Presentation at the Infrastructuring eReserch Siegen, Dec 07 2016 based on joint work with Liliana Bounegru and Jonathan Gray

1. Carolin Gerlitz
(based on joint work with Liliana Bounegru & Jonathan Gray)
University of Siegen – Digital Methods Initiative Amsterdam
Infrastructuring eResearch Workshop, Dec 7 2016
DE- & REASSEMBLING DATA
INFRASTRUCTURES

2. DIGITAL METHODS
Widely used term.
Repurposing of (1) digital data (2)
technical features (3) analytical
capacities.
How can links, likes, shares, comments
etc. be used for research? (Rogers
2013)

3. DIGITAL METHODS
Structured extraction & analysis of data.
Two main objectives: (1) study sociality
online (2) understand medium
specificity and socio-technical
configurations
Tool development, repurposing &
training.

4. EXAMPLE: TCAT
Twitter Capture and Analysis Toolkit
(TCAT).
Developed by Erik Borra & Bernhard
Rieder.
Data collection & analysis of Twitter data
based on Streaming API.

5. DE-ASSEMBLING
Digital methods rely on the
participation of a variety of actors
and entities.
Data & tool chaining.
What are the data infrastructures
that underpin dm work? What
challenges do they pose?

6. DATA PRODUCTION
Data production as distributed
accomplishment of users,
platform activities, capture
mechanisms, third party apps &
cross-platform syndication.

7. (1) COMMENSURATION
Cross-platform syndication, different
interpretation of platform features,
bots & automation.
How to commensurate data from
heterogeneous sources
(Espeland & Stevens 1998)?
s

8. (2) MULTIVALENT YET
BIASED
Data set out to cater to different
analytical interests of
stakeholders.
At the same time: support some
forms of analysis more than others
(interestedness).

9. DATA EXTRACTION
Scraping, crawling, API retrieval.
Reliant on platform data structures
and API politics, tools, plugins
and scripts.
Platforms determine the conditions of
access to their data.
Instagram Hashtag Explorer

10. DATA ANALYSIS
Reliant on further tools for querying
data, calculating metrics, stats or
combination of data formats.
DMI TCAT

11. (3) METHODOLOGICAL
UNCANNY
Open or commercial tools resonate
with known methods – but not
quite (Marres & Gerlitz 2015).

12. DATA VISUALISATION
Visualisation standards and data
outputs.
Which data formats are amenable for
which visualisation technique?
What interestedness does
visualisation introduce?
D3, tableau, Gephi

13. (4) TOOL CHAINNG
Assembling of different data sources
& tools for different tasks into a
methodological apparatus.
Cascades of inscriptions (Ruppert
et al 2013).

14. (5) DISTRIBUTED TOOL
MAKING
Many general purpose tools (incl.
extensive documentation).
Heterogeneous developers and
emergent standards.
Which tools can be chained?
How can open source tools be
maintained and scaled up?

15. (6) DATA PUBLICS
Data assemble heterogeneous publics
with different objectives, interests,
skills & needs (Ruppert 2015,
Birchall 2015).
Researchers, companies,
organisations, activists, journalism.

16. ALLINGING DATA
INFRATSRUCTURES
Methodological work as de- &
reassembly.
Specific to needs of publics.
Alignment & mal-alignment of
data sources, tools,
visualisations and research
objectives: need for
repositories and shared dev.

17. (RE)IMAGINING DATA
INSTRUCTURES
From data literacy to data
infrastructure literacy (Gray et
al. 2017).
Accounting for inscription, alignment
and malalignment.
Enable to re-think, re-assemble and
re-align infrastructures.
Methodological infrastuctural
imagination (Bowker 2014).

18. ANTWORT
:
SITUIERTE
ALGORITH
MEN
THANK YOU.
carolin.gerlitz@uni-siegen.de