See You Later, Thick Data – Preface

Anthrodendum is pleased to welcome guest bloggers Sofie, Clara, and Emilie. They are a group of junior scholars working as part of the interdisciplinary research project called DISTRACT, studying the dynamics of issue attention at a political festival. Here the trio has been experimenting with approaches to collect ethnographic data that is integrable with other data types.

Sofie Læbo Astrupgaard is a PhD fellow in Social Data Science at the University of Copenhagen, and she holds a BSc in Anthropology. Sofie’s research focuses on hybrid workplaces, and she likes to explore how data from her ethnographic fieldwork can be combined with large scale unstructured data in meaningful ways in her research.

Clara Rosa Sandbye is a PhD fellow at the Department of Anthropology at Aarhus University, where she does research within the field of restorative justice, concerning issues of criminalization, violence, and morality. She likes exploring the possibilities of collective ethnography, interdisciplinary, and mixed-methods research.

Emilie Gregersen is an MSc student in Social Data Science at the University of Copenhagen and holds a BSc in Anthropology. Her interests include experimenting with traditional ethnographic methods in combination with computational tools and methods from other disciplines, and she hopes one day to become her own version of a computational anthropologist.

See You Later, Thick Data

This blogpost is the introduction to the methodological series “See You Later, Thick Data – How we experimented with doing collaborative fieldwork as part of an interdisciplinary research project”. Through five blogposts, we – a group of anthropologically trained junior scholars – discuss some of the opportunities and challenges we faced when collecting data in an interdisciplinary case study of the Danish political festival The People’s Meeting. We took on a somewhat different approach to ethnographic fieldwork, and in this series, we share our experiences with a highly systematic and collaborative way of collecting ethnographic data that is integrable with different data types. In this introduction, we present the context of our study, including a description of The People’s Meeting and the emerging field of machine anthropology.

Thrown into a world of social data science

Our interest in the field of social data science started back in 2019 as an extra-curricular project, taking up our spare time while writing up each of our bachelor’s theses in anthropology at the University of Copenhagen. We had volunteered as “test subjects” at the Copenhagen Center for Social Data Science (SODAS) and we wondered what it would take for a group of anthropology students to learn how to code. This was how we were first introduced to the curious combination of anthropology and computational methods.

A group of anthropologists takes a crash course in programming. It almost sounds like the first phrase of a bad joke: How many anthropologists does it take to make a function in the programming language Python? It took some work and some getting used to, that’s for sure. Shortly after the course, we came to work at SODAS for several years. The groundwork for this blogpost series is a research project we took part in while working at SODAS.

In brief terms, SODAS is an interdisciplinary research and education center at the Faculty for Social Sciences, UCPH. The center houses researchers from disciplines as diverse as anthropology, economics, sociology, political science, psychology, and data science. The aim is to expand the scientific toolbox by introducing research methods from data science to social science research. The digitalization of societies creates immense quantities of digital data that offer important insights into social life today – and SODAS has deemed it its mission to utilize this data in social scientific research.

Being thrown into the world of social data science taught us important lessons about anthropology. The clash with other methodologies, epistemologies, and data types forced us to consider fundamental questions: What are the qualities of anthropological approaches and ethnographic methods? What aspects of social life can they illuminate, and what aspects call for other methods? And maybe most important; how can these different methods and diverse data types be combined in a meaningful way?

Machine anthropology – Bringing together anthropology and data science

As junior anthropology scholars with a fondness for coding, we are indeed inspired to by machine anthropology. The term has been coined by our supervisor Morten Axel Pedersen to capture ongoing attempts by interdisciplinary research teams at SODAS and elsewhere to explore what an integration between anthropology and data science might look like[1]. Drawing on but also going beyond recent attempts to mix thick ethnographic data with thin big data (e.g. Isfeldt et al. 2019), machine anthropology aspires to unsettle established disciplinary, methodological and epistemological boundaries by using computational methods for augmenting and automatizing the collection, processing and analysis of ethnographic data, and vice versa. The approach we present in the blogpost series can be considered machine anthropology as we attempt to gather ethnographic data that is integrable with quantitative data types, and this implies that the data can be computationally processed.

The research project – DISTRACT and DISTRACT Politics

The study is part of two research projects: DISTRACT: The Political Economy of Attention in Digitized Denmark and ”Ethnographic Text as Data”[2]. DISTRACT brings together diverse social science and data science methods to explore the mental, social and material techniques by which attention is captured, retained, and distracted in the world’s most digitized country, Denmark. One subproject, DISTRACT Politics, combines qualitative (e.g. fieldwork observations) and quantitative data (e.g. social media data) to map and mine the dynamics of political events (Meinert & Kapferer 2014), and to contribute to sociological work on how “issue attention” flows between politicians, media and publics across digital and non-digital media (e.g. Barbera et al 2019). Ethnographic Text as Data seeks to experiment with the use of computational methods for the collection, processing, and analysis of ethnographic data; to develop a theoretical framework for a future computational anthropology, and to contribute to and expand the quali-quantitative toolbox for the social scientific study of political processes and events. The research project our blogpost series is based on falls under the Political Attention subproject while our methodological framework is developed with Ethnographic Text as Data in mind.

The People’s Meeting as research case

The People’s Meeting is an annual political festival, which takes place in the old fishing village of Allinge on the Danish Island of Bornholm in the Baltic Sea. The festival lasts for four days and was established with the official goal of bringing together citizens and decision-makers and facilitating a democratic dialogue. Within this framework, public and private stakeholders organize events such as debates and speeches. Since the festival was launched in 2011, it has grown bigger each year. In 2019, the festival peaked by attracting more than 114.000 visitors. However, due to covid-19 regulations, the festival was cancelled in 2020, and in 2021, it was considerably downscaled to around 8.000 visitors and 450 events.

