algogossip is an exploratory project I’m currently developing, which was recently tested out at the 2022 Internet Yami-Ichi.

Last month I called up my friend, a full-time content creator. I’ve been reading a lot about the tactics people use to increase the chances of their posts being seen on social media, so I asked: Where did she go to get advice and tips for how she posts? 

When you consider how many people rely on algorithmic visibility on social media for financial stability, questions around how social media algorithms work become absolutely critical. According to a recent SignalFire report, roughly 2 million people work as full-time, professional content creators on platforms like YouTube, Instagram, and Twitch. These platforms do not provide detailed information about how their algorithms work, at the risk of being gamed. But at the same time, when changes to the algorithm are rolled out without warning, content creators are left scrambling trying to make sense of the changes. 

Background

Over the past few years, I’ve been thinking about the stories people tell each other about their everyday interactions with social media algorithms. As part of my grad school thesis research in 2016, I asked people to share their stories about Facebook’s ad targeting: What kinds of strange experiences did they have with targeted ads? During my time as a Ford-Mozilla fellow in 2017, I collaborated with Coding Rights to explore this question further: What experiences were women having on the platform? What constitutes algorithmic harm? We talked to women who had had unsettling or confusing experiences with Facebook’s targeted ads. Most recently at Mozilla, I’m leading qualitative research that aims to get at the heart of people’s experiences with YouTube’s user control mechanisms: Do they feel like they have meaningful control over the system? How do they change their behavior in an attempt to exert control?

My research has been informed by scholars writing about how people engage with social media algorithms. Taina Bucher has written extensively about the “algorithmic imaginary” (Bucher 2017), the animating force of social media algorithms, ways of thinking about what algorithms are, what they should be, and how they function. She argues that the stories people offer up about algorithms are important because they have real social impact. Michael Ann DeVito and others argue that the folk theories people hold about social media algorithms serve as frames through which we can understand their reactions to change (DeVito et al. 2017). They say that by looking seriously at the complaints people make about algorithms, we can better understand the nature of “expectation violations.”

A paper about how users exercise control over social media algorithms (Burrell et al. 2019), says that the complaints people make about social media algorithms are important feedback signals. Citing Sara Ahmed’s writing on complaint as a feminist tactic, they write that “the act of complaint itself can be a way for people to record their grievances and build solidarity in the face of limited recognition by those with organizational power.” (Ahmed has since published a book titled Complaint! that looks at how complaints are made and what they can do, specifically through a Black feminist and feminist of color lens.)

My thinking on this project has been most shaped and inspired by Sophie Bishop’s excellent scholarship on the concept of “algorithmic gossip,” a term she defines as “communally and socially informed theories and strategies” about social media algorithms that people share with one another in order to boost financial stability and visibility on social media platforms (Bishop 2019). She says that “gossip is productive” and that it is an “important and under-studied form of knowledge production.”

Gossip, especially in its association with women, has historically been looked down upon and treated as trivial, intimate, and dangerous. It’s also a tactic that’s wielded in situations where a power asymmetry exists, and most often it’s wielded by marginalized groups. I think about the whisper networks at universities or at companies that have warned newcomers about problematic individuals, or have allowed people to quickly share important knowledge. Most importantly, gossip serves to subvert power: In the absence of good, accurate information about how a system works, people rely on one another to make sense collectively. 

The project

Back to the question I asked my friend: Where did she go to get advice and tips for how she posts? She told me that she was part of a group text with other friends who were content creators, where they shared tips and offered support. Many of them subscribe to industry newsletters or work with agencies who give them advice about how and what to post. Others seek out internet forums for answers to their questions. 

I started looking into some internet forums where these conversations take place. There are a number of subreddits dedicated to answering people’s questions about how the TikTok algorithm works, advice for boosting visibility on Instagram’s algorithm, avoiding/appealing shadowbans, and similar topics. The posts in these forums range from the didactic (“Post at least 6 times a day. Upload history like 2 of those. HASHTAGS VERY important.”) to the supportive (“Why don’t you try some challenges ? Like challenge people to do ex: 5 pushups everyday and have your own hashtag.”). There is a real sense of camaraderie, with posts expressing frustration  (“I have been banned for more than a month now and they are not reviewing my appeal”) and affirmation (“Yeah this has been happening to my videos as well.” I decided to use these posts as a starting point from which I could explore further. 

Coding the project

After setting up Reddit API credentials, I scraped posts and their comments from these subreddits using Reddit’s PRAW, filtered by specific flairs (e.g. “Algorithm Question / Shadowbanned”). I imported a Python module written by Prakhar Rathi, and then wrote a script that would scrape the posts and comments and save the dataset as a CSV. I combined the data and converted it into a JSON file.

I thought about what I wanted to do with this dataset of ‘algorithmic gossip’, especially in an art gallery setting. I considered curating a selection of the data in a book or zine. I also considered building an ML model to generate new advice from this dataset.

