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.
“For decades, social researchers have argued that there is much to be learned when things go wrong.¹ In this essay, we explore what can be learned about algorithms when things do not go as anticipated, and propose the concept of algorithm troubleto capture how everyday encounters with artificial intelligence might manifest, at interfaces with users, as unexpected, failing, or wrong events. The word trouble designates a problem, but also a state of confusion and distress. We see algorithm troubles as failures, computer errors, “bugs,” but also as unsettling events that may elicit, or even provoke, other perspectives on what it means to live with algorithms — including through different ways in which these troubles are experienced, as sources of suffering, injustice, humour, or aesthetic experimentation (Meunier et al., 2019). In mapping how problems are produced, the expression algorithm trouble calls attention to what is involved in algorithms beyond computational processes. It carries an affective charge that calls upon the necessity to care about relations with technology, and not only to fix them (Bellacasa, 2017).”
The following post is from Judit Varga, Postdoctoral Researcher on the ERC-funded project FluidKnowledge, based at the Centre for Science and Technology Studies, Leiden University.
We would like to invite the Public Data Lab and its network of researchers and research centres to join and contribute to our session about quali-quantitative, digital, and computational methods in Science and Technology Studies (STS) at the next EASST conference 6-9 July 2022.
Fitting with the Public Data Network’s activities, the session starts from the observation that engaging with digital and computational ways of knowing is crucial for STS and related disciplines to study or intervene in them. The panel invites contributions that attempt or reflect on methodological experimentation and innovation in STS by combining STS concepts or qualitative, interpretative methods with digital, quantitative and computational methods, such as quali-quantitative research.
Over the past decade, STS scholars have increasingly benefited from digital methods, drawing on new media studies and design disciplines, among others. In addition, recently scholars also called for creating new dialogues between STS and QSS, which have increasingly grown apart since the 1980s. Although the delineation of STS methods from neighboring fields may be arbitrary, delineation can help articulate methodological differences, which in turn can help innovate and experiment with STS methods at the borders with other disciplines.
We invite contributions that engage with the following questions. What do we learn if we try to develop digital and computational STS research methods by articulating and bridging disciplinary divisions? In what instances is it helpful to draw boundaries between STS and digital and computational methods, for whom and why? On the contrary, how can STS benefit from not drawing such boundaries? How can we innovate STS methods to help trace hybrid and diverse actors, relations, and practices, using digital and computational methods? How can methodological innovation and experimentation with digital and computational methods help reach STS aspirations, or how might it hinder or alter them? What challenges do we face when we seek to innovate and experiment with digital and computational methods in STS? In what ways are such methodological reflection and innovation in STS relevant at a time of socio-ecological crises?
The current deadline for abstract submissions is the 1st of February 2022 7th February 2022 (the deadline has been extended).
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…
We’ve recently been experimenting with the use of ObservableHQ notebooks for gathering and transforming data in the context of digital research. This post walks through a few recent examples of notebooks from recent Public Data Lab projects.
Code notebooks are a third option that lies somewhere in between these options. Designed for programmers, notebooks allow for iterative manipulation and experimentation with code whilst keeping track of creative processes by commenting on the thinking behind each step.
Notebooks allow us to both write and run custom scripts as well as creating simple interfaces for those who may not code. Thus we can use them to help researchers, students and external collaborators to collect data, making it easier to call APIs, setting parameters, or perform manipulations.
ObservableHQ is one solution for writing programming notebooks, it runs in the browser and is oriented towards data and visualisations (“We believe thinking with data is an essential skill for the future”). Hence, we thought it could be a good starting point for what we wanted to do.
This will take place on 10-14th January 2022 at the University of Amsterdam.
More details and registration links are available here and an excerpt on this year’s theme and the format is copied below.
