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.
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.
- Accuracy and completeness:
Focusing only on data traces recorded on platforms rather than data in context can result in problems relating to accuracy and completeness. Critics focus on the multiple meanings of data and the inability of quantified data research to expose the fact that a Facebook ‘like’, for example, can represent either agreement or sympathy. In my research on Wikipedia editors of the 2011 Egyptian Revolution article, for example, I found through interviews that the most prolific editor of the article had actually shared his user details with two other friends located outside of Egypt. This experience and others led me to recognise that the data traces that we often employ are incomplete at best and inaccurate at worst. Relying only on the data that we can access can be severely problematic as we try to gain stronger insights into experiences that are mediated by digital data.
- Access and control:
A significant problem with digital methods is that the locus of control over digital data is often heavily weighted towards commercial platforms that can decide when and where to grant access. Rogers describes the ways in which the software that powers platforms can change overnight so that the tools we build as researchers to interface with that software is “scooped” by the objects (p25). Others have argued that, even when we do gain access to data on platforms like Twitter, there is some obscurity over whether we are, in fact, gaining access to the complete set of data and that only chosen researchers will obtain access to preferential access by platforms.
- Ethical issues:
Research that employs digital data introduces a number of ethical questions including issues around the consent of data subjects, questions around the positioning of users as subjects of data and the missing masses of data i.e. people and communities who are not represented by data and are thus made ever more invisible within social systems. In their critical approach to data, Dalton and Thatcher note that it is important at this stage to ask: “Whose data? On what terms? To what ends?” Others have questioned the positioning of data as somehow “raw” and apolitical, calling for research that exposes the systemic bias of data systems by charting the consequences for “how the world is known, governed and lived-in” (Kitchin and Lauriault, 2014).
The problem that we as researchers are faced with is that few solutions are offered in order to engage with data in people-centric (as opposed to data-centric) ways. In response to data critiques, many of us have resolutely denied the existence of data, conveniently believing that this is not what ethnographers (or qualitative researchers) do.
But there are two problems with this response. The first is that, like it or not, we all do digital methods to some extent, but not all of us use data in considered ways. As my colleague (and contributor to this edition as well as previous editions) Stuart Geiger will argue at the upcoming ICA conference in Fukuoka: “all researchers are mixed-methodological to some extent, and it is dangerous to obscure the more supplementary methods we inevitably find ourselves using.” Secondly, when we use digital methods, we’re not studying the world apart from the “real” or physical world. Digital methods, as Richard Rogers writes, can be used not only to study the Internet but to use the Internet to study the social world.
This edition aims to start to fill this gap, with a series of posts from researchers who are employing innovative methods for uncovering meaning, motivations and the daily practices that result in data traces. The researchers sharing their practices in the coming weeks don’t all identify as ethnographers. Ethnography requires using a suite of methods and there are multiple methods that ethnographers share with researchers who engage with data.
The methods that are being showcased here have two principles in common that make this edition different from the hang-wringing that often accompanies debates about methods. The first is that data traces are used as a single source among others; the second is a focus on people-centric rather than data-centric models.
- Data traces are a single source
In order to combat the inevitable incompleteness of digital data traces (both big and small), it is necessary to combine data traces found on digital platforms with other sources of data. Researchers in this edition combine analysis of data traces with interviews, participant observation and critical reflection in order to understand questions around behavior, motivation and meaning. Giles Moss and others (forthcoming in this edition) used interviews and focus groups to find out what UK citizens wanted from their politicians during election debates and used these principles to develop an app for evaluating audiences responses to debates in real time. Stuart Geiger (forthcoming in this edition), on the other hand, has learned that the practices and vocabularies unique to platforms like Wikipedia constitute an essential part of what it means to participate in many communities and organizations and that reading through log data can be seen as a form of participation, not just observation in online communities.
Both Moss and Geiger supplement the data traces that they extract from systems with other methods, both in order to enrich the data and to verify the patterns emerging from the data. Recognising the need to employ multiple sources of data is an important principle for rigorous empirical research both because the completeness of data samples is often obscured to researchers and because digital data traces do not stand in for the individual who created it, either actively (in the form of constructed content) or passively (in the form of data exhaust).
- People-centric vs. data-centric
The researchers participating in this edition also recognize the important role that they play as agents of informed change in society. Instead of just giving a lecture to sex educators about the meanings that young people attribute to selfies, Kath Albury (forthcoming in this edition) for example conducted a workshop in which educators learned the basics of media theory and created (and reflected upon) their own selfies. The workshops equipped the educators with an embodied understanding of the media practices that their students were engaging in and a language and vocabulary with which to analyse and understand such practices. Instead of making claims about influential social media users being represented by data through visualisations, Elizabeth Dubois presented those users with visualisations in the interview setting, enabling them to speak back to the data being created about them.
Employing people-centric methods requires refocusing on the person as the object rather than subject of data and asking questions about “Whose data this represents? What was the experience of producing this data? What are the consequences of this data for the person?” In doing so, researchers are able to move from a data-centric to a people-centric model and to focus on human problems and solutions as the subject for research.
A word on method
It is exciting to me that digital methods have re-energised questions around methods and methodology that have laid in stasis for too long. Research methods are our craft! When we talk about methods, we’re talking about how we work, which tools we choose and how we choose to conduct ourselves in the world. Talking about methods is a wonderful way of recognising that researchers are makers. We make things. In order to make beautiful things, we need to be mindful of the tools we use and the recipes we use to create. In the same way that a potter might deliberate over the choice of paint colors and maintain those paints in neatly labeled jars, or a chef might select the best knives and keep them immaculately clean, the researchers in this edition of EM care very deeply about their craft and the tools they use to make things with. For them, research is not only about what they produce but the ways in which they produce it.
In the next few weeks, researchers from around the world will be reflecting on the methods that they have used to uncover the person in the data. I hope that you are as inspired as I have been by their practice.
Thanks to the Communities and Culture Network+ for supporting some of the work that has gone into this edition, to the amazing crew at the Digital Methods Summer School, QUT, Australia for helping to source some of the methods and posts and to my colleagues at the Leeds’ School of Media and Communications who are experimenting with digital methods in careful, nuanced ways. Thanks, lastly, to the contributors of this edition: thank you for taking time to share your methods with the community. Your generosity is greatly appreciated.
Like what you’re reading? Ethnography Matters is a volunteer run site with no advertising. We’re happy to keep it that way, but we need your help. We don’t need your donations, we just want you to spread the word. Tweet about articles you like, share them with your colleagues, or become a contributor. Also join us our Slack to have deeper discussions about the readings and/or to connect with others who use applied ethnography. Help us bring ethnography to a wider audience.