Metapicturing: Visual media analysis for Instagram and other online platforms

With Richard Rogers*

Network visualisation by Geboers et al., 2017 

Instagram is currently the social media platform most associated with online images (and their analysis), but images from other platforms also can be collected and grouped, arrayed by similarity, stacked, matched, stained, labelled, depicted as network, placed side by side and otherwise analytically displayed. In the following, the initial focus is on Instagram, together with certain schools of thought such as Instagramism and Instagrammatics for its aesthetic and visual cultural study. Building on those two approaches, it subsequently focuses on other web and social media platforms, such as Google Image Search, Twitter, Facebook and 4chan. It provides demonstrations of how querying techniques create online image collections, and how these sets are analytically grouped through arrangements collectively referred to as metapictures.  

Suggested reading: 

Richard Rogers: ”Visual media analysis for Instagram and other online platforms”, Big Data & Society, January–June: 1–23: 2021.  

Analysing meme collections with the computer vision network approach

With Janna Joceli Omena*

Network gif visualisation by Omena & Rogers (forthcoming).

 The computer vision network approach involves the creative analysis of online image collections, relying on the aggregation of computer vision features to render images as networks. The method is designed to take advantage of computer vision APIs, repurposing the analytics provided by these Web-based services for social, cultural and media research. In this talk, I will introduce how computer vision analysis makes possible different ways of interpreting online image collection(s), from the content of the images themselves to the web-based textual contexts in which they occur, to the sites of image circulation. To do this, the steps of network building and analysis are introduced, paying special attention to the curation of image collections (query design), the computer vision method (detecting web entities associated with an image) and the algorithms used to visualise the network (ForceAtlase2). Finally, I present the key findings of recent network vision study, and analysis of four covid meme collections from Know Your Meme, Imgur, Facebook and Instagram. 

Suggested readings: 

Omena, J. J., Pilipets, E., Gobbo, B., & Jason, C. (2021). The Potentials of Google Vision API-based Networks to Study Natively Digital Images. Diseña, (19), 1-1. 

Omena, J.J. (2021). Digital Methods and Technicity-of-the-Mediums. From Regimes of Functioning to Digital Research. [Doctoral Dissertation] http://hdl.handle.net/10362/127961 

2022 Digital Methods Winter School project: What is a meme, technically speaking? https://wiki.digitalmethods.net/Dmi/WinterSchool2022WhatIsAMeme