsenttohavethatdata significantly greater burden on the participant [11], which could
systematizedandanalyzed",however,theauthorsgoontoconsider frustrateparticipantsandleadthemtowithdrawfromtheresearch.
whatkindsofindividualswouldbelesslikelytoprotecttheirdata
[23].SuchamodelwouldleavethosewithlimitedInternetliteracy 2.4 Contextual integrity
skills or those mistakenly oversharing their social data at risk of Nissenbaum’s model of contextual integrity is a theoretical
unwittinglyprovidingimpliedconsent. framework that attempts to prescribe “specific restrictions on
collection, use, and dissemination of information about people”
2.2 Unsuitability ofinformedconsent
dependingonpresidingnormsofinformationappropriatenessand
Research has questioned thesuitability of informed consent as distribution[28].Contextualintegrityfocusesonwhetheraflowof
it currentlystands [11, 14, 19, 26, 27]. Luger and Rodden argue informationisappropriatewithinaparticularcontext,orwhethera
that consent, as currently conceived, cannot hope to meet the violationofprivacyhasoccurred[31]. Ithaspreviouslybeenused
challenges posed by ubiquitous computing systems, and tensions to explore privacy implications of social networking sites [1, 10,
existbetweenwhatisexpectedofconsentandwhatisexpectedof 11,31,35].
a ubiquitous computer system. They describe consent as having HuttonandHendersonhaveusedcontextualintegritytoexplore
been“stretchedthintothepointofbreaking"[19]. a new method of obtaining consent for social media research,
Kayeetal. arguethatcurrentproposals fortheEuropeanData described as ‘contextual integrity consent’ [11]. This middle-
Protection Regulations1 require explicit consent, questioning the groundapproachseekstheflexibilityofdynamicconsent,allowing
legality of broad consent methods, and that static systems for userstochoosewhatdataisaccessibleandwhen, whilereducing
obtainingconsentarenolongerfitforpurposeduetothechanging the burden of such data management. It works by inferring
natureofresearchandtechnology. Newapproaches aretherefore context-specific norms; a ruleset determining the appropriate
flow of information. Individuals are asked explicitly about their
1since the writingof that paper, the GDPR has been ratified and willingnesstoshareunlesstheyclearlyconformtoordeviatefrom
willcomeintoeffectin2018. such a norm. In situations where a violation is found to have
occurred,theframeworkrejectsthepracticeinquestionandfurther the concerns and interests of all parties. Due to the potential for
actionscanbetaken,suchasre-requestingconsentfromtheuser. sensitive topics and the extent in which a sharing violation may
These norms depend on a number of factors, such as what the impact individuals, it is important that steps are put in place to
dataare,towhomthedataareflowing,andforwhatpurposethey greatlyreducerisk. Suchapredictivemodelwilllikelyserveasa
are being requested. For example, McNeilly et al. found that recommender systemuntil thetechnique can beproven, therisks
participants of a location sharing study were more likely to give are understood, and confidence in such a method is developed.
the researchers access to their location data than a health study, Working closely with all relevant stakeholders will help us to
and in both studies, photos were much less likely to be shared documentandunderstandtherisksinvolvedandthechallengesthat
than ‘liked’ pages [22]. These norms can change over time, and willneedtobeovercome.
individuals, organisations, and sections of society can each have
theirownexpectationsofwhatisappropriate. 4. CHALLENGES
Oneofthelargesttechnicalchallengeswefaceisthecontextual
3. TOWARDS CONTEXTUAL CONSENT nature of consent over items of data; One shared status update
FORHEALTH or photo may be seen as appropriate for researchers to access
and utilise, whereas another may not be, making it difficult to
Much of the work in dynamic consent has studied mobile or designbroadrulesforwhatisacceptableandwhatisnot[19,20].
socialnetworkapplications.Anopenquestionishowtoapplythese Furthermore, the given rules for any one individual may change
techniquestominingonlinehealthdata. Ouraimistoinvestigate overtime,associalrelationshipsandopinionsevolve[18].
