Måns Magnusson
Senior Lecturer/Associate Professor at Department of Statistics
- E-mail:
- mans.magnusson@statistik.uu.se
- Visiting address:
- Ekonomikum (plan 3)
Kyrkogårdsgatan 10 - Postal address:
- Box 513
751 20 UPPSALA
Download contact information for Måns Magnusson at Department of Statistics
- ORCID:
- 0000-0002-0296-2719
Short presentation
I am currently an associate professor in Statistics at the Department of Statistics, Uppsala University. More information can be found on my home page at mansmagnusson.com.
Keywords
- digital humanities
- computational social science
- textual analysis
- probabilistic machine learning
- bayesian statistics
- model comparison and evaluation
- text-as-data
- survey sampling

Publications
Recent publications
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Part of Sociological Methods & Research, p. 120-156, 2026
- DOI for Seeded Topic Models in Digital Archives: Analyzing Interpretations of Immigration in Swedish Newspapers, 1945–2019
- Download full text (pdf) of Seeded Topic Models in Digital Archives: Analyzing Interpretations of Immigration in Swedish Newspapers, 1945–2019
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Detecting Legal Citations in United Kingdom Court Judgments
Part of Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, p. 26810-26836, 2025
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Uncertainty in Bayesian leave-one-out cross-validation based model comparison
Part of Bayesian Analysis, p. 1-31, 2025
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posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Part of International Conference on Artificial Intelligence and Statistics, 2025
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Part of FAccT '25, p. 195-208, 2025
- DOI for Classifying Hate: Legal and Ethical Evaluations of ML-Assisted Hate Crime Classification and Estimation in Sweden
- Download full text (pdf) of Classifying Hate: Legal and Ethical Evaluations of ML-Assisted Hate Crime Classification and Estimation in Sweden
All publications
Articles in journal
-
Part of Sociological Methods & Research, p. 120-156, 2026
- DOI for Seeded Topic Models in Digital Archives: Analyzing Interpretations of Immigration in Swedish Newspapers, 1945–2019
- Download full text (pdf) of Seeded Topic Models in Digital Archives: Analyzing Interpretations of Immigration in Swedish Newspapers, 1945–2019
-
Uncertainty in Bayesian leave-one-out cross-validation based model comparison
Part of Bayesian Analysis, p. 1-31, 2025
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Part of The R Journal, p. 4-14, 2024
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Part of Communications in Statistics - Theory and Methods, p. 5877-5899, 2023
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From Documents to Data: A Framework for Total Corpus Quality
Part of SOCIUS, 2022
- DOI for From Documents to Data: A Framework for Total Corpus Quality
- Download full text (pdf) of From Documents to Data: A Framework for Total Corpus Quality
Conference papers
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Detecting Legal Citations in United Kingdom Court Judgments
Part of Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, p. 26810-26836, 2025
-
posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms
Part of International Conference on Artificial Intelligence and Statistics, 2025
-
Part of FAccT '25, p. 195-208, 2025
- DOI for Classifying Hate: Legal and Ethical Evaluations of ML-Assisted Hate Crime Classification and Estimation in Sweden
- Download full text (pdf) of Classifying Hate: Legal and Ethical Evaluations of ML-Assisted Hate Crime Classification and Estimation in Sweden
-
Formalising Anti-Discrimination Law in Automated Decision Systems
Part of FAccT '25, p. 181-194, 2025
- DOI for Formalising Anti-Discrimination Law in Automated Decision Systems
- Download full text (pdf) of Formalising Anti-Discrimination Law in Automated Decision Systems
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Bias in Legal Data for Generative AI
Part of Generative AI and Law (GenLaw’24’), 2024
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The Swedish parliament corpus 1867–2022
Part of Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), p. 16100-16112, 2024
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The Cambridge Law Corpus: A Dataset for Legal AI Research
Part of Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023
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Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
Part of Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), p. 2925-2934, 2020
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Robust, Accurate Stochastic Optimization for Variational Inference
Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020