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A Methodology for Mapping Instagram Hashtags
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
Angry Birds as a Social Network Market
Earlier today my colleague Michele Willson and I ran the ANZCA PreConference Social, Casual, Mobile: Changing Games which went really well, bringing together 17 games scholars from Australia and Canada, including a fantastic keynote by Mia Consalvo and plenary by John Banks.
I also had the opportunity to present today, and the slides and audio from my talk are below:
And here’s the abstract if you’re interested:
The hugely successful franchise Angry Birds by Finnish company Rovio is synonymous with the new and growing market of app-based games played on smartphones and tablets. These are often referred to as ‘casual games’, highlighting their design which rewards short bursts of play, usually on mobile media devices, rather than the sustained attention and dedicated hardware required for larger PC or console games. Significantly, there is enormous competition within the mobile games, while the usually very low cost (free, or just one or two dollars) makes a huge ranges of choices available to the average consumer. Moreover, these choices are usually framed by just one standardised interface, such as the Google Play store for Android powered devices, or the Apple App store for iOS devices. Within this plethora of options, I will argue that in addition to being well designed and enjoyable to play, successful mobile games are consciously situated within a social network market.
The concepts of ‘social network markets’ reframes the creative industries not so much as the generators of intellectual property outputs, but as complex markets in which the circulation and value of media is as much about taste, recommendations and other networked social affordances (Potts, Cunningham, Hartley, & Ormerod, 2008). For mobile games, one of the most effective methods of reaching potential players, then, is through the social attentions and activity of other players. Rovio have been very deliberate in the wide-spread engagement with players across a range of social media platforms, promoting competitive play via Twitter and Facebook, highlighting user engagement such as showcasing Angry Birds themed cakes, and generally promoting fan engagement on many levels, encouraging the ‘spreadability’ of Angry Birds amongst social networks (Jenkins, Ford, & Green, 2013). In line with recognising the importance of engagement with the franchise, Rovio have also taken a very positive approach of unauthorised merchandising and knock-offs, especially in China and South-East Asia. In line with Montgomery and Potts’ (2008) argument that a weaker intellectual property approach will foster a more innovative creative industries in China, rather than attempting to litigate of lock down unauthorised material, Rovio have stated they see this as building awareness of Angry Birds and are working to harness this new, socially-driven market (Dredge, 2012). As Rovio now license everything from Angry Birds plush toys to theme parks, social network markets can be perpetuated even by unauthorised material, which builds awareness and interest in the official games and merchandising in the long run. Far from a standalone example, this paper argues that not only is Rovio consciously situating Angry Birds within a social network market model, but that such a model can drive other mobile games success in the future.
References
Dredge, S. (2012, January 30). Angry Birds boss: “Piracy may not be a bad thing: it can get us more business.” The Guardian. Retrieved from http://www.guardian.co.uk/technology/appsblog/2012/jan/30/angry-birds-music-midem
Jenkins, H., Ford, S., & Green, J. (2013). Spreadable Media: Creating Value and Meaning in a Networked Culture. New York and London: New York University Press.
Montgomery, L., & Potts, J. (2008). Does weaker copyright mean stronger creative industries? Some lessons from China. Creative Industries Journal, 1(3), 245–261. doi:10.1386/cij.1.3.245/1
Potts, J., Cunningham, S., Hartley, J., & Ormerod, P. (2008). Social network markets: a new definition of the creative industries. Journal of Cultural Economics, 32(3), 167–185. doi:10.1007/s10824-008-9066-y
Visualising Locative Media with Foursquare’s Time Machine
I’m currently working on a chapter for the forthcoming Locative Media edited collection; the piece I’m co-writing with Clare Lloyd examines some of the pedagogical strategies that have arisen to better inform users about the data that they generate whilst using locative media in various forms (from explicit check-ins with Foursquare to less obvious locative metadata on photographs, tweets and so forth). We’ve been looking at several tools and services like PleaseRobMe.com, I Can Stalk U and Creepy which visualise the often hidden layer locative media layers of mobile devices and services.
Given this context, I was fascinated to see Foursquare’s release of their ‘Time Machine’ (deployed as a promotion for Samsung’s S4) which creates an animation and eventual infographic visualising the a user’s entire Foursquare check-in history. Since I’m very conscious of where I do and don’t use Foursquare, I was fascinated to see what sort of picture of my movements this builds. The grouping of check-ins in Perth (where I live) and the places I’ve travelled to for conferences (which is the main time I use Foursquare) was very smooth, and made my own digitised journey through the world look like a personalised network diagram. The eventual infographic produced is fairly banal, but does crunch your own Foursquare numbers. I’ve embedded mine below.
While Foursquare users are probably amongst the most aware locative media users and generators of locative data, it’s still fascinating to see what a rich and robust picture these individual points of data look like when aggregated. In line with the writing I’m doing, I can’t help wonder how people would respond to a similar sort of visualisation based on their smartphone photos or Facebook posts or some other service which is less explicit or transparent in the way locative metadata is produced and stored.
