GCDH Evening Lecture: Sophie Mützel (University of Lucerne): Big data – methodological and analytical challenges for sociology
SUB Central Building, Platz der Göttinger Sieben 1, Großer Seminarraum 1. Etage. Please note: The elevator doesn't stop on the first floor. Please use the stairs or take the elevator to the third floor and walk down. Participants who use walking aids or wheelchairs etc. should talk to staff at the front desk on the ground floor for further assistance.
The rise of big data represents a watershed moment for the social sciences. Not only are we faced with large and multifarious types of data (e.g. texts, geo location, time stamps, entire full-text archives, pictures), often very unstructured, and stemming from all sorts of sources and phenomena, we are also challenged in our theoretical underpinnings of what constitutes the social and how we can analyze it. We are also witnessing the rise of methods that help to identify patterns and relations, and to reduce complexity. Tools and algorithms of computational linguistics, machine learning, and network analysis are challenging the traditional tool kits of social science methods that work with representative samples, independent observations, statistical significance or analysts’ privileged positions in local settings.
My talk highlights the analytical and methodological challenges big data poses to the social sciences, and in particular to sociology. I discuss challenges of data construction, models of data analysis, and data interpretation. Moreover, I argue for a sociological engagement with big data analytics. Social networking companies have fully analyzed at least our social behavior online and data journalism provides colorful, interactive insights, for example, into social inequalities based on large public data sets. Sociologists, I maintain, should engage rather than refrain from such analyses: this might entail to fight back data scientists’ interpretation of the social, to challenge what is done with our data, or to adopt more data visualization in social science publications to name just a few. Whichever focus it may be, such an engagement certainly requires a sociological involvement with the tools and algorithms that collect, clean, sort, split, classify, and visualize. Thus, I argue for sociological analyses of algorithms as well as sociological analyses with algorithmic tools. In sum, my aim of the talk is twofold: to show the relevance of sociological insights for big data analyses while stressing the need to expand our theoretical horizons and tools kits in response to some of the challenges of big data.
Sophie Mützel, short bio
Sophie Mützel is Professor of Sociology at the Department of Sociology, University of Lucerne, Switzerland. She teaches on the sociology of algorithms, big data and social media, as well as on metrics in journalism and the digital economy within the study program on “media and networks”. Her research interests lie in the areas of big data and its analytics, in particular text analytics and network analysis, as well as economic and cultural sociology. She recently finished a book manuscript on “Markets from stories”. She is also the PI of the Swiss federal government funded NRP75 project “Facing big data: methods and skills for a 21st century sociology”. Sophie studied Political Science at UC Berkeley, Sociology at Cornell University, and finished her PhD in Sociology at Columbia University. After completing her PhD, she held a Jean Monnet Fellowship at the European University Institute, Italy; afterwards she taught and conducted research at Humboldt-University Berlin and at the WZB Berlin Social Science Center. She has been a research fellow at Harvard University and held a visiting professorship at the University of Vienna.
Find more information about Sophie Mützel on the Homepage of the University of Lucerne.