1.
TrISMA & Instagrammatics:
Framing & Balancing Big Data for
Humanities Research
Dr Tama Leaver, Curtin University (@tamaleaver)
Department of Internet Studies
Centre for Culture and Technology (CCAT)
(& Dr Tim Highfield, QUT (@timhighfield)
Social Media Research Group)
The Social Life of Big Data 2015 Symposium
Perth Zoo Convention Centre 2 June, 2015

2.
Overview
1. TrISMA.
2. Instagrammatics: A Method for Collecting
Media from Instagram
3. Project Context: The Ends of Identity
4. Breaking down #ultrasound

3.
[1] TrISMA

4.
Tracking Infrastructure for Social
Media Analytics [TrISMA]
• ARC-funded infrastructure cooperatively developed
by QUT (lead), Curtin, Swinburne & Deakin
universities.
• Twitter: full snapshot of all Australian Twitter use
• Facebook pages: ongoing scraping.
• Instagram: tag scraping, and attempt to ‘map’
Australian use (via geotags).
• Backed by Google BigQuery (for speed, breadth).
• Bespoke tools for ease of queries and analysis.
 Ready to go tools and data, to facilitate
Australian-centric research.

5.
[2] Instagrammatics: A Method for Collecting
Media from Instagram

6.
Building from studies using Twitter
• To map and track social media use, we start
with established methods for studying
Twitter.
• Topical datasets, using similar methods
around varied subjects, including:
– Breaking news
– Politics
– Crises
– Popular culture
– Sports

7.
Twitter data
User name
Tweet
Hashtag
Link
Date and time
@mention

8.
#hashtags on Twitter
• “… a way of indicating textually keywords or
phrases especially worth indexing… by using the #
character to mark particular keywords, Twitter
users communicate a desire to share particular
keywords folksonomically.” (Halavais, 2013, p. 36)
• Hashtag use has evolved over time to serve
additional, less organisational functions: humour,
meta-commentary, emotion…

9.
Tags and social media
• Tagging did not originate with Twitter,
although a prominent aspect of how users
tweet.
• Tags and hashtags used on other social media,
although functionality, adoption, and
intentions vary.
– Instagram vs. Pinterest vs. Facebook…

10.
The Twitter dilemma
• Does the comparative ease of access to data
and the use of common methods mean that
Twitter is over-represented in research?
• There are methodological challenges of
comparing Twitter – as a series of
(predominantly) fixed data points – with
Instagram and other more dynamic social
media data, as well as comparing text and
image/video/sound content.

11.
Twitter vs. Instagram
• Advantages of Twitter:
– Established capture and analytics methods;
– Public data;
– Consistent data (140 character limit);
– Primarily textual data (processing and analysis).
• Methods for large-scale tracking and analysis
of Twitter are well-established, but not yet for
other social media, including Instagram.

12.
Instagram data
Creator user
name
Image/
video Caption
Likes
Comments
Tag
@mention
Date/time

13.
Tracking Instagram activity
• Our initial approach builds on Twitter-specific
work and tools, which allows for comparative
analysis (methods and content).
• The starting focus is on #tags – practices,
functions, coverage of the same topic/tag,
including across different platforms.
• See Highfield and Leaver, 2015.

14.
Prototype Instagram methods
• Following the Twitter analytics model of
querying for specified keywords/hashtags,
query Instagram API for similar tag-specific
results.
• The tag search query retrieves data including:
media id, media type, user id, user name,
caption, image/video links, time and date,
location data, tags, comments (count and
content), likes (count).

15.
Changing data
• Unlike Twitter, content posted on Instagram is not
static.
• A photo or video posted can be added to by the
original user and others viewing the file.
– Liking, adding comments, replying to previous comments.
• Rather than creating standalone data, comments are
additions to the existing image – attached to this
specific data point, not in isolation.
• Additional contributions may be made to these files
hours, days, months after the fact.
•  When should we ‘capture’ the data? (How long until
comments typically finish, for example?)

16.
Authorship and intentions
• Comments also impact upon what is being
tracked and captured.
• Tracking specific tags through the Instagram
API returns media where the creator has, in
the process of publishing the content,
included these tags in their caption.
• However, it also includes media where a
follow-up comment includes these tags
(although this can later be filtered out).

17.
NB: Privacy isn’t a binary …
Individual and cultural definitions and expectations of
privacy are ambiguous, contested, and changing. People
may operate in public spaces but maintain strong
perceptions or expectations of privacy. Or, they may
acknowledge that the substance of their communication
is public, but that the specific context in which it appears
implies restrictions on how that information is -- or ought
to be -- used by other parties. Data aggregators or search
tools make information accessible to a wider public than
what might have been originally intended.
(Markham & Buchanan, 2012, p. 6)

18.
Contextual Integrity in Ethics
• Instagram may be experienced as private or partially
private in everyday use (contextually), despite being
public at a technical level (via the API).
• The shift from an iPhone only app to Android and
Windows phone, plus web profiles makes Instagram
photos more and more public.
• Researchers have to weigh intentionality in sharing,
not just technical publicness (“it’s freely available
online”).
• We need to move from ‘public vs private’ to
questions of surfacing and amplification.