Digital Culture Links: April 25th
Links through to April 22nd (catching up!):
- Siri secrets stored for up to 2 years [WA Today] – “Siri isn’t just a pretty voice with the answers. It’s also been recording and keeping all the questions users ask. Exactly what the voice assistant does with the data isn’t clear, but Apple confirmed that it keeps users’ questions for up to two years. Siri, which needs to be connected to the internet to function, sends all of its users’ queries to Apple. Apple revealed the information after
Wired posted an article raising the question and highlighting the fact that the privacy statement for Siri wasn’t very clear about how long that information is kept or what would be done with it.” - Now playing: Twitter #music [Twitter Blog] – Not content to be TV’s second screen, Twitter wants to be the locus of conversations about music, too: “Today, we’re releasing Twitter #music, a new service that will change the way people find music, based on Twitter. It uses Twitter activity, including Tweets and engagement, to detect and surface the most popular tracks and emerging artists. It also brings artists’ music-related Twitter activity front and center: go to their profiles to see which music artists they follow and listen to songs by those artists. And, of course, you can tweet songs right from the app. The songs on Twitter #music currently come from three sources: iTunes, Spotify or Rdio. By default, you will hear previews from iTunes when exploring music in the app. Subscribers to Rdio and Spotify can log in to their accounts to enjoy full tracks that are available in those respective catalogs.
- Android To Reach 1 Billion This Year | Google, Eric Schmidt, Mobiles [The Age] – “Google executive chairman Eric Schmidt predicts there will be more than 1 billion Android smartphones in use by the end of the year.”
- Soda Fountains, Speeding, and Password Sharing [The Chutry Experiment] – Fascinating post about the phenomenon of Netflix and HBO Go password sharing in the US. When a NY Times journalist admitted to this (seemingly mainstream) practice, it provoked a wide-ranging discussion about the ethics and legality of many people pooling resources to buy a single account. Is this theft? Is it illegal (apparently so)? And, of course, Game of Thrones take a centre seat!
- “Welcome to the New Prohibition” [Andy Baio on Vimeo] – Insightful talk from Andy Baio about the devolution of copyright into an enforcement tool and revenue extraction device rather than protecting or further the production of artistic material in any meaningful way. For background to this video see Baio’s posts “No Copyright Intended” and “Kind of Screwed”.
- Instagram Today: 100 Million People [Instagram Blog] – Instagram crosses the 100 million (monthly) user mark.
RIOT gear: your online trail just got way more visible
By Tama Leaver, Curtin University
The recent publication of a leaked video demonstrating American security firm Raytheon’s social media mining tool RIOT (Rapid Information Overlay Technology) has rightly incensed individuals and online privacy groups.
In a nutshell, RIOT – already shared with US government and industry as part of a joint research and development effort in 2010 – uses social media traces to profile people’s activities, map their contacts, and predict their future activities.
Yet the most surprising thing isn’t how RIOT works, but that the information it mines is what we’ve each already shared publicly.
Getting to know you
In the above video, RIOT analyses social media accounts – specifically Facebook, Twitter, Gowalla and Foursquare – and profiles an individual.
In just a few seconds, RIOT manages to extract photographs as well are the times and exact location of frequently visited places. This information is then sorted and graphed, making it relatively easy to predict likely times and locations of future activity.
RIOT can also map an individual’s network of personal and professional connections. In the demonstration video, a Raytheon employee is surveyed, and the software shows who his friends are, where he’s been and, most ominously, predicts that the most likely time and place to find him is at a specific gym at 6am on a Monday morning.
Privacy concerns
The RIOT software quite rightly raises concerns about the way online information is being treated.
Since privacy rules and regulations around social media are still in their infancy, it’s hard to tell if any legal boundaries have been crossed. This is especially unclear since it appears, from the video at least, that RIOT only scrutinises information already publicly visible on the web.
The usefulness of some social media tools for mapping a person’s activity are abundantly clear. Foursquare, for example, basically produces a database of the times and places someone elects to “check-in” to specific locations.
Checking-in allows other Foursquare users to interact with that individual, but the record is basically a map of someone’s activities. Foursquare can be a great service, allowing social networking, discounts from businesses, and various location-based activities, but it also leaves a huge data trail.
Foursquare, though, has a (relatively) small user base (around 30 million) compared to Facebook (more than one billion) – although Facebook, as we know, also allows users to check-in by specifying a location in updates and posts. But the richest source of information we tend to share publicly, but not even think about, is our photographs.
Picture this
Every modern smartphone, whether an iPhone, Windows or Android device, by default saves certain information every time you take a photograph. This information about the photograph is saved using something called the Exchangeable image file format, or “exif” data.
Exif data typically includes camera settings, such as how long the camera lens was open and whether the flash fired, but on smartphones also includes the exact geographic location (latitude and longitude) and time that each photograph is taken.
Thus, all of those photographs of celebrations, birthdays, and our kids at the beach all include a digital record of where and when each and every event occurred.
Given that so many of us share photographs online using Facebook or Twitter or Instagram or Flickr, it’s not surprising that RIOT might be able to build a picture of where we’ve been and use that to guess where we might be in the future.
Yet we don’t have to leave this trail. Most smartphones have the ability to turn geographic location information off so that it’s not recorded when we take photographs.
Most of us never think to turn these options off because we don’t think about our social media persisting, but it does. Our social media fragments – our photos and posts – have no expiry date so it’s worth taking a moment when we set up a new phone or account and tweak the settings to only share what you really want to share.
If RIOT demonstrates anything, it’s the fact that information shared publicly online will likely be read, shared, copied, stored and analysed in ways we didn’t immediately think about.
If we take the time to adjust our privacy settings and sharing options, we can exercise some control over the sort of profile RIOT, or any future tool, might build about us.
Tama Leaver receives funding from the Australian Research Council.
This article was originally published at The Conversation. Read the original article.
Tama Leaver dot Net by Tama Leaver is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.