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Hu, Q., Liu, B., Rosendahl Thomsen, M., Gao, J. & Nielbo, K. L. (2019). Dynamic evolution of sentiments in Never Let Me Go: Insights from quantitative analysis and implications. In BESC 2019 - 6th International Conference on Behavioral, Economic and Socio-Cultural Computing, Proceedings Article 8963117 IEEE. https://doi.org/10.1109/BESC48373.2019.8963117
Mitkidis, P., Lienard, P., Nielbo, K. L. & Sørensen, J. (2014). Does Goal-Demotion Enhance Cooperation? Journal of Cognition and Culture, 14(3-4), 263-272. https://doi.org/10.1163/15685373-12342124
Feldkamp, P. & Bizzoni, Y. (2023). Dimensions of Quality: Contrasting Stylistic vs. Semantic Features for Modelling Literary Quality in 9,000 Novels. In G. Angelova, M. Kunilovskaya & R. Mitkov (Eds.), International Conference Recent Advances in Natural Language Processing, RANLP 2023 (pp. 739-747). Association for Computational Linguistics. https://doi.org/10.26615/978-954-452-092-2_080
Nielbo, K. L. (2015). Digital Text Laboratory|DTL: An emerging text mining infrastructure in the humanities.
Bechmann, A. & Vahlstrup, P. B. (2013). Digital Footprint Analytics. Software
Bernstorff, M., Hansen, L., Enevoldsen, K., Damgaard, J., Hæstrup, F., Perfalk, E., Danielsen, A. A. & Østergaard, S. D. (2025). Development and validation of a machine learning model for prediction of type 2 diabetes in patients with mental illness. Acta Psychiatrica Scandinavica, 151(3), 245-258. https://doi.org/10.1111/acps.13687
Brøgger, M. N., Nielbo, K. L. & Fage-Butler, A. (2020). #Detkuhaværetmig:How Twitter enabled the expression and propagation of solidarity among healthcare professionals. Abstract from Communication, Medicine and Ethics (COMET) - Aalborg. https://www.communication.aau.dk/digitalAssets/840/840452_-brgger_nielbo_fage-butler-detkuhavretmig-how_twitter_enabled_the_expression_and_propagation_of_solidarity_among_healthcare_professionals.pdf
Brøgger, M. N., Nielbo, K. L. & Fage-Butler, A. (2021). #detkuhaværetmig: How Twitter enabled the expression and propagation of solidarity among healthcare professionals. Conjunctions : transdisciplinary journal of cultural participation, 8(1). https://doi.org/10.7146/tjcp.v8i1.123040
Lassen, I. M. S., Almasi, M., Enevoldsen, K. & Kristensen-McLachlan, R. D. (2023). Detecting intersectionality in NER models: A data-driven approach. In EACL 2023 - 7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Proceedings of LaTeCH-CLfL 2023 (pp. 116-127). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.latechclfl-1.13
Enni, S. (2021). Deliberation and Dissemination in Machine Learning. [PhD dissertation, Aarhus University]. Aarhus Universitet.
Kipke, M., Brinkmeyer, L., Bagayoko, S., Schmidt-Thieme, L. & Langner, M. (2022). Deep Level Annotation for Painter Attribution on Greek Vases utilizing Object Detection. In Proceedings of the 4th ACM International workshop on Structuring and Understanding of Multimedia heritAge Contents (pp. 23-31). ACM. https://doi.org/10.1145/3552464.3555684
Kolding, S., Nymann, K., Hansen, I. B., Enevoldsen, K. & Kristensen-McLachlan, R. D. (2023). DanSumT5: Automatic Abstractive Summarization for Danish. In T. Alumäe & M. Fishel (Eds.), Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa) (pp. 248-264). Tartu University Press. https://aclanthology.org/2023.nodalida-1.25/
Enevoldsen, K., Jessen, E. T. & Baglini, R. B. (2024). DANSK and DaCy 2.6. 0: Domain Generalization of Danish Named Entity Recognition. ArXiv. https://arxiv.org/pdf/2402.18209
Enevoldsen, K., Jessen, E. T. & Baglini, R. B. (2024). DANSK: Domain generalization of Danish named entity recognition. Northern European Journal of Language Technology, 10(1), 14-29. https://doi.org/10.3384/nejlt.2000-1533.2024.5249
Enevoldsen, K., Hansen, L., Nielsen, D. S., Egebæk, R., Holm, S., Nielsen, M. C., Bernstorff, M., Larsen, R., Jørgensen, P. B., Højmark-Bertelsen, M., Vahlstrup, P. B., Møldrup-Dalum, P. & Nielbo, K. L. (2023). Danish Foundation Models: Danish Foundation Models. ArXiv. https://arxiv.org/abs/2311.07264
Enevoldsen, K., Hansen, L. & Nielbo, K. L. (2021). DaCy: A Unified Framework for Danish NLP. ArXiv. https://doi.org/10.48550/arXiv.2107.05295
Enevoldsen, K., Hansen, L. & Nielbo, K. L. (2021). DaCy: A unified framework for Danish NLP. CEUR Workshop Proceedings, 2989, 206-216.
