Ethnography Matters

In this final post for The Person in the (Big) Data edition of Ethnography Matters, we provide a collection of five mixed methods used by researchers to shine a light on the people behind the massive streams of data that are being produced as a result of online behavior. These research methods use a variety of digital and traditional methods but they share one thing in common: they are aimed at discovering stories. As Tricia Wang wrote on EM back in 2013, ‘Big Data needs Thick Data’. While ‘Big Data delivers numbers; thick data delivers stories. Big data relies on machine learning; thick data relies on human learning.’ In the methods outlined below, researchers outline how they have made the most of digital data using innovative methods that uncover the meaning, the context, the stories behind the data. In the end, this is still the critical piece for researchers trying to understand the moment in which we are living. Or, put differently, the ways in which we may want to live but are often prevented from by a system that sometimes reduces the human experience rather than to enable its flourishing. – HF, Ed.

1. Real-time Audience Feedback: The Democratic Reflection Method

Democratic Reflection is a new methodological tool for researching the real-time responses of citizens to televised media content, which was developed by a team of researchers from the University of Leeds and the Open University in the UK as part of a larger research project on TV election debates. The research for the project began by developing an inductive understanding of what people need from TV election debates in order to perform their role as democratic citizens. Drawing on focus groups with a diverse range of participants, the research identified five key demands — or ‘democratic entitlements’ — that participants felt debates and the political actors involved in them should meet. Participants felt entitled to be: (1) addressed as rational and independent decision makers, (2) given the information needed to make considered political judgements, (3) included in and engaged by the debates, (4) recognised and represented by the political leaders, and (5) provided with meaningful choices that allow them to make a difference politically. In the next phase of the research, the research team developed a new web-based app (accessible via mobile phones, tablets, and laptops), which allows viewers to respond to televised debates in real time and evaluate them using a range of twenty statements based on the five democratic entitlements. An experiment using the Democratic Reflection app was conducted with a panel of 242 participants during the first debate of the 2015 UK General Election, generating a dataset of over 50,000 responses. Analysis of the data provides a valuable new way to understand how viewers respond to election debates: we can explore general patterns of responses, compare different individuals and groups, track changes over time, and examine how specific moments and performances during the debates may relate to particular responses. – Giles MossRead More… Five Mixed Methods for Research in the (Big) Data Age

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In this post for The Person in the (Big) Data edition of EM, we hear from Giorgia Aiello @giorgishka who
demonstrates the ways in which she used both digital and traditional methods to explore the people and practices that characterise the stock photography industry. Giorgia’s stories of photographers attempting to game the algorithms that determine which photographs will come out on top in a search for cheese are compelling and memorable, and they just show how important it is to develop an expanded idea of just what ‘data’ is constituted by, even if the dominant discourse is more limited. 

Image banks like Getty Images and Shutterstock that sell ready-to-use ‘stock’ photographs online have become the visual backbone of advertising, branding, publishing, and journalism. Also, daily exposure to stock images has increased exponentially with the rise of social networking and the generic visuals used in lifestyle articles and ‘clickbait’ posts. The stock imagery business has become a global industry through recent developments in e-commerce, copyright and social media (Glückler & Panitz, 2013).

However, stock images are most often overlooked rather than looked at—both by ‘ordinary’ people in the contexts of their everyday lives and by scholars, who have rarely taken an interest in this industry and genre in its own right. There are some notable exceptions, dating back to the ‘pre-Internet’ era of stock photography, like Paul Frosh’s work on the ‘visual content industry’ in the early 2000s or David Machin’s critical analysis of stock imagery as the ‘world’s visual language’ (Frosh, 2003; Machin, 2004). As a whole, and compared to other media and communication industries, research on online image banks and digital stock imagery is virtually uncharted territory.

Why, then, should stock images be ascribed any significance or power since people do not particularly pay attention to them? Stock images are not only the ‘wallpaper’ of consumer culture (Frosh, 2003 and 2013); they are also central to the ambient image environment that defines our visual world, which is now increasingly digital and global while also remaining very much analogue and local (just think of your own encounters with such imagery at your bank branch, at your dentist or beauty salon, or on billboards in city streets). Pre-produced images are the raw material for the world’s visual media.Read More… Taking Stock

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This edition of EM is jam-packed with methods for doing people-centred digital research and is edited by EM co-founder Heather Ford (@hfordsa)
newly-appointed Fellow in Digital Methods at the University of Leeds and thus super excited to understand her role as an ethnographer who (also) does digital methods.

Today we launch the next edition of Ethnography Matters entitled: ‘Methods for uncovering the Person in the (Big) Data’. The aim of the edition is to document some of the innovative methods that are being used to explore online communities, cultures and politics in ways that connect people to the data created about/by them. By ‘method’, we mean both the things that researchers do (interviews, memo-ing, member checking, participant observation) as well as the principles that underpin what many of us do (serving communities, enabling people-centred research, advocating for change). In this introductory post, I outline the current debate around the risks of data-centric research methods and introduce two principles of people-centric research methods that are common to the methods that we’ll be showcasing in the coming weeks.

As researchers involved in studying life in an environment suffused by data, we are all (to at least some extent) asking and answering questions about how we employ digital methods in our research practice. The increasing reliance on natively digital methods is part of what David Berry calls the “computational turn” in the social sciences, and what industry researchers recognize as moves towards Big Data and the rise of Data Science.

‘Digital Methods‘ by Richard Rogers (2013)

First, a word on digital methods. In his groundbreaking work on digital methods, Richard Rogers argued for a move towards natively digital methods. In doing so, Rogers distinguishes between methods that have been digitized (e.g. online surveys) vs. those that are “born digital” (e.g. recommender systems), arguing that the Internet should not only be seen as an object for studying online communities but as a source for studying modern life that is now suffused by data. “Digital methods,” writes Rogers, “strives to follow the evolving methods of the medium” by the researcher becoming a “native” speaker of online vocabulary and practices.

The risks of going natively digital

There are, however, risks associated with going native. As ethnographers, we recognize the important critical role that we play of bridging different communities and maintaining reflexivity about our research practice at all times and this makes ethnographers great partners in data studies. Going native in this context, in other words, is an appropriate metaphor for both the benefits and risks of digital methods because the risk is not in using digital methods but in focusing too much on data traces.

Having surveyed some of debates about data-centric methodology, I’ve categorized the risks according to three core themes: 1. accuracy and completeness, 2. access and control, 3. ethical issues.Read More… The Person in the (Big) Data

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