19.
Development, ownership and privacy
Table 1. Instagram Timeline
16 October 2010 Instagram app launched via Apple’s App Store
12 December 2010 1 million registered users
3 August 2011 150 million photos uploaded
September 2011 10 million registered users
3 April 2012 Instagram releases Android version
9 April 2012 Facebook purchases Instagram for $US1 billion
26 July 2012 80 million registered users
16 August 2012 Instagram Photo Maps launched
5 November 2012 Instagram Profiles for the Web launched
5 December 2012 Instagram removes ability for photos to appear as ‘cards’ on Twitter
17 December 2012 Instagram Alters Terms of Use
18 December 2012 Instagram reverts to previous Terms of Use after public backlash
26 February 2013 100 million active monthly users
20 June 2013 Instagram adds video (15-seconds maximum)
10 July 2013 Instagram adds native web embedding for photos and videos
6 September 2013 150 million users
12 December 2013 Instagram Direct messaging service added
24 March 2014 200 million users
26 August 2014 Instagram/Facebook release Hyperlapse app via Apple App Store
10 November 2014 Instagram enables photo caption editing after posting
10 December 2014 300 million users, 70 million photos & videos shared per day

20.
Limitations of platforms …
• Even with 300 million users, Instagram is far
from representative of users in any particular
location or demographic.
• Analysing use of any platform is necessarily
limited, and these limits need to be kept in
mind when making any claims about the
representativeness of the findings.

21.
[3] Project Context: the Ends of Identity

22.
Identity Online: The Networked Self /
Networked Publics
• Persistence
• Replicability
• Scalability
• Searchability (boyd, 2010)
• + Ownership (Aufderheide, 2010)

23.
Shared assumptions of ‘Identity 2.0’, the
‘Networked Self’, and ‘Web Presence’
• Individual agency is central.
• Presumption that identity should be
controlled, curated and managed by the ‘self’
being presented.
• When agency is not the controlling influence,
this is seen as an issue to be overcome (eg
better privacy settings, clearer Terms of Use).

24.
What about the Ends of Identity?
• Following Erving Goffman (1959) if frontstage
is self performed, and backstage is the more
essential self, who builds the stage, and who
remembers the performance(s)?
 Before (online) agency: before birth, until the
‘reigns’ of online identity tools and
performances are inherited?
 After (online) agency: who looks after online
traces of self once the self they refer to dies?

25.
At one end: parents as initial identity
curators/creators online …
• Parents/guardians set the initial
parameters of online identity.
• From ultrasounds photos to cute
toddler pics, losing that first
tooth etc …
• How do and should young people
‘inherit’ online identities?

26.
“The emergence of such social media
platforms as Facebook, Flickr, Instagram,
Twitter, Bundlr and YouTube facilitating the
sharing of images has allowed the wide
dissemination of imagery and information
about the unborn in public forums. Indeed,
sharing of the first ultrasound photograph on
social media has become a rite of pregnancy
for many women.”
(Lupton, 2013, p. 42)

27.
NB: The ‘Real Name’ Web
"Nowadays, however, the anonymity of the [early]
internet and the construction of online personas that do
not reflect offline identities have been reconstructed as
'risk factors' of internet use … Governments, schools,
parents and other concerned parties now routinely warn
against online imposters, bullying and identity theft, and
social network sites like Facebook or Google+ have
policies requiring users to register with their real names
and data, and prevent them from having more than one
account.”
(Zoonen, 2013: 45)

28.
At the other end: Memorializing
Performed Digital Selves?
• What happens to profiles,
accounts, photos, videos and other
social traces after someone dies?
• Do we have the right to delete it
all?
• Should it be memorialized?
• Who decides? (very few laws
address social media inheritance).

29.
[4] Breaking down #ultrasound

30.
On Instagram alone, every month thousands of
foetal images are shared and publicly tagged as
ultrasounds. Often these images capture the
metadata visible on the ultrasound screen, which
might include the mother’s name, the current
date, the location of the scan, the expected
delivery date, and other personal information. For
many young people, this type of sharing will be
their first mention on social media, the beginning
of a long and likely loving record published by
their parents, guardians and loved ones.
(Leaver, 2015)

31.
#ultrasound
Table 1. #ultrasound tagged media on Instagram,
2014
Images Videos Overall Media
March 3468 151 3619
April 3847 128 3975
May 3575 151 3726
3-Month Totals: 10890 430 11320

32.
#ultrasound 48hr snapshot (focused
on first Monday of each month)
• March: 289 images / 7 videos
• April: 331 images / 14 videos
• May: 373 images / 11 videos
 Now to drill down further into the
March #ultrasound images …

33.
#ultrasound
Types of photos …
• Advertising: 3
• No relevance (hashtag
spam): 6
• Ultrasound humour: 8
• Other Medical
Ultrasounds: 17
(including 1 dog)
• Also 15 images deleted
or made private

34.
Social Experiences of #Ultrasounds
• 32 photos depicting social
experiences centred on
prenatal ultrasounds
• EG parent(s) travelling
to/from the ultrasound
• EG selfie and caption
expression nervousness or
excitement prior to
ultrasound

35.
Collages/Professional Photos
incl. #ultrasounds
• 32 photos either deliberate
collages or professional
photographs incorporating
ultrasound photos
• EG professional posed shot or
ultrasound on screen or printed
• EG collage showing ultrasound,
parent(s) plus celebratory details
(eg champagne glass or ‘it’s a
boy/girl’ or planned baby name).

