Unpacking Translation Effects. Influences of Target Language Choice on Topic Modeling in Multilingual Environments

Inhaltsverzeichnis

Bibliographische Infos

Cover der Ausgabe: M&K Medien & Kommunikationswissenschaft Jahrgang 74 (2026), Heft 1
Open Access Vollzugriff

M&K Medien & Kommunikationswissenschaft

Jahrgang 74 (2026), Heft 1

Autor:innen:
Verlag
Nomos, Baden-Baden
Copyrightjahr
2026
ISSN-Online
2942-3317
ISSN-Print
1615-634X

Kapitelinformationen

Open Access Vollzugriff

Jahrgang 74 (2026), Heft 1

Unpacking Translation Effects. Influences of Target Language Choice on Topic Modeling in Multilingual Environments

Autor:innen:
ISSN-Print
1615-634X
ISSN-Online
2942-3317
Kapitelvorschau:

Literaturverzeichnis

  1. Baden, C., Pipal, C., Schoonvelde, M., & Van Der Velden, M. A. C. G. (2022). Three Gaps in Computational Text Analysis Methods for Social Sciences: A Research Agenda. Communication Methods and Measures, 16(1), 1–18. https://doi.org/10.1080/19312458.2021.2015574 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  2. Benoit, K., & Matsuo, A. (2017). spacyr: Wrapper to the “spacy” “NLP” library (1.2.1) [Software]. https://CRAN.R-project.org/package=spacyr [28.01.2026]. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  3. Bentz, C., & Kiela, D. (2014). Measuring and Modelling Lexical Diversity across Languages [Conference presentation]. 5th UK Cognitive Linguistics Conference, Lancaster, UK. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  4. Bernhard-Harrer, J., Ashour, R., Eberl, J. M., Tolochko, P., & Boomgaarden, H. (2025). Beyond Standardization: A Comprehensive Review of Topic Modeling Validation Methods for Computational Social Science Research. Political Science Research and Methods, 1–19. https://doi.org/10.1017/psrm.2025.10008 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  5. Blei, D. M. (2012). Probabilistic Topic Models. Communications of the ACM, 55(4), 77–84. https://doi.org/10.1145/2133806.2133826 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  6. Chan, C.-H., Zeng, J., Wessler, H., Jungblut, M., Welbers, K., Bajjalieh, J. W., van Atteveldt, W., & Althaus, S. L. (2020). Reproducible Extraction of Cross-lingual Topics (rectr). Communication Methods and Measures, 14(4), 285–305. https://doi.org/10.1080/19312458.2020.1812555 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  7. de Vries, E., Schoonvelde, M., & Schumacher, G. (2018). No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications. Political Analysis, 26(4), 417–430. https://doi.org/10.1017/pan.2018.26 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  8. Eisele, A., & Chen, Y. (2010). MultiUN: A Multilingual Corpus from United Nation Documents. Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC), 2868–2872. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  9. Fortson, B. W. IV. (2011). Indo-European Language and Culture: An Introduction (2nd ed.). John Wiley & Sons. https://www.wiley.com/en-us/Indo-European+Language+and+Culture%3A+An+Introduction%2C+2nd+Edition-p-9781405188968 [28.01.2026]. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  10. Grootendorst, M. (2022). BERTopic: Neural Topic Modeling with a Class-Based TF-IDF Procedure. arXiv. https://arxiv.org/pdf/2203.05794 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  11. Günther, E. (2022). Topic Modeling: Algorithmische Themenkonzepte in Gegenstand und Methodik der Kommunikationswissenschaft. [Topic Modeling: Algorithmic Topic Concepts in the Subject and Methodology of Communication Science.] Herbert von Halem Verlag. https://www.halem-verlag.de/produkt/topic-modeling/ Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  12. Hallin, D. C., & Mancini, P. (Eds.). (2011). Comparing Media Systems Beyond the Western World (1st ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139005098 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  13. Haim, M. (2023). Computational Communication Science: Eine Einführung. [Computational Communication Science: Introduction.] Springer VS. https://link.springer.com/book/10.1007/978-3-658-40171-9 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  14. Hanitzsch, T., Hanusch, F., Ramaprasad, J., & De Beer, A. S. (Eds.). (2019). Worlds of Journalism: Journalistic Cultures Around the Globe. Columbia University Press. https://doi.org/10.7312/hani18642 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  15. Hase, V., Mahl, D., Schäfer, M. S., & Keller, T. R. (2021). Climate Change in News Media across the Globe: An Automated Analysis of Issue Attention and Themes in Climate Change Coverage in 10 Countries (2006–2018). Global Environmental Change, 70, 102353. https://doi.org/10.1016/j.gloenvcha.2021.102353 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  16. Jacobi, C., van Atteveldt, W., & Welbers, K. (2016). Quantitative Analysis of Large Amounts of Journalistic Texts Using Topic Modelling. Digital Journalism, 4(1), 89–106. https://doi.org/10.1080/21670811.2015.1093271 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  17. Kettunen, K. (2014). Can Type-Token Ratio be Used to Show Morphological Complexity of Languages? Journal of Quantitative Linguistics, 21(3), 223–245. https://doi.org/10.1080/09296174.2014.911506 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  