Ethics issues in internet-mediated research

Internet-mediated Research: State of the
Obtrusive methods (questionnaires, experiments,
interviews, focus groups)
Unobtrusive methods (observation, data mining,
web crawling, 'big data')
A New Era of IMR?
Web 2.0 - The web as organic, collaborative, interactive, everchanging (e.g. Wikipedia); the rise of social media (e.g.
Facebook, Twitter, YouTube)
The Web as pervasive
Permeation into everyday lives – Shopping, banking,
dating, socialising, information-seeking, media sharing,
publishing (e.g. blogs), information storing, apps, the 'internet of
The self-documenting, self-archiving features of
the present day internet; 'digital breadcrumbs' of
contemporary life
Data Trace Examples
Discussion forums (e.g. see Yahoo groups)
Online communities (e.g. mumsnet; gransnet)
Google searches; tweets; web browsing activity;
purchases; social network links; geolocation data
The Visualisation of Complex
Nike+ involves the
placement of a sensor
underneath the footbed
of your Nike running
shoe in order to collect
data about where
you've run, how long it
took and where you
can improve over time.
Visualisation of a year's worth of runs uploaded to the Nike+ website. It plays back runs
throughout three cities: New York, London and Tokyo. The runs showed tens of thousands
of peoples' runs animating the city and bringing it to life. The software visualizes and
follows individual runs, as well as showing the collective energy of all the runners,
defining the city by the constantly changing paths of the people running in it.
Ethics in Online Research
Some ethics issues require special consideration in
an IMR context, the complexities of which are not
always obvious to researchers or participants
e.g. Data security, traceability (confidentiality,
The new BPS guidelines (November 2013)
Document aims to outline some of the key ethics
principles for researchers and ethics committees to
keep in mind when designing and / or evaluating
an IMR study
Emphasis on how to apply existing ethics
principles in an IMR context
“Thinking is not optional”
The new BPS guidelines (November 2013)
Centred around the four main principles of the BPS
'Code of Human Research Ethics' (2011)
1. Respect for the autonomy and dignity of persons
2. Scientific value
3. Social responsibility
4. Maximising benefits and minimising harm
Key feature of IMR: involves acquisition of data
from or about individuals in absence of face-toface co-presence
COUPLED WITH greater scope for carrying out
quite complex interactive procedures
Traceability of online data
Main ethics issues to consider in IMR
Public-private domain distinction
Consent; withdrawal; debrief
Levels of control
Disruption of social structures
Privacy online
CEC: Observation of public behaviour only where those
observed 'would expect to be observed by strangers'
What are people's expectations in online contexts?
Public-private domain distinction becomes increasingly
Go away! Chat room observation
experiment (Hudson & Bruckman, 2004)
Compared asking to record
entering and 'lurking' in chat rooms.
Both produced hostile responses and being 'kicked
But less so when just lurking
“We are researchers recording this chatroom for a study on
language use in online environments. For questions or comments,
email [email protected] Thank you!”
Public-Private Domain
Case Studies
Brotsky & Giles (2007) Posed as plausible persona
on pro-anorexia websites to gather data (participant
Fox, Ward & O'Rourke (2005) As above, but with
full disclosure
Tackett-Gibson (2008) Intended full disclosure but
was blocked from doing so by moderators
Unobtrusive 'data-scraping' methods
'Big data', e.g. web searches, browsing activity,
tweets, etc.
People may not be aware of the extent of logging of
their online behaviours
Online behaviours leave permanent traces in ways
not typical offline
How do researchers / RECs approach use of such
Undisclosed, unobtrusive observation
Key principles
Where the public-private nature of data is unclear,
assess likely levels of harm resulting from the
Consider scientific value of the research
Protect confidentiality (anonymity)
Key principle: Implement ethics procedures and safeguards to
be proportional to levels of risk of harm
Legal Considerations
Copyright remains with author / web service provider
Strictly speaking 'in the public domain' means not
protected by copyright law
Under the DPA (1998) if someone finds their personally
identifiable data is being used (e.g. discussion posts) they
likely have the right to stop these data being processed
Confidentiality, anonymity
Enhanced traceability of
implications, use of quotes
Other data leakage risks, e.g. email
Traces on personal computing equipment
Sensitivity of data is a factor associated with levels
of risk of harm
Researchers should carefully assess risks, and
perhaps inform participants of these risks
Valid Consent
Required where undisclosed approaches cannot be justified
Verifying characteristics (e.g. age, sex)
Engagement with consent process (check boxes?)
Adequate information – not all risks online are obvious,
e.g. potential data leakage (balance between information
and overload)
Risks will be higher in some contexts than others – key
principle of proportionality of ethics procedures to levels
of risk of harm re-emerges
Withdrawal and Debrief
Robust withdrawal procedures may be more difficult to
implement online (e.g. volunteer web survey)
Detecting withdrawal may be harder (use of partial data?)
Ensuring debrief difficult (e.g. exit by closing browser)
Enhanced risk in cases of deception (no debrief)?
Principle of proportionality of ethics procedures to levels
of risk (of harm) re-emerges, e.g. effects of withdrawal
without debrief (such as in very sensitive research)
Data Protection
UK Data Protection Act (1998)
Issue of storing data on non-UK/EU servers?
Popular software packages (e.g. SurveyMonkey, Qualtrics)
are US-based
'Safe Harbour Scheme'
Office of National Statistics
SurveyMonkey, Safe Harbour Scheme
Levels of control
Over procedures, who takes part, participant behaviours...
Implications for scientific value (validity)
Implications for levels of harm (monitoring participant
reactions, presenting debrief info., etc.)
Disruption of social structures
Especially relevant to observing online communities
May justify non-disclosure
Case study: PhD study involving non-disclosed
observation of small support group (REC approved)
Deception issues (posing as a group member)
Maximising Benefits, Minimising Harm
These considerations permeate the other issues
Careful assessment needed; trade-offs will be inevitable
(e.g. anonymity versus traceability/verifiability; disclosure
versus social disruption/harm)
“Thinking is not optional”