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Trace ethnography: a retrospective


Stuart GeigerStuart Geiger @staeiou continues our edition of ‘The Person in the (Big) Data‘ with a reflection on his practice of ‘trace ethnography’ that focuses on the trace-making techniques that render users’ activities and intentions legible to each other. Importantly, Stuart argues, we as researchers need to see these traces in the context of our active socialization within the community in question, rather than passively reading traces through lurking. 

When I was an M.A. student back in 2009, I was trying to explain various things about how Wikipedia worked to my then-advisor David Ribes. I had been ethnographically studying the cultures of collaboration in the encyclopedia project, and I had gotten to the point where I could look through the metadata documenting changes to Wikipedia and know quite a bit about the context of whatever activity was taking place. I was able to do this because Wikipedians do this: they leave publicly accessible trace data in particular ways, in order to make their actions and intentions visible to other Wikipedians. However, this was practically illegible to David, who had not done this kind of participant-observation in Wikipedia and had therefore not gained this kind of socio-technical competency. 

For example, if I added “{{db-a7}}” to the top an article, a big red notice would be automatically added to the page, saying that the page has been nominated for “speedy deletion.” Tagging the article in this way would also put it into various information flows where Wikipedia administrators would review it. If any of Wikipedia’s administrators agreed that the article met speedy deletion criteria A7, then they would be empowered to unilaterally delete it without further discussion. If I was not the article’s creator, I could remove the {{db-a7}} trace from the article to take it out of the speedy deletion process, which means the person who nominated it for deletion would have to go through the standard deletion process. However, if I was the article’s creator, it would not be proper for me to remove that tag — and if I did, others would find out and put it back. If someone added the “{{db-a7}}” trace to an article I created, I could add “{{hangon}}” below it in order to inhibit this process a bit — although a hangon is a just a request, it does not prevent an administrator from deleting the article.

File:Wiki Women's Edit-a-thon-1.jpg

Wikipedians at an in-person edit-a-thon (the Women’s History Month edit-a-thon in 2012). However, most of the time, Wikipedians don’t get to do their work sitting right next to each other, which is why they rely extensively on trace data to coordinate render their activities accountable to each other. Photo by Matthew Roth, CC-BY-SA 3.0

I knew all of this both because Wikipedians told me and because this was something I experienced again and again as a participant observer. Wikipedians had documented this documentary practice in many different places on Wikipedia’s meta pages. I had first-hand experience with these trace data, first on the receiving end with one of my own articles. Then later, I became someone who nominated others’ articles for deletion. When I was learning how to participate in the project as a Wikipedian (which I now consider myself to be), I started to use these kinds of trace data practices and conventions to signify my own actions and intentions to others. This made things far easier for me as a Wikipedian, in the same way that learning my university’s arcane budgeting and human resource codes helps me navigate that bureaucracy far easier.Read More… Trace ethnography: a retrospective

About a bot: Materiality, multiplicity, and memory in the study of software agents


Stuart Geiger (@steaiou)

Stuart Geiger

Editors’ note: The next post for our Ethnographies of Objects edition is by one of the people who inspired it when he talked about an ‘ethnography of robots’ for EM last year. Stuart Geiger (@staeiou) is a PhD student at UC Berkeley’s School of Information and long time Wikipedia editor who has been studying Wikipedia bots for many years and who has brought us really great insights: not only into how Wikipedia works but also on new ways of thinking about how to do ethnography of largely-online communities. In this thoughtful post, Stuart talks about how his ideas about bots have changed over the years, and about which of the images below is the “real” bot.     

A few weeks ago, Heather Ford wrote to me and told me about this special edition of Ethnography Matters, focusing on the ethnography of objects.  She asked me if there was something I’d like to write about bots, which I’ve been struggling to ethnographically study for some time.  As I said in an interview I did with EM last year, I want to figure out how to ethnographically study these automated software agents themselves, not just the people who build them or have to deal with them.  Among all the topics that are involved in the ethnography of objects, Heather briefly mentioned that she was asking all the authors to provide a picture of their given object, whatever weird form that may take for bots.

At first, I started to think about the more standard epistemological questions I’d been wrestling with:  What is the relationship between the ethnographer and the ethnographic subject when that subject isn’t a human, but an autonomous software program?  What does it mean to relate an emic account of a such a being, and what does ethnographic fieldwork look like in such an endeavor?  How do classic concepts like agency, materiality, and the fieldsite play out when investigating what is often seen as more of an object than a subject?  What do we even mean when we say ‘object’, and what are we using this term to exclude?  I could take any one of these topics and write far too much about them, I thought.

As always, after jotting down some notes, my mind started to wander as I entered procrastination mode. I shelved the more ‘theoretical’ questions and moved to what I thought was the easier part of Heather’s request: to provide a photo of a bot.  I thought that finding an image would be a fun diversion, and I had so many great cases to choose from.  There were humorous bots, horrifying bots, and hidden bots.  There were bots who performed controversial tasks, and bots whose work was more mundane.  There were bots I loved and bots I hated, bots that were new and bots that were old.  There were bots I knew backwards and forwards, and bots who were still a mystery to me.  I just had to find an image that I felt best encapsulated what it meant to be a bot, and then write about it.  However, I didn’t realize that this simple task would prove to be far more difficult than I anticipated — and working out how to use imagery rather than text to talk about bots has helped me come to articulate many of the more complicated issues at work in my ethnography, particularly those around materiality, multiplicity, and memory.

Read More… About a bot: Materiality, multiplicity, and memory in the study of software agents