18. Koltsova, O., & Pashakhin, S. (2020). Agenda Divergence in a Developing conflict: Quantitative Evidence from Ukrainian and Russian TV Newsfeeds. Media, War & Conflict, 13(3), 237–257. https://doi.org/10.1177/1750635219829876 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  19. Kotait, R. (2024). Richness Lost in Machine Translationese. The Egyptian Journal of Language Engineering, 11(1), 66–85. https://doi.org/10.21608/EJLE.2024.267336.1064 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  20. Licht, H. (2023). Cross-Lingual Classification of Political Texts Using Multilingual Sentence Embeddings. Political Analysis, 31(3), 366–379. https://doi.org/10.1017/pan.2022.29 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  21. Licht, H. & Lind, F. (2023). Going Cross-Lingual: A Guide to Multilingual Text Analysis. Computational Communication Research, 5(2), 1–31. https://doi.org/10.5117/CCR2023.2.2.LICH Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  22. Licht, H., Sczepanski, R., Laurer, M., & Bekmuratovna, A. (2024). No More Cost in Translation: Validating Open-Source Machine Translation for Quantitative Text Analysis. OSF. https://doi.org/10.31219/osf.io/9trjs Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  23. Lind, F., Eberl, J., Eisele, O., Heidenreich, T., Galyga, S., & Boomgaarden, H. G. (2022). Building the Bridge: Topic Modeling for Comparative Research. Communication Methods and Measures, 16(2), 96–114. https://doi.org/10.1080/19312458.2021.1965973 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  24. Lucas, C. G., Nielsen, R. A., Roberts, M. E., Stewart, B., Alex, S., & Tingley, D. (2015). Computer-Assisted Text Analysis for Comparative Politics. Political Analysis, 23(2), 254–277. https://doi.org/10.1093/pan/mpu019 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  25. Maier, D., Baden, C., Stoltenberg, D., de Vries-Kedem, M., & Waldherr, A. (2022). Machine Translation vs. Multilingual Dictionaries Assessing Two Strategies for the Topic Modeling of Multilingual Text Collections. Communication Methods and Measures, 16(1), 19–38. https://doi.org/10.1080/19312458.2021.1955845 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  26. Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures, 12(2–3), 93–118. https://doi.org/10.1080/19312458.2018.1430754 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  27. Reber, U. (2019). Overcoming Language Barriers: Assessing the Potential of Machine Translation and Topic Modeling for the Comparative Analysis of Multilingual Text Corpora. Communication Methods and Measures, 13(2), 102–125. https://doi.org/10.1080/19312458.2018.1555798 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  28. Rinke, E. M., Dobbrick, T., Löb, C., Zirn, C., & Wessler, H. (2022). Expert-Informed Topic Models for Document Set Discovery. Communication Methods and Measures, 16(1), 39–58. https://doi.org/10.1080/19312458.2021.1920008 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  29. Roberts, M. E., Stewart, B., & Tingley, D. (2019). stm: An R Package for Structural Topic Models. Journal of Statistical Software, 91(2), 1–40. https://doi.org/10.18637/jss.v091.i02 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  30. Tiedemann, J. (2012). Parallel Data, Tools and Interfaces in OPUS. Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012), 2214–2218. http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf [28.01.2026]. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  31. Torres, M. (2024). A Framework for the Unsupervised and Semi-Supervised Analysis of Visual Frames. Political Analysis, 32(2), 199–220. https://doi.org/10.1017/pan.2023.32 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  32. van Atteveldt, W., Trilling, D., & Arcila, C. (2022). Computational Analysis of Communication. Wiley Blackwell. https://cssbook.net/ [28.01.2026]. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  33. Vanmassenhove, E., Shterionov, D., & Way, A. (2019). Lost in Translation: Loss and Decay of Linguistic Richness in Machine Translation. Proceedings of Machine Translation Summit XVII: Research Track, 222–232. https://aclanthology.org/W19-6622/ [28.01.2026]. Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  34. Wang, H., Wu, H., He, Z., Huang, L., & Church, K. W. (2022). Progress in Machine Translation. Engineering, 18, 143–153. https://doi.org/10.1016/j.eng.2021.03.023 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  35. Windsor, L. C., Cupit, J. G., & Windsor, A. J. (2019). Automated Content Analysis across Six Languages. PLOS One, 14(11), e0224425. https://doi.org/10.1371/journal.pone.0224425 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71
  36. Yoo, M. H., Kim, J., & Song, S. (2025). Multilingual Capabilities of GPT: A Study of Structural Ambiguity. PloS one, 20(7), e0326943. https://doi.org/10.1371/journal.pone.0326943 Google Scholar öffnen DOI: 10.5771/1615-634X-2026-1-71

Zitation

Download RIS Download BibTex

Downloads

Download Kapitel (PDF) Download kompl. Werk (PDF)

Hinweis

Dieses Werk ist urheberrechtlich geschützt und darf nur nach den Vorgaben der Nutzungs- und Lizenzvereinbarung genutzt werden. Jede darüber hinaus gehende Nutzung des Werkes ist ausdrücklich untersagt und wird verfolgt.

Per E-Mail teilen

Medien