At this week’s fantastically engaging CCI Digital Methods Summer School held at Swinburne University in Melbourne, Tim Highfield and I presented a workshop about analysing visual social media, focusing on Instagram data collection and anaylsis. It was based, in part, on our recent First Monday paper, but also looked beyond that at ways of surfacing research questions and approaches. We were pleased with the interest in the workshop, and really positive responses to it, so we’ve shared the slides here:
There will be more on Instagram from us later this year, but if you’re working on Instagram I’d love to hear what you’re doing; either leave a comment here or ping me an email if you want to get in touch.
Today First Monday published A Methodology for Mapping Instagram Hashtags by Tim Highfield and myself. This methodology paper explains the processes behind the various media we’ve been tracking as part of the Ends of Identity project, although the utility of the methods go far beyond that. Beyond technical questions, we’ve included some important ethical and privacy questions that arose as we started to explore Instagram mapping. Here’s the abstract:
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, especially 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.
If you’re interested, Axel Bruns did a great liveblog summary of the paper, and for the truly dedicated there is an mp3 audio copy of the talk. The paper itself is in the process of being written up and should be in full chapter form in a month or so; if you’d like to look over a full draft once it’s written up, just let me know.
Instead of blowing my own trumpet further, though, I’d rather talk about two recent open access developments that are far more interesting. The first is a truly outstanding collaboration by a group from the Association of Internet Researchers (and the Selfies Research Network on Facebook) who’ve created an open access, Creative Commons licensed, Selfies course. Each of the six weeks covers a particular perspective or area relating to selfies, with readings, provocations, suggested assignments and, of course, selfie activities. The breadth of ideas, and structured learning activities, make this a great course in its own right, but even more impressively it’s explicitly presented as material that can be used, explored, utilised and built on by other educators across a range of disciplines and levels. This sort of collaboration and sharing epitomises the very best of open access education, and it doesn’t hurt that the people behind it are some of the smartest thinkers about online culture around today.
So, kudos and well done to the talented group who’ve created this amazing resource, namely: Theresa Senft (New York University, USA); Jill Walker Rettberg (University of Bergen, Norway); Elizabeth Losh (University of California, San Diego, USA); Kath Albury (University of New South Wales, Australia); Radhika Gajjala (Bowling Green State University, USA); Gaby David (EHESS, France); Alice Marwick (Fordham University, USA); Crystal Abidin (University of Western Australia, Australia); Magda Olszanowski (Concordia University, Canada); Fatima Aziz (EHESS, France); Katie Warfield (Kwantien University College, Canada); and Negar Mottahedeh (Duke University, USA).
The newly-released edited collection Locative Media by Rowan Wilken and Gerard Goggin features a chapter from Clare Lloyd and me looking at issues of privacy and transparency relating to the data generated, stored and analysed when using mobile and locative-based services. Here’s the abstract:
A person’s location is, by its very nature, ephemeral, continually changing and shifting. Locative media, by contrast, is created when a device encodes a users’ geographic location, and usually the exact time as well, translating this data into information that not only persists, but can be aggregated, searched, indexed, mapped, analysed and recalled in a variety of ways for a range of purposes However, while the utility of locative media for the purposes of tracking, advertising and profiling is obvious to many large corporations, these uses are far from transparent for many users of mobile media devices such as smartphones, tablets and satellite navigation tools. Moreover, when a new mobile media device is purchased, users are often overwhelmed with the sheer number of options, tools and apps at their disposal. Often, exploring the settings or privacy preferences of a new device in a sufficiently granular manner to even notice the various location-related options simply escapes many new users. Similarly, even those who deactivate geolocation tracking initially often unintentionally reactivate it, and leave it on, in order to use the full functionality of many apps. A significant challenge has thus arisen: how can users be made aware of the potential existence and persistence of their own locative media? This chapter examines a number of tools and approaches which are designed to inform everyday users of the uses, and potential abuses, or locative media; PleaseRobMe, I Can Stalk U, iPhone Tracker and the aptly named Creepy. These awareness-raising tools make visible the operation of certain elements of locative media, such as revealing the existence of geographic coordinates in cameraphone photographs, and making explicit possible misuses of a visible locative media trail. All four are designed as pedagogical tools, aiming to make users aware of the tools they are already using. In an era where locative media devices are easy to use but their ease occludes extremely complex data generation and potential tracking, this chapter argues that these tools are part of a significant step forward in developing public awareness of locative media, and related privacy issues.
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.