The Digital Methods Initiative (DMI), Amsterdam, is holding its annual Winter School on ‘Social media data critique’. The format is that of a (social media and web) data sprint, with tutorials as well as hands-on work for telling stories with data. There is also a programme of keynote speakers. It is intended for advanced Master’s students, PhD candidates and motivated scholars who would like to work on (and complete) a digital methods project in an intensive workshop setting. For a preview of what the event is like, you can view short video clips from previous editions of the School.
Data critique and platform dependencies: How to study social media data?
Source criticism is the scholarly activity traditionally concerned with provenance and reliability. When considering the state of social media data provision such criticism would be aimed at what platforms allow researchers to do (such as accessing an API) and not to do (scrape). It also would consider whether the data returned from querying is ‘good’, meaning complete or representative. How do social media platforms fare when considering these principles? How to audit or otherwise scrutinise social media platforms’ data supply?
Recently Facebook has come under renewed criticism for its data supply through the publication of its ‘transparency’ report, Widely Viewed Content. It is a list of web URLs and Facebook posts that receive the greatest ‘reach’ on the platform when appearing on users’ News Feeds. Its publication comes on the heels of Facebook’s well catalogued ‘fake news problem’, first reported in 2016 as well as a well publicised Twitter feed that lists the most-engaged with posts on Facebook (using Crowdtangle data). In both instances those contributions, together with additional scholarly work, have shown that dubious information and extreme right-wing content are disproportionately interacted with. Facebook’s transparency report, which has been called ‘transparency theater’, demonstrates that it is not the case. How to check the data? For now, “all anybody has is the company’s word for it.”
For Facebook as well as a variety of other platforms there are no public archives. Facebook’s data sharing model is one of an industry-academic ‘partnership’. The Social Science One project, launched when Facebook ended access to its Pages API, offers big data — “57 million URLs, more than 1.7 trillion rows, and nearly 40 trillion cell values, describing URLs shared more than 100 times publicly on Facebook (between 1/1/2017 and 2/28/2021).” To obtain the data (if one can handle it) requires writing a research proposal and if accepted compliance with Facebook’s ‘onboarding’, a non-negotiable research data agreement. Ultimately, the data is accessed (not downloaded) in a Facebook research environment, “the Facebook Open Research Tool (FORT) … behind a VPN that does not have access to the Internet”. There are also “regular meetings Facebook holds with researchers”. A data access ethnography project, not so unlike to one written about trying to work with Twitter’s archive at the Library of Congress, may be a worthwhile undertaking.
Other projects would evaluate ‘repurposing’ marketing data, as Robert Putnam’s ‘Bowling Alone’ project did and as is a more general digital methods approach. Comparing multiple marketing data outputs may be of interest, and crossing those with CrowdTangle ‘s outputs. Facepager, one of the last pieces of software (after Netvizz and Netlytic) to still have access to Facebook’s graph API reports that “access permissions are under heavy reconstruction”. Its usage requires further scrutiny. There is also a difference between the user view and the developer view (and between ethnographic and computational approaches), which is also worth exploring. ‘Interface methods‘ may be useful here. These and other considerations for developing social media data criticism are topics of interest for this year’s Winter School theme.
At the Winter School there are the usual social media tool tutorials (and the occasional tool requiem), but also continued attention to thinking through and proposing how to work with social media data. There are also empirical and conceptual projects that participants work on. Projects from the past Summer and Winter Schools include: Detecting Conspiratorial Hermeneutics via Words & Images, Mapping the Dutchophone Fringe on Telegram, Greenwashing, in_authenticity & protest, Searching constructive/authentic posts in media comment sections: NU.nl/The Guardian, Mapping deepfakes with digital methods and visual analytics, “Go back to plebbit”: Mapping the platform antagonism between 4chan and Reddit, Profiling Bolsobots Networks, Infodemic everywhere, Post-Trump Information Ecology, Streams of Conspirational Folklore, and FIlterTube: Investigating echo chambers, filter bubbles and polarization on YouTube.
Organisers: Lucia Bainotti, Richard Rogers and Guillen Torres, Media Studies, University of Amsterdam. Application information at https://www.digitalmethods.net.
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).