Computational studies of fanfiction have gained traction in recent years, due to both the abundance of data and the development of contemporary NLP methods and tools. In this position paper, we outline the predominant themes and findings of previous studies of fanfiction and propose fruitful suggestions for future research. Specifically, we identify two primary ways that fanfiction has been approached from a computational perspective: one concerning the style of successful or popular fanfiction; the other concerning gender and power dynamics in fanfiction texts. This existing research, however, has only begun to grapple with the complexities and challenges of working with fan-produced content. We argue that online fanfiction is a complex and, in many ways, unique cultural phenomenon which requires new ways of thinking about the motivations and purposes of textual production. Fanfiction is a dynamic, community-produced, transformative genre, and as long as research neglects this whole picture, studies will remain underdeveloped and insufficient to answer meaningful questions. Furthermore, these new ways of approaching fanfiction need to be based on ethical archiving and research practices that are rooted in the theory from qualitative research, and which show ethical care for the people who create and engage with fanfiction.
This work presents an interpretable framework for socioeconomic status (SES) profiling based on narrative data. Building on our previous publication, “AI Assistant for Socioeconomic Empowerment Using Federated Learning” (NLP4DH 2025), this extended study explores a complementary system that focuses on thematic topic modeling, transformer-based embedding comparisons, and visualization tools. The framework analyzes student and public narratives to detect SES-related themes (e.g., financial hardship, resilience, access to resources) and assigns SES profiles through similarity-based scoring. By emphasizing interpretability and topic-based filtering, the system facilitates analysis of language patterns linked to different SES groups while supporting qualitative inspection. Results demonstrate the model’s ability to generalize across diverse domains and align with known social science frameworks, contributing toward responsible and transparent AI in education and public policy contexts.
We use Icelandic corpora, the Icelandic Gigaword Corpus, the Icelandic Parsed Historical Corpus, and the Newspaper Corpus, in order to investigate the history of a syntactic construction, Stylistic Fronting (SF). SF has long been noted to be associated with formal style and it has received considerable attention in the theoretical syntax literature, but less has been said in the literature about its history throughout the centuries and modern times. We find that use of SF remained stable from the 12th century to the 20th century, but its rate declines in the (late) 20th and 21st century. Our analysis furthermore shows that use of SF is only significantly connected to genre in the most recent data, suggesting that the link between SF and style may be a relatively modern innovation. Finally, we test our data for the Constant Rate Effect, revealing that it is present for some grammatical contexts of SF. Our paper is a digital humanities study of historical linguistics which would not be possible without parsed corpora that together span all centuries involved in the change.
Social media platforms, such as Twitter (now X), play a crucial role during crises by enabling real-time information sharing. However, the multimodal data can be ambiguous with misalignment of labels cross-modality. Being able to classify informative and not informative tweets can help in crisis response, yet they can be ambiguous and unbalanced in datasets, impairing model performance. This study explores the effectiveness of multimodal learning approaches for classifying crisis-related tweets regardless of ambiguity and addressing class imbalance through synthetic data augmentation using generative artificial intelligence (AI). Experimental results demonstrate that multimodal models consistently outperform unimodal ones, particularly on ambiguous tweets where label misalignment between modalities is prevalent. Furthermore, the addition of synthetic data significantly boosts macro F1 scores, indicating improved performance on the minority class.
This study investigates the correspondences between a recent map of Uralic languages that also covers the Erzya and Moksha languages in detail. We discuss our point of view in linguistic cartography more generally, but especially within the context of Uralic languages, and address various difficulties that can be recognized in defining the speaker area boundaries and choosing settlements that should be included in the traditional or contemporary speech communities. We use the historical data of Heikki Paasonen, which, we believe, is a highly reliable indicator of at least some areas that should be included in the traditional distributions of these languages as points of comparison. This data is contrasted with the contemporary language maps.
