The academic journal Computational Communication Research has published a paper titled "Topic Model Validation Methods and their Impact on Model Selection and Evaluation", written by Jana Bernhard, Martin Teuffenbach, and Hajo G. Boomgaarden. The paper investigates the critical role of validation methods in topic model selection within computational communication science research, highlighting how different approaches can considerably influence the chosen model, ultimately impacting study results, and thus theory development, or practical implications.

Topic Modeling is currently one of the most widely employed unsupervised text-as-data techniques in the field of communication science. While researchers increasingly recognize the importance of validating topic models and given the prevalence of discussions of inadequate validation practices in the literature, there is limited understanding of the consequences of employing different validation strategies when evaluating topic models. This study applies two different methods for topic modeling to the same text corpus. It uses four validation strategies to assess how the choice of validation method affects the final model selection and evaluation. The findings indicate that different approaches and methods lead to different model choices and evaluations, which is problematic. This might lead to unwanted results in case the choice of model has a decisive impact on findings and, consequently, on theory development and practical implications.

The study is part of the collaborative project "Digitize! ".

Find the full open-access paper here: https://doi.org/10.5117/CCR2023.1.13.BERN

Cite article:

Bernhard, J., Teuffenbach, M., & Boomgaarden, H. G. (2023). Topic Model Validation Methods and their Impact on Model Selection and Evaluation. Computational Communication Research, 5(1), 1–26. https://doi.org/10.5117/CCR2023.1.13.BER