I’m pleased to announced that the special themed issue of Digital Culture and Education on Facebook in Education, edited by Mike Kent and I, has been released. The issue features an introductory article by Mike and I, ‘Facebook in Education: Lessons Learnt’ in which we may have some opinions about whether the hype around MOOCs and disruptive online education ignores the very long history of learning online (hint: it does). As something of a corrective to that hype, this issue explores different aspects of the complicated relationship between Facebook as a platform and learning and teaching in higher education.
Having some form of anonymity online offers many people a kind of freedom. Whether it’s used for exposing corruption or just experimenting socially online it provides a way for the content (but not its author) to be seen.
But this freedom can also easily be abused by those who use anonymity to troll, abuse or harass others, which is why Facebook has previously been opposed to “anonymity on the internet”.
CEO Mark Zuckerberg has been committed to Facebook as a site for users to have a single real identity since its beginning a decade ago as a platform to connect college students. Today, Facebook’s core business is still about connecting people with those they already know.
But there have been concerns about what personal information is revealed when people use any third-party apps on Facebook.
So this latest announcement aims to address any reluctance some users may have to sign in to third-party apps. Users will soon be able to log in to them without revealing any of their wealth of personal information.
That does not mean they will be anonymous to Facebook – the social media site will still track user activity.
It might seem like the beginning of a shift away from singular, fixed identities, but tweaking privacy settings hardly indicates that Facebook is embracing anonymity. It’s a long way from changing how third-party apps are approached to changing Facebook’s entire real-name culture.
Having the option to log in to third-party apps anonymously does not necessarily mean Facebook users will actually use it. Effective use of Facebook’s privacy settings depends on user knowledge and motivation, and not all users opt in.
A recent Pew Research Center report reveals that the most common strategy people use to be less visible online is to clear their cookies and browser history.
Only 14% of those interviewed said they had used a service to browse the internet anonymously. So, for most Facebook users, their experience won’t change.
Facebook login on other apps and websites
Facebook offers users the ability to use their authenticated Facebook identity to log in to third-party web services and mobile apps. At its simplest and most appealing level, this alleviates the need for users to fill in all their details when signing up for a new app. Instead they can just click the “Log in with Facebook” button.
For online corporations whose businesses depend on building detailed user profiles to attract advertisers, authentication is a real boon. It means they know exactly what apps people are using and when they log in to them.
Automated data flows can often push information back into the authenticating service (such as the music someone is playing on Spotify turning up in their Facebook newsfeed).
While having one account to log in to a range of apps and services is certainly handy, this convenience means it’s almost impossible to tell what information is being shared.
Is Facebook just sharing your email address and full name, or is it providing your date of birth, most recent location, hometown, a full list of friends and so forth? Understandably, this again raises privacy concerns for many people.
How anonymous login works
To address these concerns, Facebook is testing anonymous login as well as a more granular approach to authentication. (It’s worth noting, neither of these changes have been made available to users yet.)
Given the long history of privacy missteps by Facebook, the new login appears to be a step forward. Users will be told what information an app is requesting, and have the option of selectively deciding which of those items Facebook should actually provide.
Facebook will also ask users whether they want to allow the app to post information to Facebook on their behalf. Significantly, this now places the onus on users to manage the way Facebook shares their information on their behalf.
Sometimes people want to try out apps, but they’re not ready to share any information about themselves.
It’s certainly useful to try out apps without having to fill in and establish a full profile, but very few apps can actually operate without some sort of persistent user identity.
The implication is once a user has tested an app, to use its full functionality they’ll have to set up a profile, probably by allowing Facebook to share some of their data with the app or service.
Taking on the competition
The value of identity and anonymity are both central to the current social media war to gain user attention and loyalty.
Facebook’s anonymous login might cynically be seen as an attempt to court users who have flocked to Snapchat, an app which has anonymity built into its design from the outset.
Snapchat’s creators famously turned down a US$3 billion buyout bid from Facebook. Last week it also revealed part of its competitive plan, an updated version of Snapchat that offers seamless real-time video and text chat.
By default, these conversations disappear as soon as they’ve happened, but users can select important items to hold on to.
Whether competing with Snapchat, or any number of other social media services, Facebook will have to continue to consider the way identity and anonymity are valued by users. At the moment its flirting with anonymity is tokenistic at best.
Tama Leaver receives funding from the Australian Research Council (ARC).
Emily van der Nagel does not work for, consult to, own shares in or receive funding from any company or organisation that would benefit from this article, and has no relevant affiliations.
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.
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