Nielbo, K. L., Schjødt, U., Andersen, M. N. & Sørensen, J. (2013). Cultural modulation of probabilistic learning.
Lassche, A., Kipke, M., Eriksen, R. S., Baunvig, K. L. & Nielbo, K. L. (2026). Corsaren: Multimodal Storytelling in Nineteenth Century Danish Satire. Abstract from DH Benelux 2026, Maastricht, Netherlands.
Bizzoni, Y., Thomsen, M. R., Lassen, I. M. S. & Nielbo, K. L. (2022). Correlations between GoodReads Appreciation and the Sentiment Arc Fractality of the Grimm brothers' Fairy Tales. In Proceedings of the Computational Humanities Research Conference 2022 (Vol. 3290, pp. 374-386). CEUR-WS.org. https://ceur-ws.org/Vol-3290/short_paper9235.pdf
Bechmann, A., Nielbo, K. L., Keller, T., Graham, T., Angus, D., Bruns, A., Nijmeijer, R., Neudard, L.-M., Marchal, N., bradshaw, S., Rossini, P., Stromer-Galley, J., Baptista, E. A. & Veiga de Oliveira, V. (2020). 'Coordinated inauthentic behaviour' and other online influence operations in social media spaces. In AoIR Selected Papers of Internet Research (Vol. 2020). AoIR (The International Association of Internet Researchers). http://10.5210/spir.v2020i0.11132
Lyngbaek, L., Feldkamp, P., Bizzoni, Y., Nielbo, K. L. & Enevoldsen, K. (2025). Continuous Sentiment Scores for Literary and Multilingual Contexts. In Anthology of Computers and the Humanities: Computational Humanities Research 2025 (Vol. 3, pp. 480-497). Association for Computers and the Humanities. https://doi.org/10.63744/nVu1Zq5gRkuD
Kristensen-McLachlan, R. D., Hicke, R. M. M., Kardos, M. & Thunø, M. (2024). Context is Key(NMF): Modelling Topical Information Dynamics in Chinese Diaspora Media. In W. Haverals, M. Koolen & L. Thompson (Eds.), Proceedings of the Computational Humanities Research Conference 2024 (Vol. 3834, pp. 829-847). CEUR-WS. https://ceur-ws.org/Vol-3834/paper49.pdf
Hussain, Z. & Nielbo, K. (2026). Conformity Mitigations in Large Language Models Lie on a Single Resistance-Receptivity Frontier.
Nielbo, K. L., Braxton, D. M. & Upal, A. (2012). Computing Religion: A new tool in the multilevel analysis of religion. Method and Theory in the Study of Religion, 24(3), 267-290. https://doi.org/10.1163/157006812X635709
Mortensen, J. P. B. & Bizzoni, Y. (2025). Computing Mark’s Genre. Religions , 16(12), Article 1568. https://doi.org/10.3390/rel16121568
Wackerhausen, A.-C. & Nielbo, K. L. (2022). Computationally assisted conceptual analysis-What is it is, and how to do it. International Journal of Digital Humanities, 3(1-3), 51-89. https://doi.org/10.1007/s42803-021-00041-4
Nielbo, K. L., Tilton, L. & Karsdorp, F. (2024, Nov 4). Computational Humanities Research: A new open access journal at the intersection of computational methods and the humanities.
