Location mentions in local news are crucial for examining issues like spatial inequalities, news deserts and the impact of media ownership on news diversity. However, while geoparsing – extracting and resolving location mentions – has advanced through statistical and deep learning methods, its use in local media studies remains limited and fragmented due to technical challenges and a lack of practical frameworks. To address these challenges, we identify key considerations for successful geoparsing and review spatially oriented local media studies, finding over-reliance on limited geospatial vocabularies, limited toponym disambiguation and inadequate validation of methods. These findings underscore the need for adaptable and robust solutions, and recent advancements in fine-tuned large language models (LLMs) for geoparsing offer a promising direction by simplifying technical implementation and excelling at understanding contextual nuances. However, their application to U.K. local media – marked by fine-grained geographies and colloquial place names – remains underexplored due to the absence of benchmark datasets. This gap hinders researchers’ ability to evaluate and refine geoparsing methods for this domain. To address this, we introduce the Local Media UK Geoparsing (LMUK-Geo) dataset, a hand-annotated corpus of U.K. local news articles designed to support the development and evaluation of geoparsing pipelines. We also propose an LLM-driven approach for toponym disambiguation that replaces fine-tuning with accessible prompt engineering. Using LMUK-Geo, we benchmark our approach against a fine-tuned method. Both perform well on the novel dataset: the fine-tuned model excels in minimising coordinate-error distances, while the prompt-based method offers a scalable alternative for district-level classification, particularly when relying on predictions agreed upon by multiple models. Our contributions establish a foundation for geoparsing local media, advancing methodological frameworks and practical tools to enable systematic and comparative research.

In this study, we perform a comprehensive evaluation of sentiment classification for German language data using three different approaches: (1) dictionary-based methods, (2) fine-tuned transformer models such as BERT and XLM-T and (3) various large language models (LLMs) with zero-shot capabilities, including natural language inference models, Siamese models and dialog-based models. The evaluation considers a variety of German language datasets, including contemporary social media texts, product reviews and humanities datasets. Our results confirm that dictionary-based methods, while computationally efficient and interpretable, fall short in classification accuracy. Fine-tuned models offer strong performance, but require significant training data and computational resources. LLMs with zero-shot capabilities, particularly dialog-based models, demonstrate competitive performance, often rivaling fine-tuned models, while eliminating the need for task-specific training. However, challenges remain regarding non-determinism, prompt sensitivity and the high resource requirements of large LLMs. The results suggest that for sentiment analysis in the computational humanities, where non-English and historical language data are common, LLM-based zero-shot classification is a viable alternative to fine-tuned models and dictionaries. Nevertheless, model selection remains highly context-dependent, requiring careful consideration of trade-offs between accuracy, resource efficiency and transparency.

Language models have the ability to identify the characteristics of much shorter literary passages than was thought feasible with traditional stylometry. We evaluate authorship and genre detection for a new corpus of literary novels. We find that a range of LLMs are able to distinguish authorship and genre, but that different models do so in different ways. Some models rely more on memorization, while others make greater use of author or genre characteristics learned during fine-tuning. We additionally use three methods – direct syntactic ablation of input text and two means of studying internal model values – to probe one high-performing LLM for features that characterize styles. We find that authorial style is easier to characterize than genre-level style and is more impacted by minor syntactic decisions and contextual word usage. However, some traits like pronoun usage and word order prove significant for defining both kinds of literary style.

In this article, we evaluate several large language models (LLMs) on a word-level translation alignment task between Ancient Greek and English. Comparing model performance to a human gold standard, we examine the performance of four different LLMs, two open-weight and two proprietary. We then take the best-performing model and generate examples of word-level alignments for further finetuning of the open-weight models. We observe significant improvement of open-weight models due to finetuning on synthetic data. These findings suggest that open-weight models, though not able to perform a certain task themselves, can be bolstered through finetuning to achieve impressive results. We believe that this work can help inform the development of more such tools in the digital classics and the computational humanities at large.