Laura K. Nelson
Assistant Professor of Sociology
I use computational tools, principally automated text analysis, to study social movements, culture, gender, institutions, and organizations. I have a particular interest in applying these tools with a qualitative lens, and to better understand intersectionality and inequality.
I am an open source and open science enthusiast. I seek to use open-source tools and computational methods to make the social sciences and humanities more transparent, reproducible, and scalable.
Best Meta-Reviewer, 12th International Conference on Social Informatics (SocInfo20)
Outstanding Faculty of the Year, 2020 (given by the graduate students at Northeastern University, Department of Sociology and Anthropology)
American Sociological Association
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Education
PhD, Sociology
University of California, Berkeley, 2014 -
Contact
617-373-3922 l.nelson@northeastern.edu Website -
Address
360 Huntington Avenue
Boston, MA 02115
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Bostonography: The City through Data, Texts, Maps, and Networks
INSH 2102
Uses Boston as a case study for integrating computational methods with the social sciences and humanities to provide new insights into major cultural, historical, and societal questions as they relate to and extend beyond the city of Boston. Through lectures, discussions, and labs, the course examines a variety of data sets that measure geographic, historical, literary, political, civic, and institutional landscapes. Offers students an opportunity to combine analytical tools, such as geospatial mapping, data visualization, and network science, with readings, hands-on class activities, and museum or site visits, enabling a comprehensive view of complex cultural and social phenomena.
Analyzing Complex Digitized Data
INSH 6406
Introduces cutting-edge ways of structuring and analyzing complex data or digitized text-as-data using the open-source programming language Python. Scholars across multiple disciplines are finding themselves face-to-face with massive amounts of digitized data. In the humanities and social sciences, these data are often in the form of unstructured text and un- or under-structured data. Encourages students to think about novel ways they can apply these techniques to their own data and research questions and to apply the methods in their own research, whether it be in academia or in industry.