Vrangbæk, E. E. H. & Nielbo, K. L. (2021). Composition and Change in De Ciuitate Dei: A Case Study of Computationally Assisted Methods. In A. Dupont & G. Partoens (Eds.), Papers presented at the Eighteenth International Conference on Patristic Studies held in Oxford 2019 (Vol. 14: Augustine of Hippo's De ciuitate Dei: Content, Transmission, and Interpretations, pp. 149-164). Peeters. https://doi.org/10.2307/j.ctv27vt566.14
Bizzoni, Y. & Feldkamp, P. (2023). Comparing Transformer and Dictionary-based Sentiment Models for Literary Texts: Hemingway as a Case-study. In M. Hämäläinen, E. Öhman, F. Pirinen, K. Alnajjar, S. Miyagawa, Y. Bizzoni, N. Partanen & J. Rueter (Eds.), Proceedings of the Joint 3rd International Conference on Natural Language Processing for Digital Humanities and 8th International Workshop on Computational Linguistics for Uralic Languages (pp. 219-228). Association for Computational Linguistics. https://aclanthology.org/2023.nlp4dh-1.25
Feldkamp, P., Kostkan, J., Overgaard, E., Jacobsen, M. & Bizzoni, Y. (2024). Comparing Tools for Sentiment Analysis of Danish Literature from Hymns to Fairy Tales: Low-Resource Language and Domain Challenges. In O. De Clercq, V. Barriere, J. Barnes, R. Klinger, J. Sedoc & S. Tafreshi (Eds.), WASSA 2024 - 14th Workshop on Computational Approaches to Subjectivity, Sentiment, and Social Media Analysis, Proceedings of the Workshop (pp. 186-199). Association for Computational Linguistics. https://aclanthology.org/2024.wassa-1.15
Mitkidis, P., Sørensen, J., Nielbo, K. L., Andersen, M. N. & Lienard, P. (2013). Collective-Goal Ascription Increases Cooperation in Humans. PLoS One, 8(5). https://doi.org/10.1371/journal.pone.0064776
Schjødt, U., Sørensen, J., Nielbo, K. L., Xygalatas, D., Mitkidis, P. & Bulbulia, J. (2013). Cognitive resource depletion in religious interactions. Religion, Brain, and Behavior, 3(1), 39-55. https://doi.org/10.1080/2153599X.2012.736714
Baskakovs, A., Estebe, S., Enevoldsen, K., Nielbo, K., Mathys, C. & Legrand, N. (2026). Closed-form predictive coding via hierarchical Gaussian filters.
Kreten, T. & Kipke, M. (2025). CineSearcher - A Multimodal Film Exploration Workspace. In SUMAC 2025 - Proceedings of the 7th International Workshop on analySis, Understanding and proMotion of heritAge Contents, Co-Located with: MM 2025 (pp. 51-60). ACM. https://doi.org/10.1145/3746273.3760207
Lassche, A., Kostkan, J. & Nielbo, K. L. (2022). Chronicling Crises: Event Detection in Early Modern Chronicles from the Low Countries. In Proceedings of the Computational Humanities Research Conference 2022 (CHR 2022) (pp. 215-230). CEUR-WS.org. https://ceur-ws.org/Vol-3290/short_paper4697.pdf
Kristensen-McLachlan, R. D., Canavan, M., Kardos, M., Jacobsen, M. & Aarøe, L. (2023). Chatbots Are Not Reliable Text Annotators. arxiv.org. https://doi.org/10.48550/arXiv.2311.05769
Abell, K. M., Schjødt, U., Nielbo, K. L., Andersen, M. N. & Sørensen, J. (2013). Causal Uncertainty in Ritualized Behavior: Evidence of a Positive Event Segmentation Pattern. Poster session presented at IACSR 2013 Berlin Conference on Sociale Cognition and Religion, Berlin, Germany. http://iacsr.com/iacsr/IACSR_2013_Schedule.pdf
