%0 Conference Proceedings %T DanTok: Domain Beats Language for Danish Social Media POS Tagging %A Kirstein Hansen, Kia %A Barrett, Maria %A Müller-Eberstein, Max %A Damgaard, Cathrine %A Eriksen, Trine %A van der Goot, Rob %Y Alumäe, Tanel %Y Fishel, Mark %S Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa) %D 2023 %8 May %I University of Tartu Library %C Tórshavn, Faroe Islands %F kirstein-hansen-etal-2023-dantok %X Language from social media remains challenging to process automatically, especially for non-English languages. In this work, we introduce the first NLP dataset for TikTok comments and the first Danish social media dataset with part-of-speech annotation. We further supply annotations for normalization, code-switching, and annotator uncertainty. As transferring models to such a highly specialized domain is non-trivial, we conduct an extensive study into which source data and modeling decisions most impact the performance. Surprisingly, transferring from in-domain data, even from a different language, outperforms in-language, out-of-domain training. These benefits nonetheless rely on the underlying language models having been at least partially pre-trained on data from the target language. Using our additional annotation layers, we further analyze how normalization, code-switching, and human uncertainty affect the tagging accuracy. %U https://aclanthology.org/2023.nodalida-1.27 %P 271-279