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Mapping the Ends of Identity on Instagram

While many studies explore the way that individuals represent themselves online, a less studied but equally important question is the way that individuals who cannot represent themselves are portrayed. This paper outlines an investigation into some of those individuals, exploring the ends of identity – birth and death – and the way the very young and deceased are portrayed via the popular mobile photo sharing app and platform Instagram. In order to explore visual representations of birth and death on Instagram, photos with four specific tags were tracked: #birth, #ultrasound, #funeral and #RIP. The data gathered included quantitative and qualitative material. On the quantitative front, metadata was aggregated about each photo posted for three months using the four target tags. This includes metadata such as the date taken, place taken, number of likes, number of comments, what tags were used, and what descriptions were given to the photographs. The quantitative data gives also gives an overall picture of the frequency and volume of the tags used. To give a more detailed understanding of the photos themselves, on one day of each month tracked, all of the photographs on Instagram using the four tags were downloaded and coded, giving a much clearer representative sampling of exactly how each tag is used, the sort of photos shared, and allowed a level of filtering. For example, the #ultrasound hashtag includes a range of images, not just prenatal ultrasounds, including both current images (taken and shared at that moment), historical images, collages, and even ultrasound humour (for example, prenatal ultrasound images with including a photoshopped inclusion of a cash, or a cigarette, joking about the what the future might hold). This paper will outline the methods developed for tracking Instagram photos via tags, it will then present a quantitative overview of the uses and frequency of the four hashtags tracked, give a qualitative overview of the #ultrasound and #RIP tags, and conclude with some general extrapolations about the way that birth and death are visually represented online in the era of mobile media.

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Mapping the Ends of Identity on Instagram

  1. 1. Mapping the Ends of Identity on Instagram 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
  2. 2. Overview 1. Context/Background: The Ends of Identity 2. Method: Collecting Photos from Instagram 3. Breaking down #ultrasound
  3. 3. [1] Context/Background: the Ends of Identity
  4. 4. Identity Online: The Networked Self / Networked Publics • Persistence • Replicability • Scalability • Searchability (boyd, 2010) • + Ownership (Aufderheide, 2010)
  5. 5. 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).
  6. 6. 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?
  7. 7. 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?
  8. 8. “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) 25.09.2013
  9. 9. 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)
  10. 10. 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).
  11. 11. [2] Method: Collecting Photos from Instagram
  12. 12. 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
  13. 13. Twitter data User name Tweet Hashtag Link Date and time @mention
  14. 14. #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…
  15. 15. 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…
  16. 16. 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.
  17. 17. 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.
  18. 18. Instagram data Creator user name Image/ video Caption Likes Comments Tag @mention Date/time
  19. 19. 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, 2014.
  20. 20. 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).
  21. 21. 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?)
  22. 22. 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).
  23. 23. 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)
  24. 24. 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”).
  25. 25. [3] Breaking down hashtags & #ultrasound
  26. 26. #funeral • March: 5589 items (5375 image; 214 videos) • April: 5649 (5429 images; 220 videos) • May: 5259 (5059 images; 200 videos) • Total: 16497 (15863 images; 634 videos)​
  27. 27. #funeral 48hr snapshot (focused on first Monday of each month) • March: 398 images / 9 videos • April: 543 images / 26 videos • May: 472 images / 19 videos (#RIP images enormous, eg 15000-19000 for the same periods)
  28. 28. #birth • March: 11876 items (11408 images; 468 videos) • April: 13085 (12538 images; 547 videos) • May: 5259 (12866 images; 582 videos) • Total: 38409 (36812 images; 1597 videos)
  29. 29. #birth 48hr snapshot (focused on first Monday of each month) • March: 662 images / 36 videos • April: 1087 images / 41 videos • May: 1483 images / 67 videos
  30. 30. #ultrasound • March: 3619 items (3468 images; 151 videos) • April: 3975 (3847 images; 128 videos) • May: 3726 (3575 images; 151 videos) • Total: 11320 (10890 images; 430 videos)
  31. 31. #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 …
  32. 32. #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
  33. 33. 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
  34. 34. 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).
  35. 35. 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.
  36. 36. 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)
  37. 37. 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
  38. 38. 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 (NB: Instagram users representative of particular demographics, too.)
  39. 39. 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.
  40. 40. 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.
  41. 41. 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
  42. 42. 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. • 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. (2014). A Methodology for Mapping Instagram Hashtags. Presented at the Digital Humanities Australasia 2014, Perth, Australia. Retrieved from • 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., & Lloyd, C. (2014, Forthcoming). Seeking Transparency in Locative Media. In R. Wilken & G. Goggin (Eds.), Locative Media. London & New York: Routledge. • Markham, A., & Buchanan, E. (2012). Ethical Decision-Making and Internet Research Recommendations from the AoIR Ethics Working Committee (Version 2.0). Retrieved from • Zoonen, L. van. (2013). From identity to identification: fixating the fragmented self. Media, Culture & Society, 35(1), 44–51. doi:10.1177/0163443712464557
  43. 43. Questions or Comments? Or find me later … @tamaleaver