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.

Approaches in literary quality tend to belong to two main grounds: one sees quality as completely subjective, relying on the idiosyncratic nature of individual perspectives on the apperception of beauty; the other is ground-truth inspired, and attempts to find one or two values that predict something like an objective quality: the number of copies sold, for example, or the winning of a prestigious prize. While the first school usually does not try to predict quality at all, the second relies on a single majority vote in one form or another. In this article we discuss the advantages and limitations of these schools of thought and describe a different approach to reader’s quality judgments, which moves away from raw majority vote, but does try to create intermediate classes or groups of annotators. Drawing on previous works we describe the benefits and drawbacks of building similar annotation classes. Finally we share early results from a large corpus of literary reviews for an insight into which classes of readers might make most sense when dealing with the appreciation of literary quality.

e explore the correlation between the sentiment arcs of H. C. Andersen’s fairy tales and their popularity, measured as their average score on the platform GoodReads. Specifically, we do not conceive a story’s overall sentimental trend as predictive per se, but we focus on its coherence and predictability over time as represented by the arc’s Hurst exponent. We find that degrading Hurst values tend to imply degrading quality scores, while a Hurst exponent between .55 and .65 might indicate a “sweet spot” for literary appreciation.