Mathieu Jacomy and Anders Munk, TANT Lab & Public Data Lab
6 minutes read
Gephisto is Gephi in one click. You give it network data, and it gives you a visualization. No settings. No skills needed. The dream! With a twist.
Gephisto produces visualizations such as the one above. It exists as a website, and you can just try it below. It includes test networks, you don’t even need one. Do it! Try it, and come back here. Then we talk about it.
Blog post by By Emillie de Keulenaar, Francisco Kerche, Giulia Tucci, Janna Joceli Omena and Thais Lobo [alphabetical order].
Brazilian political bots have been active since 2014 to influence elections through the creation and maintenance of fake profiles across social media platforms. In 2017, bots’ influence and forms of interference gained a new status with the emergence of “bot factories” acting in support of Jair Bolsonaro’s election and presidency. What we call bolsobots are inauthentic social media accounts created to consistently support Bolsonaro’s political agenda over the years, namely Bolsonaro as a political candidate, President, and avatar of a conservative and militaristic vision of Brazilian history, where social discipline, Christian values and a strong but economically liberal state aim to uproot the decadent influence of “socialism” (Messenberg, 2019).From viralising or spreading hashtags to establishing target audiences with pro-Bolsonaro “slogan accounts” with a strong, visual presence, these bots have also been tied to documented disinformation campaigns (Lobo & Carvalho, 2018; Militão & Rebello, 2019; Santini, Salles, & Tucci, 2021). Despite the efforts of social media platforms, including Whatsapp and Telegram, to restrict their more or less coordinated inauthentic activities (Euronews, 2021), bolsobots still exist and actively adapt to online cultures.
Accounting for the upcoming Brazilian 2022 elections, the project Profiling Bolsobots Networks investigates the practices of pro- and anti- Bolsonaro bots across Instagram, Twitter and TikTok. It aims to empirically demonstrate how to capture the operation of bolsobot networks; the types of accounts that constitute bot ecologies; how (differently) bots behave and promote content; how bolsobots change over time and across platforms, pending to different cultures of authenticity; and, finally, how platform moderation policies may impact their activities over time. In doing so, the project will produce a series of research reports on “bolsobot” networks and digital methods recipes to further the understanding of bots’ presence and influence in the communication ecosystem.
We are (so far) a group of six scholars collaborating on this project: Janna Joceli Omena (Public Data Lab / iNOVA Media Lab / University of Warwick), Thais Lobo (Public Data Lab / King’s College London), Francisco Kerche (Universidade Federal do Rio de Janeiro), Giulia Tucci (Universidade Federal do Rio de Janeiro), Emillie de Keulenaar (OILab / University of Groningen) and Elias Bitencourt (Universidade do Estado da Bahia). Below are some of the preliminary outputs of the project.
In this post Jason Chao, PhD candidate at the University of Siegen, introduces Memespector-GUI, a tool for doing research with and about data from computer vision APIs.
In recent years, tech companies started to offer computer vision capabilities through Application Programming Interfaces (APIs). Big names in the cloud industry have integrated computer vision services in their artificial intelligence (AI) products. These computer vision APIs are designed for software developers to integrate into their products and services. Indeed, your images may have been processed by these APIs unbeknownst to you. The operations and outputs of computer vision APIs are not usually presented directly to end-users.
The open-source Memespector-GUI tool aims to support investigations both with and about computer vision APIs by enabling users to repurpose, incorporate, audit and/or critically examine their outputs in the context of social and cultural research.
What kinds of outputs do these computer vision APIs produce? The specifications and the affordances of these APIs vary from platform to platform. As an example here is a quick walkthrough of some of the features of Google Vision API…
While in an image-saturated society, methods for visual analysis gain urgency, this special issue explores visual ways to study online images. The proposition we make is to stay as close to the material as possible. How to approach the visual with the visual? What type of images may one design to make sense of, reshape, and reanimate online image collections? The special issue also touches upon the role that algorithmic tools, including machine vision, can play in such research efforts. Which kinds of collaborations between humans and machines can we envision to better grasp and critically interrogate the dynamics of today’s digital visual culture?
The articles (available both in English and in Spanish) touch on the diversity of formats and uses of online images, focusing on collection and visual interpretation methods. Other themes touched by this issue are image machine co-creation processes and their ethics, participatory actions for image production and analysis, and feminist approaches to digital visual work.
Further information about the issue can be found in our introduction. Following is the complete list of contributions (with links) and authors (some from the Public Data Lab).