I thought about some of the previous exploration of voice technology I had done with my collective tendernet, and considered the ways we think about gossip as spoken: it has an aural quality. I got really excited about the creative potential for a voice interaction – could you call a phone number and get a voice message? Pick up an object, put it to your ear, and a message plays? With one tendernet collaborator Zoe Bachman, we brainstormed some ideas and agreed on an aesthetic: y2k tech girlie. Another collaborator Katrina Peterson took the aesthetic concept and iterated some cute logo designs.

Ahead of the 2022 Internet Yami-Ichi, I decided to build a web-based piece (see the prototype here) that employs text-to-speech. I built the website using JavaScript, making use of the p5.js and p5.speech.js libraries. Each time you click the page, a new piece of ‘algorithmic gossip’ appears and is read out loud in an unnatural-sounding robotic voice. I tested out different qualities of the voice, including testing out a “whisper” (it sounded terrifying).

Testing it out

Testing out the piece at the Internet Yami-Ichi was a lot of fun. The energy of the event was very much a cross between an art book fair, a bazaar, and an art gallery. I talked to lots of people who came through about the concept behind the project and got some great ideas. I was also inspired by hearing more about Angie Waller’s work with Unknown Unknowns; she is also working with comments and pictures scraped from the internet.

I want to continue refining and exploring this dataset through different creative explorations. 

References

Sara Ahmed. 2021. Complaint! Duke University Press, Durham.

Sara Ahmed. 2018. Refusal, Resignation and Complaint. feministkilljoys. Retrieved May 7, 2022 from https://feministkilljoys.com/2018/06/28/refusal-resignation-and-complaint/

Sophie Bishop. 2019. Managing visibility on YouTube through algorithmic gossip. New Media & Society 21, 11–12 (November 2019), 2589–2606. DOI:https://doi.org/10.1177/1461444819854731

Sophie Bishop. 2020. Algorithmic Experts: Selling Algorithmic Lore on YouTube. Social Media + Society 6, 1 (January 2020), 205630511989732. DOI:https://doi.org/10.1177/2056305119897323

Taina Bucher. 2017. The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information, Communication & Society 20, 1 (January 2017), 30–44. DOI:https://doi.org/10.1080/1369118X.2016.1154086

Michael A. DeVito, Darren Gergle, and Jeremy Birnholtz. 2017. “Algorithms ruin everything”: #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17), Association for Computing Machinery, New York, NY, USA, 3163–3174. DOI:https://doi.org/10.1145/3025453.3025659

Michael Ann DeVito. 2021. Adaptive Folk Theorization as a Path to Algorithmic Literacy on Changing Platforms. Proc. ACM Hum.-Comput. Interact. 5, CSCW2 (October 2021), 1–38. DOI:https://doi.org/10.1145/3476080

A few months ago I bought a beautiful hand-woven object off the internet. The object measures 5.5 inches by 5.5 inches and consists dozens of thin threads tightly woven through small beads, strewn across a square resin frame.

When it first arrived, I enjoyed challenging friends by showing them the object without any context and asking them to identify what it is. One group of friends thought that it was some kind of weaving device. “Do you weave with it?” one friend asked. “Small loom for patching clothes,” guessed another. At first another friend thought it was a loom, but upon closer examination he noticed that the tiny threads woven throughout it are, in fact, thin wire filaments. Does it carry an electric charge, he asked?

He was right. The object is what is known as a “ferromagnetic core memory,” an antiquated form of computer memory. As I started researching the origin of the object, I learned more about how the histories of computation, memory, textile production, and labor are intertwined.

Core memory was first developed in the 1950s and was the most common type of random-access computer memory until 1975. Random-access memory (RAM) is a type of computer memory that can be accessed at any time, regardless of when it was saved. Core memory works as follows: Wires are tightly laced through small ferrite rings (known as cores). Ferrite is used because it becomes magnetized when exposed to magnetic fields. Electric currents are sent through the wire, which creates magnetic fields. The core can be polarized negatively or positively based on magnetic fields operating in opposite directions (i.e. switching the polarity). Those opposing polarities correspond to 1 or 0, the components that make up bits and bytes.

Weaving core memory

In the United States, the lightweight quality of hand-woven core rope memory, “a technique of physically weaving software into high-density storage,” powered the early Apollo Guidance Computer that put the Apollo on the moon. The history of core rope memory has been well-documented: highly skilled weavers and craft workers, most of whom were women, worked in a Raytheon factory in Waltham, Massachusetts to weave the core rope memory. There was a gendered aspect to this labor: The core rope was referred to as LOL memory (“Little Old Lady” memory). Journalists, engineers, and even a manager at Raytheon allegedly described this work as requiring no thinking and no skill.

The software for flights was managed by a “rope mother” (who was usually male), although Margaret Hamilton, who is best remembered for overseeing the development of the Apollo software, was rope mother on the Luminary.

