Perspectives on Forests and Forestry in Finnish Online Discussions - A Topic Modeling Approach to Suomi24 (2025)

Abstract: This paper explores how forests and forest industry are perceived on the largest online discussion forum in Finland, Suomi24 (‘Finland24’). Using 30,636 posts published in 2014–2020, we investigate what kind of topics and perspectives towards forest management can be found. We use BERTopic as our topic modeling approach and evaluate the results of its different modular combinations. As the dataset is not labeled, we demonstrate the validity of our best model through illustrating some of the topics about forest use. The results show that a combination of UMAP and K-means leads to the best topic quality. Our exploratory qualitative analysis indicates that the posts reflect polarized discourses between the forest industry and forest conservation adherents.

## introduction the importance of forests as carbon sinks has been globally recognized as part of climate change mitigation @xcite . in finland, where forests have a significant socio-economic role, the issue has received increased attention and created tensions across different economic and political views @xcite @xcite . in fact, around 75% of finnish land area is covered by forests of which only 12.9% is partially or totally conserved from industrial forest management (ministry of @xcite . perspectives of the forest industry have also been prominent in the media. analyses of finnish newspapers show that despite emerging multiobjective discourses, the positive framing of the forest industry still seems to dominate (näyhä and wallius; @xcite . however, computational approaches to forest discourses have not, to our knowledge, been applied. while analyzing the representation of forests in the mainstream media is valuable, the voices of common citizens cannot be overlooked. in fact, around 60% of finnish forests are owned by private individuals @xcite . the right of public access and the high percentage of private forest ownership make public opinion critical to understanding how forest-related issues are perceived and debated. to set light on the perspective of forest owners and users and understand their attitudes towards forest management, we used data from suomi24 (translated as 'finland24'). suomi24 is the oldest and largest online forum in finland and has been called a pool of finnish public opinions @xcite . we applied topic modeling to cluster documents and to identify forest-related themes in our dataset. recent advances in machine learning and large language models have led to the development of new topic modeling tools @xcite . in particular, bidirectional encoder representations from transformers (bert) have been found to be powerful in many nlp tasks @xcite . bertopic presented by @xcite has proved to perform well in many topic modeling tasks and was also adopted in our work. the modular approach of bertopic allowed us to build several different models. the different combinations were compared through computational and qualitative measures. in this paper, our first aim is to evaluate the performance of different bertopic models and demonstrate how topic modeling can be used to identify relevant topics about the use of forests in finland. second, we aim at characterizing how forest management and industry are discussed in our suomi24 dataset. the model evaluation results showed that there was great variance in the model quality. however, a comparison of topic keywords showed that all of them captured similar topics that can give valuable insights into finnish forest discourse. the qualitative exploration suggested that pro-forestry discourses dominate over pro-nature discourses, but the distinction between these two is not always clear. finally, we briefly discuss how this analysis can be extended in the future. ## data and methods our methodology combined quantitative exploration and closer qualitative analysis of selected topics. the design allowed to compare the performance of different topic models on unlabeled data. the steps of the workflow are described in this section. ## results and analysis the combinations of different algorithms and the evaluation results are shown in moreover, the hdbscan algorithm labeled most of the documents as 'noise', while a closer look at the discarded documents showed that many of them were relevant to forest discussion, and the 'noise' category keywords contained several forest-related terms. due to this, the hdbscan models were not included in further evaluation of topic quality and qualitative analysis. although the performance of the models varied, we observed that all of them produced topics with similar keywords. this reinforced our confidence in the reliability of the generated topics. the finnish sentence embedding model performed slightly better than the multilingual one, but the choice of the dimensionality reduction and clustering algorithms had a greater effect on the result. overall, umap was the most suitable dimensionality reduction algorithm for our dataset and k-means functioned well for topic clustering. as ## discussion and future work in this paper, we presented a framework that combined topic modeling and qualitative exploration to investigate how forest-related issues are addressed in a finnish online forum, suomi24. we compared different bertopic models, and the evaluation results showed that its default clustering algorithm, hdbscan, did not function well with our data. based on our observations, numerous relevant posts were discarded by these models. best results were obtained by combining finnish sentence bert, umap, and k-means. as @xcite point out, the parameters of a neural topic model are often difficult to interpret and hence it is hard to diagnose why the hdbscan model did not work. k-means was found to produce topics of better quality, but as the method forces all texts into some clusters, we needed to filter the resulting topics to discard irrelevant documents. even the best models contained several topics of low quality, which is due to several reasons. first, we collected the dataset from the suomi24 corpus using a list of search words, which means that it potentially contained several texts not related to the themes of interest. misspellings and colloquial language in the posts introduced noise in the data, leading to suboptimal sentence embeddings and reduced model accuracy. while the finnish sentence transformer outperformed the multilingual one, finnish is still a lower-resource language, which may show in the performance of the models. the best model could be improved by changing the number of topics. in addition, we did not test different hyperparameters for the used algorithms, so our final model could be improved through finetuning the umap and k-means modules. we noted a striking difference in the computational and human annotated results of quality evaluation. moreover, the coherence measure c v is usually used with lda models, and it measures coherence based on the co-occurrence of the given topic keywords in a corpus. since bertopic generates topics through embeddings, not words, this approach does not fully capture the semantic coherence of the generated topics. these observations remind us that quality in topic modeling is dependent on several aspects @xcite and computational performance measures can be misleading. thus, human evaluation is crucial when the resulting topics are used for qualitative analysis. overall, the evaluation of neural topic models calls for new measures. many topics shared a common broader theme, and this overlap suggests that the number of topics could be further reduced. however, close reading the posts showed that different topics offered diverse viewpoints and reflected distinct discourses on the same theme. for instance, the theme related to nature conservation and environmentalists could have been further divided into political, activist, and other perspectives on the theme. although the scope of this paper did not allow us to delve deeper into these differences, it was an interesting observation for future studies. the exploratory qualitative analysis showed that opinions on forestry and forest management tend to be polarized. in the future, we aim to expand the analysis of such polarization by studying texts in selected topics (e.g., clearcuttings) by applying methods of 'making strange', close-reading @xcite and analyses of topic chains following @xcite and @xcite .