36.
Ultrasounds with personally
identifiable text in the photo
• 71 photos (26% of the set)
included personally
identifiable information in the
photo (usually generated by
the ultrasound equipment)
• Typically includes mother’s full
name, mother’s DOB, medical
facility, estimated gestation
period to date, date of the
scan, etc.

37.
Ultrasounds without personally
identifiable text in the photo
• 105 photos (38% of the set)
do not include personally
identifiable information in the
photograph
• Some deliberately obscured,
some out of focus, most
zoomed to avoid those
details (either consciously or
simply to take a better
photograph)

38.
Instagram Videos …
• 7 videos in data collected
• 1 not relevant (hashtag spam)
• 1 other medical ultrasound
• 2 videos included personally
identifiable information
• 3 videos did not contain
personally identifiable
information

39.
Not visible …
• December 2013 Instagram Direct
messaging introduced: photos sent
only to specified Instagram contacts:
no way of identifying how many are
ultrasound photos (or tagged as
such).
• Also, no way to track #ultrasound
from private Instagram accounts

40.
Tentative Conclusions: Privacy
• 15 images deleted/hidden in first fortnight is
significant (potentially rethinking sharing
publicly).
• 71 images with personally identifiable
information = the initial (named) social media
footprint preceding birth.
• Whether conscious choice (informed) or not,
very hard to tell.

41.
Tentative Conclusions: Ultrasounds part of
the social experience of pregnancy
• Social experience (selfies, journey to/from)
and collages/professional photos demonstrate
the mainstream sociality of sharing ultrasound
photos.
• Collages show explicit choices about framing
the ’story’ of the ultrasound; often a form of
visual digital storytelling.

42.
Tentative conclusions:
identity/presence forming
• All shared #ultrasound photos are indicative of a
growing culture of sharing photos of young people
by parents/guardians/etc.
• Literacies regarding the persistence of this data are
haphazard, rarely informed by the apps/platforms,
showing a cultural need for widespread embedding
of mobile media literacies.
• Social norms about sharing these images are
evolving because of affordances, as much as driving
them

43.
Bigger Picture …
• Instagram can provide both big data
(quantitative) and also the richness of granular
data (qualitative).
• Asking social questions of big data must take
account of the insights available at the granular
level.
• Balancing big data and granularity allows the
humanities researchers, with all their tools for
interpreting granularity, to enhance their
research with contextualised big data.

44.
References
• Aufderheide, P. (2010). Copyright, Fair Use, and Social Networks. In Z. Papacharissi (Ed.), A Networked
Self: Identity, Community, and Culture on Social Network Sites (pp. 274-303). Routledge.
• boyd, danah. (2010). Social Network Sites and Networked Publics: Affordances, Dymanics and
Implications. In Z. Papacharissi (Ed.), A Networked Self: Identity, Community, and Culture on Social
Network Sites (pp. 39-58). Routledge.
• boyd, d., & Crawford, K. (2012). Critical Questions for Big Data. Information, Communication & Society,
15(5), 662-679.
• Bruns, A., & Burgess, J. (2011). Mapping Online Publics. http://mappingonlinepublics.net/
• Halavais, A. (2013). Structure of Twitter: Social and Technical. In K. Weller, A. Bruns, J. Burgess, M.
Mahrt, & C. Puschmann (Eds.), Twitter and Society. New York: Peter Lang.
• Highfield, T., & Leaver, T. (2015). A methodology for mapping Instagram hashtags. First Monday, 20(1).
http://doi.org/10.5210/fm.v20i1.5563
• Goffman, E. (1959). The Presentation of Self in Everyday Life. New York: Anchor Book.
• Lupton, D. (2013). The Social Worlds of the Unborn. Basingstoke: Palgrave MacMillan.
• Leaver, T. (2015). Researching the Ends of Identity: Birth and Death on Social Media. Social Media +
Society, 1(1). http://doi.org/10.1177/2056305115578877
• Markham, A., & Buchanan, E. (2012). Ethical Decision-Making and Internet Research Recommendations
from the AoIR Ethics Working Committee (Version 2.0). Retrieved from
http://aoir.org/reports/ethics2.pdf
• Zoonen, L. van. (2013). From identity to identification: fixating the fragmented self. Media,
Culture & Society, 35(1), 44–51. doi:10.1177/0163443712464557

45.
Questions or Comments?
Or find me later …
www.tamaleaver.net
@tamaleaver
t.leaver@curtin.edu.au