In their paper “Making Core Memory: Design Inquiry into Gendered Legacies of Engineering and Craftwork,” Daniela Rosner and others explore how the high-status, male labor of building computers was powered by low-status craftwork largely carried out largely by women (specifically, women of color). According to Rosner, the work performed at Raytheon was described as “tender loving care” by the man who oversaw the Apollo Guidance Computer’s hardware.

Lisa Nakamura interrogates these ideas in her paper “Indigenous Circuits: Navajo Women and the Racialization of Early Electronic Manufacture”, which looks at the indigenous women who built integrated circuits for the Apollo Guidance Computer. From 1965-1975, the Silicon Valley company Fairchild Semiconductor ran a circuit manufacturing plant in New Mexico on Navajo land where Navajo women were employed. Nakamura demonstrates how racialized notions of labor shaped how value was conferred on the engineering/craftmanship work those Navajo women carried out. According to Nakamura, the work the Navajo women did was described as “affective labor, or a ‘labor of love.’”

The critical contributions of these craftworkers – both the women weaving core memory in MA and the women building integrated circuits in NM – were systematically undervalued and largely erased from computing history until recently. Gendered and racialized notions about what is considered “real” tech work persist today.

The Saratov-2 computer

The ferrite core memory plate I own is a relic of Soviet computing history. The Saratov-2 microcomputer from which my core memory plate is from appears to have been uncovered in the ruins of a fire. Russian urban explorer Ralph Mirebs describes his discovery in a 2020 blog post “Cemetery of Soviet Computers.” Apparently no photos remained of this “legendary machine,” the Saratov-2, until the author came across the ruins. (He declined to share the location). The Saratov-2 is apparently rare enough that no examples were believed to have survived until this discovery.

The Saratov-2 was a clone of the US minicomputer PDP-8/M. Cloning US computers was common practice at the time: In the 1970s, the USSR began getting its hands on PDP minicomputers with the intent of copying them. The PDP-8 was allegedly acquired by the USSR from a sunken US submarine, and then reverse engineered by the Central Research Institute of Measuring Equipment (ЦНИИИА) in the city of Saratov. At least that’s what the Etsy posting says – a blog post written by another computer hobbyist investigates this claim further and can’t confirm it.

What made the Saratov-2 unique was that it didn’t have a microprocessor. Instead, it was broken down into its individual components, which sat in drawers. The ferrite core memory cube, the microcomputer’s RAM, was located in one such drawer.

What about the Saratov-2 core memory plate I own? What handiwork and labor did it require? It’s difficult to say who hand wove the core memory that powered these early microcomputers, since information about them is scant.

According to Ralph Mirebs (site in Russian), the decimal number on the core memory plate I own begins with KhSHM, which suggests the plate was manufactured at the Central Institute of Measuring Equipment in Saratov, Russia during the 1970s. The Central Institute of Measuring Equipment (TsNIIIA for short) was founded in 1958 and specialized in the manufacturing of electronic devices, including magnetic materials and integrated circuits. This is where the Saratov-2 minicomputer was developed.

I was able to track down the location of the Central Institute of Measuring Equipment. The cluster of TsNIIIA buildings are located at the intersection of Moskovskaya and Radishcheva streets in Saratov, Saratov Oblast, Russia.

The TsNIIIA closed in 1991, and a joint stock company TsNIIIA took its place. In 2017, the owners tried to sell the 32 buildings but failed. In 2021, it was announced the buildings would be turned into a “technocenter.” It’s unclear what the buildings are currently used for.

So, who were the artisans who worked in a TsNIIIA building to weave core memory for the Saratov-2 microcomputers? I’m really not sure. If anyone has more information, I’d love to learn more.

References

Nakamura, Lisa. “Indigenous Circuits: Navajo Women and the Racialization of Early Electronic Manufacture.” American Quarterly, vol. 66, no. 4, 2014, pp. 919–41. DOI.org (Crossref), https://doi.org/10.1353/aq.2014.0070.

Rankin, Joy Lisi. “Core memory weavers and Navajo women made the Apollo missions possible.” Science News. https://www.sciencenews.org/article/core-memory-weavers-navajo-apollo-raytheon-computer-nasa. Accessed 21 Apr. 2022.

Rosner, Daniela. Critical Fabulations: Reworking the Methods and Margins of Design. The MIT Press, 2018.

Rosner, Daniela K., et al. “Making Core Memory: Design Inquiry into Gendered Legacies of Engineering and Craftwork.” Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, ACM, 2018, pp. 1–13. DOI.org (Crossref), https://doi.org/10.1145/3173574.3174105.

Shorey, Samantha, and Daniela Rosner. “A Voice of Process: Re-Presencing the Gendered Labor of Apollo Innovation.” Communication  1, vol. 7, no. 2, Mar. 2019, https://doi.org/10.7275/yen8-qn18.

“Software Woven into Wire: Core Rope and the Apollo Guidance Computer.” http://www.righto.com/2019/07/software-woven-into-wire-core-rope-and.html. Accessed 14 Jan. 2022.