Finished projects
Cultural evolution of beauty standards
This project examines how beauty ideals in media and fashion have changed over the past 25 years using large-scale data from advertisements, magazine covers, runway shows, and editorials. It shows that representation has become more diverse, while the dominant thin ideal has remained largely unchanged, revealing how inclusion can expand without fundamentally transforming the standards that define beauty.
The paper is published in PNAS
Dialectograms: Machine Learning Differences between Discursive Communities
This project examines how discursive communities use the same words in different ways using word embeddings and a new visualization method called dialectograms. It shows how differences in meaning can be mapped across political communities, revealing patterns of affective polarization, disagreement about political action, and competing understandings of which issues require political intervention.
This project is published open access in the journal Nature: Scientific Reports
Fixing Fieldnotes: Developing and Testing a Digital Tool for the Collection, Processing, and Analysis of Ethnographic Data
This project examines how ethnographic fieldnotes can be made more useful for collaborative and mixed-methods research through a digital tool for systematic collection, processing, and analysis. It shows how fieldnotes can become easier to organize, integrate with other data, and analyze computationally, while preserving the flexibility and exploratory strengths of ethnographic fieldwork.
Paper published Social Science Computer Review and can be found her: Link to paper
In November 2024 I defended my PhD thesis in Social Data Science at the Copenhagen Center for Social Data Science. The thesis was entilted Political Participation, Responsiveness and Discourse in the Social Media Age.
The thesis contains 4 papers, some of them projects that are also descriped on this page. All are concerned with political behavior and how to measure it using computational methods, as well as experimentations with interdiciplinary computational analysis of social science questions.
You can download and read it here – note that the results are non-peer-reviewed: Ph_D__Dissertation
Developing tools for Computational ethnography.
As part of my Ph.D, I work on integrating ethnographic fieldwork with computational and qual/quant methods. As a part of this work, I was involved in developing an app, for ethnographers to store, access, and manage fieldnotes in a more structured way. This allows for after-the-fact NLP analysis of the notes, but also for ethnographers to sort and access fieldnotes based on time, place, keyword, etc.
My colleagues have now sucessfully made a startup based on our work – Check it out here: https://ethnote.org/
This work was generously supported by the Carlsberg Fondation’s reserach infrastructure grant. Read more here: Link to Carlberg Foundation
Ongoing work
GenAI influence on Academic Writing
This project examines how the availability of generative AI has changed student writing, using university exam data from before and after the release of ChatGPT. It shows that AI use in take-home exams makes writing more linguistically sophisticated but also more similar across students, revealing how GenAI can improve textual performance while homogenizing individual expression.
Learning the Language of Academia: Ethnic Differences in the Acquisition of Academic Writing Skills
This project examines whether students from ethnic Danish and non-Danish backgrounds differ in their development of academic writing, using NLP methods on more than 250,000 university exam papers. It shows that writing complexity and idea density are strongly related to grades, but ethnic differences in these textual features are small, suggesting that grade disparities may arise more from how written work is evaluated than from differences in academic language itself.
Mobilizing the Margins – Demographic and Political
Characheristics of Those Who Mobilize Their Opinion on
Social Media
This project examines who participates in politics by liking and commenting on politicians’ posts on social media, using large-scale data from Danish Facebook. It shows that minorities often use these channels to make their opinions visible, while ideologically extreme users are also highly active, raising questions about whose voices politicians encounter online.
Responsiveness of politicians to social media feedback
A main project of my Ph.D. where I examine to what extend social media feedback such as “likes”, can be said to drive the political agenda. The project is built on 8 years of Facebook data labeled using deep learning.
Ideological scaling of the Danish Parliament using word embeddings
