Tag Archives: algorithms

Why do brands lose their chill? How bots, algorithms, and humans can work together on social media

Note from the Editor, Tricia Wang: The fourth contributor to the Co-designing with machines edition is Molly Templeton (@mollymeme), digital and social media expert, Director of Social Media at Everybody at Once, and one of the internet’s first breakaway YouTube stars. Her piece urges brands’ social media strategy to look beyond the numbers when working in the digital entertainment and marketing industry. Molly gives specific examples where algorithms don’t know how to parse tweets by humans that are coded with multiple layers of emotional and cultural meaning. She offers the industry a new way to balance the emotional labor in audience management with data analysis. Her articles draws on her work at Everybody at Once, a consultancy that specializes in audience development and social strategy for media, entertainment, and sports.

@Tacobell spent an hour sending this same gif out to dozens of people. The account is probably run by humans (most social media presences today are). And they were following best practice by “replicating community behavior,” that is, talking the way normal people talk to each other (a human taco bell fan would definitely send a gif). But when @tacobell only sends the same gifs out over and over again, it’s uncanny. It’s pulling the right answers from the playbook, but at the wrong frequency.  

Why do brands lose their chill?

I think that brands lose their chill when they don’t let their social media managers exercise empathy. The best brands on social media balance the benefit of interaction with the risk of human error – managers are constantly concerned with pissing off the organization, or the audience, and ultimately trying to please both sets of real people. Hitting campaign goals and maximizing efficiency are important, but social media managers need to bring humanity to their work. They have to understand the audience’s moods and where they’re coming from, and they have to exercise empathy at every level: customer service, information and content sharing, community management, call-to-actions, participation campaigns, crisis and abuse management. That is a lot of emotional labor.   

With the recent chatter about chat bots on Facebook’s messenger platform, a lot of people are thinking about how bots can take over communications roles from humans. I’ve been thinking a lot about the opposite: how can machines help people manage the emotional labor of working with audiences? Can bots ever help with the difficult, and very human task of managing with empathy?  

Social media is a business of empathy  

Emotional connections drive social media. When people gather around the things they feel passionate about, they create energy. It’s because of limbic resonance — the deep, neurological response humans have to other people’s emotions. As my colleague Kenyatta Cheese says, it’s that energy that makes participating as a fan on social media feel as electric as it does when you’re part of a physical crowd.  Read More… Why do brands lose their chill? How bots, algorithms, and humans can work together on social media

Falling in: how ethnography happened to me and what I’ve learned from it

guest author Austin Toombs

Austin Toombs

Editor’s Note: Austin Toombs (@altoombs) brings a background in computer science and a critical sensibility to his ethnographic research on maker cultures.  He explores the formation of maker identities in his research, focusing on how specific sites such as hackerspaces, makerspaces, Fab Labs, and other co-working spaces intersect with the politics of making, gendered practices, urban vs. rural geographies, and creative hardware and software developments. Austin is a PhD student in Human Computer Interaction Design in the School of Informatics and Computing at Indiana University. He is a member of the Cultural Research In Technology (CRIT) Group, and is advised by Shaowen Bardzell and Jeffrey Bardzell. He is also a member of ISTC-Social.

My research as a PhD student began by looking at cultures of participation surrounding hobbyist programming. I was—and still am—interested in the fuzzy-gray area between work and play, and as someone who misses the puzzle, thrill, and flow of programming, these communities were great starting points for me. Working on this research led me, almost inevitably, toward my ethnographic work with my local hackerspace and the broader maker community. In this context, I have seen how this local community embraces the work/play ambiguity, how it can function primarily as a social environment, and how it works to actively cultivate an attitude of lifelong, playful, and ad hoc learning. In this post I explore the role ethnography played in my work and how the ethnographic approach helped me get to these insights. I also discuss some of the complications and issues I have run into because of this approach, and how I am working toward solving them. For more information, feel free to contact me!


the role of ethnography in my work

My first encounter with the concept of a hackerspace came from my initial research on hobbyist programmers. I remember nearly dancing with excitement when I realized that the city I lived in happened to have a hackerspace, because I knew immediately that I would be joining them in some capacity, if not for research, then for my own personal enjoyment. The first few visits to the space were exploratory; I wanted to see what was going on, how the members and regular attendees interacted with each other, and whether or not this seemed like a good fit for my research.

