Elizaveta Kopacheva

Elizaveta Kopacheva

Postdoctoral Fellow
Department of Computer Science and Media Technology Faculty of Technology
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I am a proud graduate of Saint Petersburg State University (with a specialization in Informatics and International Relations), Södertörn University (with a specialization in Political Science); and Linnaeus University (with a specialization in Computational Social Sciences).


I am currently lecturing on the course Quantitative Methods in Social Science (4XA110). Previously, I was one of the teaching staff on the following courses: International Law and Sustainable Development, Social Science Methods II, International Law and Human Rights, Human Rights in Europe, and Democratization in The World.

I have been through formal pedagogical training (Higher Education Teacher Training) comprising three completed courses: Didactical development (4PE33U), Juridical, norm critical and ethical aspects of teaching (2PE30U), and Teaching and Learning Processes (4PE32U).


My scientific interests are vast and multidisciplinary. Throughout my PhD, I focused on studying political participation by analyzing Big Data and applying computational methods and techniques, including Bayesian network analysis, regression analysis, Bayesian structural equation modeling, social network analysis, unsupervised and supervised machine learning, text mining, etc. I am particularly fascinated by the explanatory and predictive modeling of political participation in unconventional activities such as protests. In my PhD project, I focused on political activism in Russia and consider it to be my domain. However, I have also been involved in exploratory projects that looked into discussions on social media platforms. Exploratory and descriptive research normally comprise the workload around a research project. This part of work also includes visualization of Big Data, which is a fascinating part of the work in itself. Communicating the results of a research project to the public without domain expertise is a challenging yet exciting endeavour. So, I continually try to improve in this area, not least by taking courses in information visualization and visual analytics.

The overwhelming part of my work is the analysis of unstructured data. Specifically, I have always been fascinated by text mining and the possibilities of computational methods and techniques to extract needed information from free text. In this regard, today, I more often work with natural language processing, information extraction, and large language models and their application in e-Health.


Selected publications

Article in journal (Refereed)

Doctoral thesis, comprehensive summary (Other academic)