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Mapping Movements
Social movement research and big data:
critiques and alternatives
Sky Croeser
Curtin University, Australia
skycroeser.net // @scroeser
Tim Highfield
Curtin University + Queensland University of
Technology, Australia
timhighfield.net // @timhighfield
Principles for
social movement research
Research should:
• Be relevant and useful to movements.
• Protect participants from harm.
• Be accessible to activists.
• Make biases visible.
• Be open to questions from, and discussion
with, activists.
• Provide empirically-grounded analysis.
Big data and the
researcher-movement relationship
• Big data research does not
require presence in social
movement spaces.
• Quantitative research is often
seen as more 'true'.
• The structure of academia
encourages research designs
that allow for swift
publication.
Mapping Movements
Looking at how social movements
are using new media.
• Case studies:
• Occupy Oakland;
• the Tunisian WSF;
• antifascist activism in
Athens.
• Methodology:
• Quantitative + qualitative.
• Online + offline.
The relevance and use of research
• Does big data research relieve us of an
obligation to repay activists for their time?
• Does big data research make it more difficult
to identify movement priorities?
Protecting participants
• Big data research has the
potential to open activists
to unforeseen risks, even
when working with data
that is already open.
Research should be accessible
• Is there a commitment to writing in an
accessible way?
• Can activists access the tools which
researchers are using for big data work?
• Can activists interpret and question big data
methodologies?
Researchers should have
a clear political stance
• Activists want to know who they are dealing
with before allowing access to their
movement
• Big data research
removes their
ability to do this.
Activists as experts
• (How) do we make space for activists' (more
detailed and grounded) knowledge in big data
research?
• Does big data research commit to 'nothing
about us without us'?
• How do cultural assumptions about big data
as hard science contribute to the divide
between researchers-as-subjects and
activists-as-objects?
Issues with accuracy
• Does having a clear political
stance undermine the
‘objectivity’ of the
researcher?
• Big data itself does not
(cannot) represent a wholly
accurate resource, either.
The biases of big data
• Regardless of the size of the data captured,
the dataset is not everything, nor is it
representative of the entire social movement.

• Biases associated with singleplatform studies, particularly
Twitter – over-representation
within research, based on
access, tools, ethical approvals.
Big data blind spots
• Not everyone in the movement is online (for
various reasons), and the impact this has on
the shape of the online discussion as opposed
to the physical movement.
• What is posted on social media is being
framed, coded, censored by participants
based on the wider context which may be
unknown to the researcher at a distance.
Limits of data
• Raw numbers and representativeness.
• Unused features and features tracked/not
tracked by tools used.
• Means of capture (hashtags, keywords, but
discussions range beyond).
• Noise and spam.
• Access to the platform in question.
How big data can help us
• Rich datasets add nuance to our
understanding of social movements.
• Identify phenomena around online aspects
of the movement, patterns of activity.
• Provide the means for examining how
movement occurs online.
• Such examinations are vital for understanding
how social movements make use of the
internet.
By their powers combined
• When the movement has physical and digital
forms, then understanding both – and their
context – is crucial.
• Researchers can study not only who is
involved in the movement online, but also
who is not and why, including perspectives
which are unlikely to be
visible or obvious from
online data alone.
Mapping Movements
• Sky’s research blog:
• http://skycroeser.net/tag/mappingmovements/

• Tim’s research blog:
• http://timhighfield.net/?cat=49
• Twitter: @scroeser | @timhighfield

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Mapping Movements: Social movement research and big data: critiques and alternatives

  • 1. Mapping Movements Social movement research and big data: critiques and alternatives Sky Croeser Curtin University, Australia skycroeser.net // @scroeser Tim Highfield Curtin University + Queensland University of Technology, Australia timhighfield.net // @timhighfield
  • 2.
  • 3. Principles for social movement research Research should: • Be relevant and useful to movements. • Protect participants from harm. • Be accessible to activists. • Make biases visible. • Be open to questions from, and discussion with, activists. • Provide empirically-grounded analysis.
  • 4. Big data and the researcher-movement relationship • Big data research does not require presence in social movement spaces. • Quantitative research is often seen as more 'true'. • The structure of academia encourages research designs that allow for swift publication.
  • 5. Mapping Movements Looking at how social movements are using new media. • Case studies: • Occupy Oakland; • the Tunisian WSF; • antifascist activism in Athens. • Methodology: • Quantitative + qualitative. • Online + offline.
  • 6. The relevance and use of research • Does big data research relieve us of an obligation to repay activists for their time? • Does big data research make it more difficult to identify movement priorities?
  • 7. Protecting participants • Big data research has the potential to open activists to unforeseen risks, even when working with data that is already open.
  • 8. Research should be accessible • Is there a commitment to writing in an accessible way? • Can activists access the tools which researchers are using for big data work? • Can activists interpret and question big data methodologies?
  • 9. Researchers should have a clear political stance • Activists want to know who they are dealing with before allowing access to their movement • Big data research removes their ability to do this.
  • 10. Activists as experts • (How) do we make space for activists' (more detailed and grounded) knowledge in big data research? • Does big data research commit to 'nothing about us without us'? • How do cultural assumptions about big data as hard science contribute to the divide between researchers-as-subjects and activists-as-objects?
  • 11. Issues with accuracy • Does having a clear political stance undermine the ‘objectivity’ of the researcher? • Big data itself does not (cannot) represent a wholly accurate resource, either.
  • 12. The biases of big data • Regardless of the size of the data captured, the dataset is not everything, nor is it representative of the entire social movement. • Biases associated with singleplatform studies, particularly Twitter – over-representation within research, based on access, tools, ethical approvals.
  • 13. Big data blind spots • Not everyone in the movement is online (for various reasons), and the impact this has on the shape of the online discussion as opposed to the physical movement. • What is posted on social media is being framed, coded, censored by participants based on the wider context which may be unknown to the researcher at a distance.
  • 14. Limits of data • Raw numbers and representativeness. • Unused features and features tracked/not tracked by tools used. • Means of capture (hashtags, keywords, but discussions range beyond). • Noise and spam. • Access to the platform in question.
  • 15. How big data can help us • Rich datasets add nuance to our understanding of social movements. • Identify phenomena around online aspects of the movement, patterns of activity. • Provide the means for examining how movement occurs online. • Such examinations are vital for understanding how social movements make use of the internet.
  • 16. By their powers combined • When the movement has physical and digital forms, then understanding both – and their context – is crucial. • Researchers can study not only who is involved in the movement online, but also who is not and why, including perspectives which are unlikely to be visible or obvious from online data alone.
  • 17. Mapping Movements • Sky’s research blog: • http://skycroeser.net/tag/mappingmovements/ • Tim’s research blog: • http://timhighfield.net/?cat=49 • Twitter: @scroeser | @timhighfield