The issues debated range from human rights, climate issues, working conditions, public health, and much more. Some events are TV-transmitted and live-streamed, and it’s not unusual that politicians use this occasion to announce new and often substantial political messages. In the past years, the People’s Meeting has been more present in the online sphere as well as stakeholders promote their activities at the festival, while politicians and activists continue discussions raised during debates on social media.

In many ways, The People’s Meeting resembles a microcosm of the political landscape in Denmark. The festival attracts the most influential political actors and stakeholders, as well as the media and smaller, more locally anchored organizations. It also attracts thousands of members of general public, ranging from well-to-do retirees to high-school and university students attracted by the large amounts of free beer and snacks handed out by various stakeholders

The People’s Meeting, then, is a perfect setting to study political attention in Denmark. Indeed, as one of our scholarly collaborators, Lasse Liebst points out, festivals ”offer a natural laboratory” (2019: 30) for systematic empirical social science investigation. In addition, it represents an ideal site for interdisciplinary collaboration and machine anthropological experimentation. So, in collaboration with the non-profit organization behind The People’s Meeting, qualitative and quantitative data pertaining to micro-sociological processes and political attention dynamics pertaining to this “natural laboratory” was what we sought to collect in June 2021.

A DISTRACT expedition to the People’s Meeting

We were a team of 10 DISTRACT researchers, who travelled to Bornholm in June 2021. Our primary goal was to test fundamental social theories about so-called interaction rituals and attention dynamics (we will expand on this theory later). A second objective was to experiment with methods for collecting, processing and integrating radically different kinds of social data ranging from ethnographic fieldnotes to sensor data and social media data. Our team was composed of both senior and junior scholars. Half of us had a background in anthropology, and disciplines such as sociology, economy, political science, and social data science were also represented among team members.

All together, we collected a large pool of different data during the festival. While our ethnographic methods such as observations and interviews are the focus of this blogpost series, as alluded to, our team also collected quantitative and digital data, including sensor data, weather data, social media data, as well as register data and survey data we obtained from the organizers of The People’s Meeting. With this blogpost series, we share our experiences with producing ethnographic data that is scalable and integrable with other data types. This involves an online platform for writing fieldnotes and a highly structured methodological approach.

What is the coming blogpost series about?

Now that we have given you an introduction of both our own journey into the field of social data science as well as the context of our project, let us give you a brief outline of what you can expect from the upcoming methodological blogpost series. It will consist of five posts in total: In the first post, we elaborate on why we chose to diverge from classical anthropological approaches to data collection and instead gather what we term “broad” ethnographic data. By this, we mean data that fits an interdisciplinary, collaborative setting and fulfils the three Cs: Namely, that broad data can be Compiled in a common format, Compared between researchers, and holds a potential for Computational processing. In the second post, we describe how we compiled (1st C) ethnographic fieldnotes across our group by using the self-developed “Ethno-platform”, an online tool for writing and archiving fieldnotes. In the third post, we present a systematic approach to data collection that involves self-developed observation schemes and seating charts. These allowed us to align and compare (2nd C) ethnographic data across ethnographers. In the fourth post, we move on to describe how we retrieved structured data from the Ethno-platform that could be merged with data from the schemes and charts. This enabled us to computationally process (3rd C) our data to explore patterns, and it also allowed us to combine this broad ethnographic data with other data types. In the fifth and final post, we discuss the trade-offs when collecting broad instead of thick data, and we argue that for interdisciplinary collaborations, broad data can be preferable.

The approach to ethnographic data collection we present in this series might challenge anthropologically trained readers, just as we ourselves were challenged along the way. What we call broad ethnography does not offer thick holistic descriptions of people, places, and situations. Neither does its methodology offer much flexibility or deep immersion into a field. For some readers our contribution might even seem slightly blasphemous. However, before rejecting it as so, bear in mind that what we present is not a suggestion to reform anthropological fieldwork. Rather, collecting broad data is suited for short-term, collaborative ethnographic data collection in interdisciplinary research, and for this purpose, we believe it holds a great deal of potential. We hope that our blogpost series will engage our readers and we are looking forward to discussing the trade-offs of broadening thick ethnographic data as we know it.

Bibliographic references

Barberá, P., Casas, A., Nagler, J., Egan, P., Bonneau, R., Jost, J., & Tucker, J. (2019). Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data. American Political Science Review, 113(4), 883-901.

Breslin, S., A. Blok, T. Enggaard, T. Gårdhus, and Pedersen, M. A. (2022). “Affective Publics”
Performing Trust on Danish Twitter during the COVID-19 Lockdown. Current Anthropology 63(2).

Isfeldt, A. S., Enggaard, T. R., Blok, A., & Pedersen, M. A. (2022). Grøn Genstart: A quali-quantitative micro-history of a political idea in real-time. Big Data & Society, 9(1).

Liebst, L. S. (2019). Exploring the sources of collective effervescence: A multilevel study. Sociological Science, 6, 27-42.

Meinert, L., & Kapferer, B. (Eds.). (2015). In the event: Toward an anthropology of generic moments. Berghahn Books.

Sekara, V., Stopczynski, A., & Lehmann, S. (2016). Fundamental structures of dynamic social networks. Proceedings of the National Academy of Sciences, 113(36), 9977-9982.

Notes

[1] SODAS is the Copenhagen Center for Social Data Science at the University of Copenhagen

[2]DISTRACT is funded by the Advanced Grant project 834540 from the European Research Council. Text as Data is funded by the Data + Program at the University of Copenhagen.