At this week’s Digital Humanities Australasia 2014 conference in Perth, Tim Highfield and I presented the first paper from a new project looking a visual social media, with a particular focus on Instagram. The slides and abstract are below (sadly with Slideshare discontinuing screencasts, I’m not sure if I’ll be adding audio to presentations again):

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 may be necessary to capture the conversation and impact of an Instagram photo in terms of comments, likes and so forth.

This is an exploratory study, developing and introducing methods to track and analyse Instagram data; it builds upon the methods, tools, and scripts used by Bruns and Burgess (2010, 2011) in their large-scale analysis of Twitter datasets. These processes allow for the filtering of the collected data based on time and keywords, and for additional analytics around time intervals and overall user contributions. Such tools allow us to identify quantitative patterns within the captured, large-scale datasets, which are then supported by qualitative examinations of filtered datasets.

References

Bruns, A., & Burgess, J. (2010). Mapping Online Publics. Retrieved from http://mappingonlinepublics.net

Bruns, A., & Burgess, J. (2011, June 22). Gawk scripts for Twitter processing. Mapping Online Publics. Retrieved from http://mappingonlinepublics.net/resources/

Bruns, A., Burgess, J., Crawford, K., & Shaw, F. (2012). #qldfloods and @ QPSMedia: Crisis Communication on Twitter in the 2011 South East Queensland Floods. Brisbane. Retrieved from http://cci.edu.au/floodsreport.pdf

Moe, H., & Larsson, A. O. (2013). Untangling a Complex Media System. Information, Communication & Society, 16(5), 775–794. doi:10.1080/1369118X.2013.783607

Papacharissi, Z., & de Fatima Oliveira, M. (2012). Affective News and Networked Publics: The Rhythms of News Storytelling on #Egypt. Journal of Communication, 62, 266–282. doi:10.1111/j.1460-2466.2012.01630.x

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