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A Methodology for Mapping Instagram Hashtags
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A Methodology for Mapping Instagram Hashtags

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Social media platforms for content-sharing, information diffusion, and publishing thoughts and opinions have been the subject of a wide range of studies examining the formation of different publics, …

Social media platforms for content-sharing, information diffusion, and publishing thoughts and opinions have been the subject of a wide range of studies examining the formation of different publics, politics and media to health and crisis communication. For various reasons, some platforms are more widely-represented in research to date than others, particularly when examining large-scale activity captured through automated processes, or datasets reflecting the wider trend towards ‘big data’. Facebook, for instance, as a closed platform with different privacy settings available for its users, has not been subject to the same extensive quantitative and mixed-methods studies as other social media, such as Twitter. Indeed, Twitter serves as a leading example for the creation of methods for studying social media activity across myriad contexts: the strict character limit for tweets and the common functions of hashtags, replies, and retweets, as well as the more public nature of posting on Twitter, mean that the same processes can be used to track and analyse data collected through the Twitter API, despite covering very different subjects, languages, and contexts (see, for instance, Bruns, Burgess, Crawford, & Shaw, 2012; Moe & Larsson, 2013; Papacharissi & de Fatima Oliveira, 2012)

Building on the research carried out into Twitter, this paper outlines the development of a project which uses similar methods to study uses and activity on through the image-sharing platform Instagram. While the content of the two social media platforms is dissimilar – short textual comments versus images and video – there are significant architectural parallels which encourage the extension of analytical methods from one platform to another. The importance of tagging on Instagram, for instance, has conceptual and practical links to the hashtags employed on Twitter (and other social media platforms), with tags serving as markers for the main subjects, ideas, events, locations, or emotions featured in tweets and images alike. The Instagram API allows queries around user-specified tags, providing extensive information about relevant images and videos, similar to the results provided by the Twitter API for searches around particular hashtags or keywords. For Instagram, though, the information provided is more detailed than with Twitter, allowing the analysis of collected data to incorporate several different dimensions; for example, the information about the tagged images returned through the Instagram API will allow us to examine patterns of use around publishing activity (time of day, day of the week), types of content (image or video), filters used, and locations specified around these particular terms. More complex data also leads to more complex issues; for example, as Instagram photos can accrue comments over a long period, just capturing metadata for an image when it is first available may lack the full context information and scheduled revisiting of images ...

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  • 1. A Methodology for Mapping Instagram Hashtags Dr Tim Highfield & Dr Tama Leaver Centre for Culture and Technology (CCAT) & Department of Internet Studies Curtin University
  • 2. Overview 1. The social media research context 2. Challenges and questions 3. Ethics beyond the binary
  • 3. [1] The social media research context
  • 4. Social media/Big Data • Internet Studies – and other, related fields – is seeing numerous quantitative-driven, large- scale projects capturing and analysing activity on social media platforms. – Automated processes querying APIs for data which is publicly available online. • These provide means for studying online activity, uses of these new platforms, over time and across large populations and topics .
  • 5. The Big Data question • “Too often, Big Data enables the practice of apophenia: seeing patterns where none actually exist, simply because enormous quantities of data can offer connections that radiate in all directions.” (boyd & Crawford, 2012, p. 668) • Research needs to reconcile collection and analytical processes, capturing large datasets yet also acknowledging limits and the ‘small’ data within them.
  • 6. Studying social media: 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. Mapping Twitter discussions Network map of Twitter users featuring #tdf (Tour de France) in tweets 29 June-23 July 2012 see also mappingonlinepublics.net
  • 9. #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…
  • 10. 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…
  • 11. 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 other, more dynamic social media data, as well as comparing text and image/video/sound content.
  • 12. 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 for other social media?
  • 13. [2] Challenges and questions
  • 14. Instagram data Creator user name Image/ video Caption Likes Comments Tag @mention Date/time
  • 15. Tracking Instagram activity • Our initial approach builds on Twitter-specific work, 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.
  • 16. 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).
  • 17. 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.
  • 18. 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 (even if the original author did not use the tag).
  • 19. Processing and filtering • Deliberate acts and intentions – authors using tags in their own captions at the time of publication – vs. less certain behaviours. • Personal information in comments and content. – Addressing anonymity, pseudonymity a question without a consistent answer in social media research. • Processing and filtering data for time, keyword, etc., but also for the information it provides and reveals (intentionally or not).
  • 20. [3] Ethics beyond the binary
  • 21. 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)
  • 22. Instagram’s growth … • October 2010: Launch (Apple AppStore). Only available to iOS devices (iPhone etc.) • December 2010: 1 million users. • June 2011: 5 million users. • September 2011: 10 million users. • April 2012: Android version released. • September 2012: 100 million users. • Late 2012: public web profiles. • MOBILE ≠ PRIVATE
  • 23. Privacy: experiential but not technical
  • 24. Data ownership …
  • 25. Official Instagram web timelines …
  • 26. A Social Media Contradiction “a social media contradiction may arise where users focus on the social elements – often acts of communication and sharing which are thought of as ephemeral and in the moment, comparable to a telephone conversation – while the companies and corporations creating these apps are more focused on the media elements, which are measurable, aggregatable, can be algorithmically analysed in a variety of potentially valuable ways, and can last indefinitely.” (Leaver & Lloyd, 2014)
  • 27. Contextual Integrity in Ethics • Instagram may be experienced as private or partially private in everyday use (contextually), despite always 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”). • Carefully framed aggregates and de-identified examples are one possible way of framing Instagram photos ethically.
  • 28. Conclusion • Methods for studying Instagram currently in development, pilot study underway. • Research into Instagram – and social media in general – needs to reconcile: – Public and public-ness; – Quantitative and qualitative methods; – Types of data and content: text and image, as well as their contexts and appropriate analytical methods, and their associated meta-data; – Evolving uses and affordances of social media platforms; – Social media use overall – platforms not used in isolation.
  • 29. References • 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. • 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 http://aoir.org/reports/ethics2.pdf

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