The document discusses the ethics surrounding digital media and internet research, emphasizing the importance of ethical responsibility in data collection and analysis. It highlights the need for researchers to navigate challenges such as privacy, data security, and ethical decision-making in online contexts. The conclusion advocates for a process-oriented approach that adapts to the evolving nature of digital research ethics.

1. REVISITING DIGITAL
MEDIA & INTERNET
RESEARCH ETHICS
A Process Oriented Approach
NELE HEISE, M.A. | @NELEHEISE
WORKSHOP ”RESEARCH ETHICS FOR DATA AND DIGITAL METHODS”
NOVEMBER 29, 2016 | DATA SCHOOL UTRECHT

2. Hello.
 Media Researcher at the University of Hamburg +
Graduate School Media and Communication Hamburg;
 forms, practices and conditions of media participation in
the digital age, as well as ethics of online communication
and digital media, e.g. Big Data, algorithms
 Master thesis: »Ethics of Internet Research« (2011)

3. TODAY
DIGITAL METHODS, DATA AND ETHICS
A PROCESS ORIENTED APPROACH
CONCLUSION
WHY BOTHER ABOUT ETHICS?

5. WHY BOTHER ABOUT ETHICS?

6. August 9, 1945HTTP://WWW.ARCHIVES.GOV/RESEARCH/MILITARY/WW2/PHOTOS/IMAGES/WW2-163.JPG

7. »The social sciences can […] have a
tremendous impact on society, even to
the point of revolutionizing our
conceptions of human nature, society,
and culture.«
DIENER & CRANDALL [1978: 195]

8. HTTPS://WWW.THEGUARDIAN.COM/SCIENCE/HEAD-QUARTERS/2014/JUL/01/FACEBOOK-CORNELL-STUDY-EMOTIONAL-CONTAGION-ETHICS-BREACH
Corporate vs.
Academic

9. HTTP://MOTHERBOARD.VICE.COM/READ/70000-OKCUPID-USERS-JUST-HAD-THEIR-DATA-PUBLISHED
»The data may be ‘public’
(though it must require login
and agreement to a [terms of
service]) but that does not
absolve anyone from an
ethical responsibility.«
R. Munksgaard

10. HTTPS://TWITTER.COM/SCOTT_BOT/STATUS/730508560778530816

11. HTTPS://ARXIV.ORG/ABS/1611.04135
AI Research
& Ethics

12. HTTPS://THEINTERCEPT.COM/2016/11/18/TROUBLING-STUDY-SAYS-ARTIFICIAL-INTELLIGENCE-CAN-PREDICT-WHO-WILL-BE-CRIMINALS-BASED-ON-FACIAL-FEATURES/
AI Research
& Ethics

13. AI Research
& Ethics
HTTPS://TWITTER.COM/NATEMATIAS/STATUS/802291972488757252

14. HTTPS://TWITTER.COM/TECHREVIEW/STATUS/800734306998120448
Algorithm
Ethics
HTTPS://TWITTER.COM/TECHREVIEW/STATUS/800734306998120448

15. Ethical challenges and questions, according to The Alan
Turing Institute & Oxford Internet Institute:
 how data is generated, recorded, shared (ethics of data)
 how artificial intelligence, machine learning and robots
interpret data (ethics of algorithms)
 devising responsible innovation and professional codes
to guide this emerging science (ethics of practice)
 See e.g. special issue of Philosophical Transactions A on “the ethical impact
of data science” (ed. by M. Taddeo & L. Floridi)
HTTPS://WWW.TURING.AC.UK/NEWS/WHAT-IS-DATA-ETHICS/
Data Science &
Ethics

17. SOME BASICS

18. RESEARCH ETHICS: KEY ELEMENTS
Ethics are guidelines and principles that help us to
uphold our values – to decide which goals of research are
most important and to reconcile values and goals that
are in conflict. Ethical guides are not simply prohibitions;
they also support our positive responsibilities.
DIENER/CRANDALL [1978: 3]
(shared) valuesdecision-making responsibility

19. LEVELS OF ETHICAL REASONING
cf. STROHM KITCHENER & KITCHENER [2009]
critical-evaluative
immediate-situational
Meta-Ethics
Ethical Theories
Ethical Principles
Ethical Rules
»ordinary moral sense«

