I presented via a Skype in June at the Instagram Conference: Studying Instagram Beyond Selfies on the algorithmic brand culture of Instagram.
I talked about the algorithmic brand culture of Instagram. Part of what I describe is how the participatory culture of turning public cultural events - like music festivals - into flows of images on Instagram doubles as the activity of creating datasets of images that machines can classify. Do public cultural sites like music festivals are sites where participatory culture teaches machines to classify culture?
My argument here is that we need to think about the interplay between participatory culture and machine learning. And, to understand how platforms like Instagram are building an algorithmic brand culture we need to develop ways of simulating their machine learning and image-classification capacities in the public domain, where they can be subject to public scrutiny.
I develop this argument, with my colleague Daniel Angus, in this piece in Media, Culture & Society: Algorithmic brand culture: participatory labour, machine learning and branding on social media.
You create images that are meaningful to you, those images are classifiable by machines. Our images are training data, they are used to train algorithms to recognise the people, places, moments that capture our attention. Instagram is an algorithmic brand culture in action.
The past couple of years we have been anticipating this moment when platforms cross that threshold into classifying patterns and judgments in images that we ourselves could not put into words. So, now Instagram crosses that threshold, we should ask: what is next? Sooner or later advertisements that are entirely simulations created by machines that analyse your images and place brands within a totally fabricated scene?
Instagram also predict they'll face same 'saturation' problem as Facebook. As brands flood the platform, organic reach decreases, and paid reach becomes imperative. That means increasingly targeted, less serendipitous feeds?
Stories really matter here because they give Instagram the 'two speeds' or 'two flows' that Facebook didn't have. Stories + Home feed give Instagram a 'killer' mix of ephemeral blinks and flowing glances optimised by machine learning. I began thinking about how the interplay between participation and machine learning was critical to engineering the home feed in this piece here. To me, the engineering of the Instagram home feed reminds me of the engineering of the algorithms that keep gamblers sitting at poker machines.
Here's some recent work Daniel Angus, Mark Andrejevic and myself have been doing on machine learning and Instagram. In this work we are working on building a machine vision system that can classify Instagram images as a way of critically simulating the algorithmic power of the platform. Our argument is that platforms like Instagram shape public culture but are not open to public scrutiny. To understand the interplay between participatory culture and machine learning we need to build 'image machines' of social media in the public domain where we can explore and experiment with their capacity to make judgments about the content of our images. Our early experiments demonstrate how 'off the shelf' machine vision algorithms can quickly classify objects (like bottles), people and brand logos.
Below are some images of the music festival Splendour in the Grass, which help to illustrate some of the ways in which the festival site, performances, art installations and brand activations are 'instagrammable', by which I mean they both invite humans to capture them as images and they are classifiable by machines.