A methodology for mapping Instagram hashtags

Tim Highfield, Tama Leaver
2014 First Monday  
While social media research has provided detailed cumulative analyses of selected social media platforms and content, especially Twitter, newer platforms, apps, and visual content have been less extensively studied so far. This paper proposes a methodology for studying Instagram activity, building on established methods for Twitter research by initially examining hashtags, as common structural features to both platforms. In doing so, we outline methodological challenges to studying Instagram,
more » ... pecially in comparison to Twitter. Finally, we address critical questions around ethics and privacy for social media users and researchers alike, setting out key considerations for future social media research. one of the most studied social media platforms 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 presenting very different subjects, languages, and contexts (Bruns et al., 2012; Moe and Larsson, 2013; Papacharissi and de Fatima Oliveira, 2012) . Building on the research carried out into Twitter, this paper outlines emerging methods to study uses and activity on the image-sharing app and social media platform Instagram. While the content of the two social media platforms is dissimilar -mainly short textual comments versus images and videothere are some architectural parallels which encourage the extension of 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 and 'Web 2.0' platforms), with tags serving as markers for the main subjects, ideas, events, locations, or emotions featured in tweets and images alike. The Instagram Application Programming Interface (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. The information provided allows for the analysis of collected data to incorporate several different dimensions; for example, the information about the tagged images returned through the Instagram API allows us to examine patterns of use around publishing activity (time of day, day of the week), types of content (image or video), and locations specified around these particular terms. 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 (2011a) 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 metrics 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 the resulting filtered datasets.
doi:10.5210/fm.v20i1.5563 fatcat:mn4vpgw74ngrlkmrjzy7ahjaxi