20.  Ethics as safeguard for good scientific practice,
based on trust, scientific freedom, …
 responsibilities regarding
 employees, colleagues, team members, peers,
scientific community, … [internal]
 participants, society, clients etc. [external]

21. ETHICAL PRINCIPLES & STANDARDS
 Core principles:
 Human Dignity,Autonomy (self-determination, agency)
& Respect for Persons
 maximization of benefits and minimization of harms
(Nonmalificence & Beneficence), Justice
 Protection, Safety & Fidelity
 Research standards in regard to participants
– Voluntariness, informed consent
– Anonymity, Privacy & Confidentiality
– »Do no harm« (avoidance of potential risks)

22. DIGITAL METHODS,
DATA AND ETHICS

23. »virtual worlds, as sites for
meaningful social interaction, also
tend to be sites where meaningful
ethical harm can occur […].«
VANACKER & HEIDER [2012: 83]

24. »Internet research encompasses inquiry that:
a) utilizes the internet to collect data or information, e.g., through online interviews,
surveys, archiving, or automated means of data scraping;
b) studies how people use and access the internet, e.g., through collecting and
observing activities or participating on social network sites, listservs, web sites,
blogs, games, virtual worlds, or other online environments or contexts;
c) utilizes or engages in data processing, analysis, or storage of datasets, databanks,
and/or repositories available via the
d) studies software, code, and internet technologies
e) examines the design or structures of systems, interfaces, pages, and elements
f) employs visual and textual analysis, semiotic analysis, content analysis, or other
methods of analysis to study the web and/or internet-facilitated images, writings,
and media forms.
g) studies large scale production, use, and regulation of the internet by governments,
industries, corporations, and military forces.«
MARKHAM & BUCHANAN [2012: 3f.]

25. CHALLENGES OF DIGITAL RESEARCH METHODS
 De-contextualization (space/time) and global reach
of research and data
 Data structures: networked, persistent, searchable, …
 Informational constraints: disembodiment,
communicative distance, degree of social presence,
anonymity (verification/authentication)
 Blurring boundaries of publicity and privacy (data,
spaces) and hybrid roles of the researcher
 Legal gray areas and insecurities (no best practices)

26. e. g. frictionless sharing and »contextual integrity« [NISSENBAUM: 2004]
[DE-]CONTEXTUALIZATION

27. STAKEHOLDERS, ACTORS, RELATIONS
Codes/Guidelines
Intermediaries
MCKEE & PORTER [2009: 17]

28.  personal rights (of others) and handling of
personal identifiable information; right to
informational self-determination (in Germany)
 data security and privacy (national, EU)
 scientific freedom; science/education related laws
 third parties and providers, e.g. Terms of Use
LEGAL CONTEXT

29. Many ethical conflicts in online / digital
media research occur due to violations of
communication norms and principles (e.g.
reciprocity, authenticity, personhood)
online research = communication

30. Research
 Ethical rules (codes), standards and principles
 Technical / methodological requirements
 Research experience, practices and object of study
Use  (Online) communication ethics
 (In)formal rules of play (e.g. netiquettes)
 Media literacy/competence
 Technical and social frames of media practices
 Characteristics of digital media environments
 Terms of Use and providers’ rights
 Ethical position & paradigm
 Values (trust, responsibility, accountability)
hybrid contexts
DIGITAL MEDIA RESEARCH CONTEXTS

31. CONTEXTUAL DETAILS MATTER
 Object(s) of analysis: texts, aggregated bits of information,
or the persons themselves?
 use expectations of the online site and participants?
 sensitivity of the information collected?
 ages, geo-cultural-political affiliations, and/or
technological expertise of the online participants?
 In what form are the researchers collecting data, and in
what forms are they re-distributing it?
 Is the researcher using real names or real user/avatar
names, quoting passages, taking screenshots, etc.?
 And where will this material appear and to whom will it be
accessible?
MCKEE & PORTER [2009: 7F.]

