Ethnography Matters

Now that the initial heat has faded, it is a good time to place the Facebook experiment in historical perspective. In the first two quotes above, social psychologist Alan C. Elms and philospher-ethicist C.D. Herrera represent two sides of a debate over the ethics and efficacy of relying on deception in experimental research. I highlight these two quotes because they demonstrate moments within social psychology, even if they are a generation apart, when deception surfaces as a topic for reconsideration. Elms, one of the original research assistants involved in Stanley Milgram’s obedience research, writes as deception is being called into question. Herrera, writing with the benefit of hindsight, suggests that paradigms other than behaviorist are the way forward. The crux of this disagreement lies in the conceptualization of the research subject. Is the research subject a reflexive being with an intelligence on par with the researcher’s intelligence, or is the research subject a raw material to be deceived and manipulated by the superior intelligence of the researcher?

Danger, cows ahead
CC BY-SA akenator

Unseen, but looming in the background of this disagreement, is the Industrial Psychology/Human Relations approach, which developed in the 1920’s and 1930’s through the work of researchers like Elton Mayo and his consociates, and in experiments such as those at the Hawthorne plant.

This debate is worth revisiting in light of the Facebook experiment and its fallout. Any understanding of the Facebook experiment — and the kind of experimentation allowed by Big Data more generally — must include the long, intertwined history of behaviorism and experimental deception as it has been refracted through both Adam Kramer’s home discipline of social psychology and somewhat through his adopted discipline of “data scientist” [¹].Read More… The Facebook Experiment: Cow-Sociology, Redux

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David Ayman Shamma

Editor’s note: In the last post of the Ethnomining‘ edition, David Ayman Shamma @ayman gives a personal perspective on mixed methods. Based on the example of data produced by people of Egypt who stood up against then Egyptian president and his party in 2011, he advocates for a comprehensive approach for data analysis beyond the “Big Data vs the World” situation we seem to have reached. In doing so, his perspective complements the previous posts by showing the richness of ethnographic data in order to deepen quantitative findings.
David Ayman Shamma is a research scientist in the Internet Experiences group at Yahoo! Research for which he designs and evaluate systems for multimedia-mediated communication.

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There’s a problem we face now; the so called Big Data world created an overshadowing world of numerical data analysis leaving everyone else to try to find a coined niche like “small data” or “long data” or “sideways data” or the like. The silos and fragmentation is overwhelming. But really, it’s just all data. Regardless of the its form or flavor, there are people who are experts at number crunching data and people who are experts at field work data. Unfortunately, the speed at which data science moves is attractive and that’s part of the problem; we don’t get the full picture at speed and everyone is racing to produce answers first.

A few months ago, in a conversation with a colleague, he told me “you don’t know what you don’t know, especially when it’s not there.” We were looking for a way to automatically surface a community of photographers on Flickr who didn’t annotate their photos. They didn’t use any titles or tags or any annotations what so ever. But they were clearly a strong and prolific community. If there was some way to automatically identify them, then we could help connect them.

Now, finding metrics for social engagement in unannotated data is not an impossible task when provided with some signal in the data that has some correlation, statistical or otherwise, to the effect you’re trying to surface. But in some cases, it’s just not possible. What you need is just not there; therein is a problem. In other cases, it’s much harder to surface features when you don’t know what they look like.

When you have a lot of data, finding that unexplainable prediction through algorithmic statistics becomes easier. It doesn’t explain why and it doesn’t always work.

Enter Ethnography to answer the why and find out what things might look like—surfacing findings in the age of big data. When I was invited to write a post on Ethnography Matters, I decided to illustrate this through a personally motivated example.

In the late January of 2011, the people of Egypt stood up against then President Hosni Mubarak and his National Democratic Party. They wanted employment, a fair government, and an end to the 30 year long emergency law which had removed most of their civilian rights. Undoubtedly, you read about it somewhere. At the time, my mother was in Cairo visiting her 100+ year old mother. So this left me glued to the only source of news I could find—a rather buggy Al Jazeera video stream. U.S. news agencies were slow to start some sparse coverage. Somewhere in-between, it was burning up on Twitter.

A visualization of Twitter activity directed towards Tahrir by aymanshamma

Read More… Tweeting Minarets: A personal perspective of joining methodologies

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Fabien Girardin

Editor’s note: This post for the April ‘Ethnomining‘ edition comes from Fabien Girardin @fabiengirardin who describes his work with networked/sensor data at the Louvre Museum in Paris. Based on this inspiring case study, he discusses the overall process, how mixed-methods are relevant in his work, and what kind lessons he learnt doing this.

Fabien Girardin is Partner at the Near Future Laboratory, a research agency. He is active in the domains of user experience, data science and urban informatics.

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Visitor congestion at the Louvre Museum, picture by Fabien Girardin

At the Near Future Laboratory we like to experiment and to go in different directions from the typical technology consultancy. We thrive on the involvement of multiple practices, and bet on the unordinary when it comes to question formulation, data collection and solution creation. After completing my PhD in Computer Science, I left the bounded disciplines of academia to embrace learning and connecting to the other “fields”, the other ways of knowing and seeing the world. Along with partners Julian Bleecker, Nicolas Nova and a network of tactical scouts, we formed a technology-based practice that combines insight and analysis, design and research, and rapid prototyping to transform ideas into material form.

Over the past 5 years, I have led investigations that aim to extract knowledge from the byproducts of people’s digital activities (i.e. network data, also often called digital shadows or digital footprints). That intangible material can take the form of logs of cellular network activity, aggregated credit card transactions, real-time traffic information, user-generated content or social network updates. Over time my contributions have evolved into helping transform this type of big data into insights, products and services. Whether applied for a client or as part of our self-started initiatives, this practice requires the basic skills of a “data scientist” (data analysis, information architecture, software engineering and creativity) along with a capacity to engage at the intersections with a wide variety of professionals, from physicists and engineers to lawyers, strategists and designers. The transversal incline of investigations on network data requires understanding the different languages that shape technologies, reporting on the context of their use, and describing people’s practices. The model of inquiry blends qualitative field observations with quantitative evidence often extracted from logs.

The investigation with network data involves multiple practices and skills from engineering, to statistics, design, strategy planning, product management and ethnography; picture by Fabien Girardin

Past projects have led us to exploit untapped data sources, uncover opportunities to transform data into insights, and materialize new services or products. Our method first contemplates datasets and techniques to approach our objectives. Then we develop tangible solutions that engage the project stakeholders in exploring different scenarios and solutions. It is through the experiences of  people with knowledge of the project domain that we are able to extract possible near-future changes and opportunities.

Read More… Insights from network data analysis that yield field observations

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