contextualconsentforhealth;canwecapture,interpret,andacton Participants may have concerns about sharing sensitive data
context-specific norms within a healthcare domain? Further, can with any autonomous system, given that it is an extension of the
weimproveoncontextualconsentbyblendingcontextualintegrity researcher. Thisraisesnewchallengesabouthowsuchaclassifier
with machine learning techniques? Finally, such techniques are couldbeused,oreventrained,ifsomeparticipantsarenotwilling
only useful if available to practitioners, and feedback from such to share certain data with researchers. Approaching this project,
deploymentswillinformfurtherdevelopmentoftoolsandmodels. wemuststrikeabalancebetweenaccuracyandappropriateness,as
By predicting when social media users find it appropriate to participantsmayormaynotbecomfortablewithhavingtheactual
share different types of data with different stakeholders, such as contentoftheirsocialdatabeingmined,however,itisunclearhow
researchers and clinicians, theconsent process will, firstly, better effectivepredictiontechniquescanbeusingmetadataalone.
reflect the context in which data were created, and secondly, Asignificantethicalconsiderationisthepotentialforerroneous
respect users’ preferences about which data should be made and unacceptable norm violations taking place as a result of this
availableandwithwhom. Forinstance,someoneseekingsupport research. In short, the sharing of a particular bit of data against
from their peers because they are anxious about an upcoming the will of the owner may erode trust and call into question the
medical procedure might not want this shared with medical entireproject. Suchasystemmaynot begivenasecond chance.
researchers, while others may want reports about side-effects of Ourinterimsolutiontothisistofocusonasystemwhichsuggests
theirmedicationtobeviewedbyclinicians. appropriate sharing levels to the user for approval, rather than
We are focusing specifically within a health context for three explicitlysharingthedataautonomously. Whilethisincreasesthe
reasons. Firstly, we believe that health data is an area where burden of sharing content, it would stillbelessburdensome than
presiding norms exist. Secondly, getting these expectations of asking theuser to specifically choose the acceptable audience on
appropriate data flow correct is important due to the potential eachbitofcontentshared,suchaswithgroups,lists,orcircles.We
sensitivity of the data. Finally, given the vast number of studies believethatthislimitationexistsinanysystemthataimstoreduce
usingsocialmediadatawithinamedicaldomain, webelievethis theburdenofconsentbyautomatingtheuser’sdecision.
workistimely,andthereisanopportunitytoprovideguidanceand Thecollectionofdataforsuchresearchwillalsolikelyproveto
tools for the appropriate handling of social media data in health beabarriertoovercome. Collectinganydatainanethicallyaware
researchtoacademicsandpractitionersalike. way will introduce selection bias. Obtaining large sample sizes
We are currently planning two studies to further this mayalsobedifficult.Webelievethatthischallengeisalsoinherent
investigation. The first will involve collecting a large corpus of in such research, although there may be ways to minimise the
data to use in a training and model evaluation phase. We will impactwithoutcompromisingtheprincipleofinformedconsent.
workwithparticipantstodeterminewhatthemainpredictorsareof
appropriatedataflowtodifferentmedicalstakeholders. Withthis
5. CONCLUSION
data, wewill then derive machine-learning models for predicting
consentwiththesedifferentstakeholders. Finally,wewillevaluate Peopleareincreasinglysharingmoreandmoreinformationvia
ourmodel’seffectivenessinafollow-upstudywithparticipantsin social media, and as a result, such platforms are seen as rich
asocialmediamedicalsupportcommunity. datasourcesforresearchersfromvariousfieldsandbackgrounds.
Gomer et al. propose a similar semi-autonomous method This can lead to interesting research and discoveries, however, it
of obtaining consent. Their proposed system trains a consent alsoraisessignificant questions over informed consent intheage
agent, usesthismodeltoreceiveandacceptorrejectrequestsfor of social media and data mining. We argue that the successful
participation, and allows users to review past decisions with the exploitationofonlinepersonalhealthdatarequiresnewandusable
resultsre-trainingthemodel[8]. Thismethoddiffersfromoursas methods of obtaining consent from the content creators. By
ittrainsandbuildsamodelonaperson-by-personbasis,ratherthan deriving a new and usable method of obtaining consent in such
buildingonthecollectivenormswhichshapecontextualintegrity. circumstances, we hope that researchers can continue safe in the
Assuch,itwouldrequireusertrainingbeforeitcouldbeused. At knowledgethattheresearchistransparentandtheparticipantsare
thetimeofwriting,nofollow-upworkhassincebeenpublished. informedabouthowtheirdataisbeingusedandwhy.
Throughout this research, we aim to engage with clinicians, We are currently planning studies to explore the contextual
members of support communities and researchers to understand normsofdatasharingwithinthecontextofhealthresearchinorder
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