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 single- platform 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/mapping-

movements/

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

• Twitter: @scroeser | @timhighfield