32.  Type of Data
 Type of Venues
and/or Contexts
 set of questions to
reflect and evaluate
ethical practice
AoIR Guidelines [2012]

33. PRACTICAL JUDGEMENT
IN SPECIFIC CONTEXTS
[›PHRONESIS‹]
HTTP://S2.QUICKMEME.COM/IMG/2C/2CF739C4AAC76C9EFF549E105
390EB22B899CBF161784E671A1F50AD6E63040F.JPG

34. A PROCESS ORIENTED APPROACH

35. HTTP://CT.FRA.BZ/OL/FZ/SW/I54/5/10/3/FRABZ-ONE-DOES-NOT-SIMPLY-START-THE-PROJECT-WITHOUT-CONSIDERING-ETHICS-9E0B60.JPG

36. »Different ethical issues become salient as the
researcher develops research questions, seeks
and gains access to individuals and/or
information, manages and protects personally
identifiable information, selects analytical
tools, and represents the data through
dissemination, in published reports,
conference presentations, or other venues.«
MARKHAM & BUCHANAN [2012: 4]

37. HTTPS://WWW.FLICKR.COM/PHOTOS/LLAMNUDS/8543376308/
Research Question
Research Design
Data Collection
Data Analysis
Publication/Dissemination

38. Initial Stage Data Collection Data Analysis Publication
ACCESS TO SPACES & DATA
ACCESS TO
PARTICIPANTS
VISIBILITY / AUTHENTICITY STRATEGIES
ANONYMITY VS
AUTHORSHIP
ANONYMIZATION STRATEGIES
(INTER)ACTION VS.
ARTIFACTS
VOLUNTARINESS / INFORMED CONSENT
DATA SECURITY / STORAGE
DO NO HARM-PRINCIPLE
TRANSPARENCY | DISCLOSURE | RECIPROCITY
INHERENT
BIASES

39. When in the research process – and by whom
– is consent required?
 Object of Study: actions/practices vs
artifacts/text
 Accessibility and/or sensitivity of data and
information
 Expectations of the users, e.g. regarding
publicity/privacy of information/behaviour, …

40. OBTAINING INFORMED CONSENT
Visual heuristic to support ethical decision-making; in: MCKEE & PORTER [2009: 132/136]

41. »it is not only […] a technology, an infrastructure, which
is simply there–it is appropriated and has a specific
meaning to us. If you are going to MySpace you do
different things than on Facebook […] these are very
different spaces or Lebenswelten, with different
functions and meanings. Once you acknowledge that […]
we as researchers have to take a certain position: not to
sniff around and observe everything because it is easily
accessible, but instead to be aware of the fact that these
spaces are made by people for themselves.«
PhD Candidate studying self-representation in SNS; in: HEISE [2013]
USERS’ PERSPECTIVES

42.  Goal: protecting individual/group privacy and
personal identifiable information
 Potential risks: de-anonymization due to
traceability of data, …
 Possible approaches: »Data Fabrication«
[MARKHAM 2012], »visual paraphrases«, word
clouds etc.
Which degree of anonymization is required?

43. PUBLICATION OF RESULTS / DATA
in: HEISE & SCHMIDT [2014]

44. CONCLUSION

45. »There cannot be a blanket,
whole cloth approach to Internet
Research ethics.«
MCKEE & PORTER [2009: 7]

46. Process oriented ethical perspective as …
 basis of research design and (individual/group)
practice  sensitivity, deliberative process
 means to improve your research, e.g. in regard to
project management (planning/adjusting), quality
of methods/data, impact (trust, responsibility),
transparency, accountability
 flexible, adjustable case and context sensitive
framework to anticipate, address and evaluate
(potential) ethical dimensions of research

47. RESOURCES
Guidelines
 MARKHAM/BUCHANAN [2012]: AoIR Recommendations (aoir.org)
 NESH [2015]: Ethical Guidelines for Internet Research (etikkom.no)
 ESOMAR/GRBN [2015]: Online Research Guideline (esomar.org)
 BRITISH PSYCHOLOGICAL SOCIETY [2013]: Ethics Guidelines for Internet-
mediated Research
Literature
 ALIM [2014]: Ethics & automated data retraction from Social Media
 ZIMMER [2010]: Ethics & Facebook, private/public data, publication
 MCKEE/PORTER [2009]: Ethics of Internet Research (case based approach)
 MARKHAM/BAYM [2009]: qualitative Internet methodology (challenges)
 HEISE/SCHMIDT [2014]: ethics of online research [GER]
 DZEYK [2001]: ethical dimensions of online research [GER]
 RIVERS/LEWIS [2014]: Ethical research standards in a world of big data.

48. Nele Heise, M.A.
University of Hamburg | Journalism and Mass
Communication Studies
Graduate School Media & Communication Hamburg
@neleheise
nele.heise@wiso.uni-hamburg.de
THANK YOU.

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