Feldkamp, P., Lassche, A., Kostkan, J., Kardos, M., Enevoldsen, K., Baunvig, K. F. & Nielbo, K. L. (2024). Canonical Status and Literary Influence: A Comparative Study of Danish Novels from the Modern Breakthrough (1870–1900). In M. Hämäläinen, E. Öhman, S. Miyagawa, K. Alnajjar & Y. Bizzoni (Eds.), NLP4DH 2024 - 4th International Conference on Natural Language Processing for Digital Humanities, Proceedings of the Conference (pp. 140-155). Association for Computational Linguistics. https://aclanthology.org/2024.nlp4dh-1.14
Jurik, B. A., Blekinge, A. A., Ferneke-Nielsen, R. B. & Møldrup-Dalum, P. (2015). Bridging the gap between real world repositories and scalable preservation environments. International Journal on Digital Libraries, 16(3-4), 267-282. https://doi.org/10.1007/s00799-015-0152-4
Baunvig, K. F. & Nielbo, K. L. (2023). Benign Structures: The Worldview of Danish National Poet, Pastor, and Politician N.F.S. Grundtvig (1783-1872). In S. Gilbert & A. Rockenberger (Eds.), Book of Abstracts: Sustainability: Environment, Community, Data (pp. 9-11). Oslo Universitet.
Baunvig, K. F. & Nielbo, K. L. (2023). Benign Structures: The Worldview of Danish NationalPoet, Pastor, and Politician N.F.S. Grundtvig (1783-1872). In A. Rockenberger, J. Tiemann & S. Gilbert (Eds.), Conference Proceedings : DHNB2023 (pp. 1-10). Article 1 Universitetsforlaget, Oslo. https://doi.org/10.5617/dhnbpub.10646
Bizzoni, Y. & Feldkamp, P. (2024). Below the Sea (with the Sharks): Probing Textual Features of Implicit Sentiment in a Literary Case-study. In V. Pyatkin, D. Fried, E. Stengel-Eskin, A. Liu & S. Pezzelle (Eds.), The Third Workshop on Understanding Implicit and Underspecified Language: Proceedings of the Workshop (pp. 54-61). Association for Computational Linguistics (ACL). https://aclanthology.org/2024.unimplicit-1.5
Nielbo, K. L., Perner, M. L., Larsen, C., Nielsen, J. & Laursen, D. (2019). Automated compositional change detection in Saxo Grammaticus’ Gesta Danorum. CEUR Workshop Proceedings, 2364, 321-332.
Nielbo, K. L. & Sørensen, J. (2016). Attentional Resource Allocation and Cultural Modulation in a Computational Model of Ritualized Behavior. Religion, Brain, and Behavior, 6(4), 318-335 . https://doi.org/10.1080/2153599X.2015.1087420
Elbæk, C. T., Aarøe, L., Otterbring, T., Hochman, G., Baskakovs, A. & Mitkidis, P. (2023). A scarcity mindset does not influence economic cheating across or between social classes. Abstract from IAREP-SABE International Conference, Nice, France.
Bechmann, A. & Nielbo, K. L. (2018). Are We Exposed to the Same “News” in the News Feed? An empirical analysis of filter bubbles as information similarity for Danish Facebook users. Digital Journalism, 6(8), 990–1002. https://doi.org/10.1080/21670811.2018.1510741
Bechmann, A. & Nielbo, K. L. (2017). Are We Consuming the Same News? An Empirical Analysis of Information Similarity for Danish Facebook Users. Paper presented at Future of Journalism 2017, Cardiff, United Kingdom.
Kristensen-McLachlan, R. D., Canavan, M., Kardos, M., Jacobsen, M. & Aarøe, L. (2025). Are Chatbots Reliable Text Annotators? Sometimes. PNAS Nexus, 4(4), Article pgaf069. https://doi.org/10.1093/pnasnexus/pgaf069
Enevoldsen, K. & Hansen, L. (2017). Analysing political biases in danish newspapers using sentiment analysis. Journal of Language Works - Sprogvidenskabeligt Studentertidsskrift, 2(2). https://tidsskrift.dk/lwo/article/view/96014

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