My initial goal was to use the site as a potentially endless supply of case studies to explore my questions about work and play. Thankfully, I realized fairly early on that this case-study-first approach would not work for me. Instead, I found myself drawn to the overall narrative of the hackerspace and its members. How did this particular maker community form? What did the members do for their day jobs? How did they become ‘makers’? What do they think about themselves, and how has becoming a member of this community influenced that?

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Studying Up: The Ethnography of Technologists

Nick Seaver

Editor’s Note: Nick Seaver (@npseaver) kicks off the March-April special edition of Ethnography Matters, which will feature a number of researchers at the Intel Science and Technology Center for Social Computing on the forefront of exploring the cultures of hackers, makers, and engineers.

Nick’s post makes the case for the importance of “studying up“: doing ethnographies not only of disempowered groups, but of groups who wield power in society, perhaps even more than the ethnographers themselves.

Nick’s own research explores how people imagine and negotiate the relationship between cultural and technical domains, particularly in the organization, reproduction, and dissemination of sonic materials. His current project focuses on the development of algorithmic music recommendation systems. Nick is a PhD candidate in sociocultural anthropology at UC Irvine. Before coming to UCI, Nick researched the history of the player piano at MIT. 

When people in the tech industry hear “ethnography,” they tend to think “user research.” Whether we’re talking about broad, multinational explorations or narrowly targeted interviews, ethnography has proven to be a fantastic way to bring outside voices in to the making of technology. As a growing collection of writing on Ethnography Matters attests, ethnography can help us better understand how technology fits into people’s everyday lives, how “users” turn technologies to unexpected ends, and how across the world, technologies get taken up or rejected in a diverse range of cultural contexts. Ethnography takes “users” and shows how they are people — creative, cultural, and contextual, rarely fitting into the small boxes that the term “user” provides for them.

But ethnography doesn’t have to be limited to “users.”

Engineers in context. cc by-nc-nd 2.0 | http://www.flickr.com/somewhatfrank

My ethnographic research is focused on the developers of technologies — specifically, people who design and build systems for music recommendation. These systems, like PandoraSpotifySongza, or Beats Music, suggest listening material to users, drawing on a mix of data sources, algorithms, and human curation. The people who build them are the typical audience for ethnographic user studies: they’re producing technology that works in an explicitly cultural domain, trying to model and profile a diverse range of users. But for the engineers, product managers, and researchers I work with, ethnography takes a backseat to other ways of knowing people: data mining, machine learning, and personal experience as a music listener are far more common sources of information.

Ethnographers with an interest in big data have worked hard to define what they do in relation to these other methods. Ethnography, they argue, provides thick, specific, contextualized understanding, which can complement and sometimes correct the findings of the more quantitative, formalized methods that dominate in tech companies. However, our understandings of what big data researchers actually do tend to lack the specificity and thickness we bring to our descriptions of users.

Just as ethnography is an excellent tool for showing how “users” are more complicated than one might have thought, it is also useful for understanding the processes through which technologies get built. By turning an ethnographic eye to the designers of technology — to their social and cultural lives, and even to their understandings of users — we can get a more nuanced picture of what goes on under the labels “big data” or “algorithms.” For outsiders interested in the cultural ramifications of technologies like recommender systems, this perspective is crucial for making informed critiques. For developers themselves, being the subject of ethnographic research provides a unique opportunity for reflection and self-evaluation.

Starbucks Listeners and Savants

Among music tech companies, it is very common to think about users in terms of how avidly they consume music. Here is one popular typology, as printed in David Jennings’ book Net, Blogs, and Rock ‘n’ Roll:

Read More… Studying Up: The Ethnography of Technologists