Research on online cultural production shows that platforms are acting as mediators that can heavily shape textual form. Yet, empirical work is often platform-bounded, making it difficult to assess whether stylistic regularities that we observe are indeed genre signals or if some of them are platform artefacts. We address this question through a cross-platform design focused on creepypasta, a digital-born horror genre circulating across heterogeneous infrastructures. Using a corpus of ∼23,000 English-language stories published from 2007 to 2024 on Reddit’s /r/nosleep and the Creepypasta Fandom wiki, we compare stylistic profiles across platforms and relate them to differences in rule regimes and moderation practices, established through qualitative extraction and close reading of platform guidelines. Across readability indices, lexical diversity measures, syntactic proxies, and a cross-fit feature-based model, we find that platform membership leaves only a narrow stylistic imprint, largely reducible to a single architectural rule: r/NoSleep’s mandatory first-person narration. Beyond this constraint, differences are modest and fail to form coherent platform-specific stylistic signatures. This helps us define what is stylistically common in creepypastas, and understand what the genre is to its writers beyond the topics it deals with or the platform it is written on.

Low-resource languages pose persistent challenges for Natural Language Processing tasks such as lemmatization and part-of-speech (POS) tagging. This paper investigates the capacity of recent large language models (LLMs), including GPT-4 variants and open-weight Mistral models, to address these tasks in few-shot and zero-shot settings for four historically and linguistically diverse under-resourced languages: Ancient Greek, Classical Armenian, Old Georgian, and Syriac. Using a novel benchmark comprising aligned training and out-of-domain test corpora, we evaluate the performance of foundation models across lemmatization and POS-tagging, and compare them with PIE, a task-specific RNN baseline. Our results demonstrate that LLMs, even without fine-tuning, achieve competitive or superior performance in POS-tagging and lemmatization across most languages in few-shot settings. Significant challenges persist for languages characterized by complex morphology and non-Latin scripts, but we demonstrate that LLMs are a credible and relevant option for initiating linguistic annotation tasks in the absence of data, serving as an effective aid for annotation.

Graph-based Retrieval-Augmented Generation (RAG) is increasingly used to explore long, heterogeneous, and weakly structured corpora, including historical archives. However, in such settings, naive full-corpus indexing is often computationally costly and sensitive to OCR noise, document redundancy, and topical dispersion. In this paper, we investigate corpus pre-targeting strategies as an intermediate layer to improve the efficiency and effectiveness of graph-based RAG for historical research.We evaluate a set of pre-targeting heuristics tailored to single-hop and multi-hop of historical questions on HistoriQA-ThirdRepublic, a French question-answering dataset derived from parliamentary debates and contemporary newspapers. Our results show that appropriate pre-targeting strategies can improve retrieval recall by 3–5% while reducing token consumption by 32–37% compared to full-corpus indexing, without degrading coverage of relevant documents.Beyond performance gains, this work highlights the importance of corpus-level optimization for applying RAG to large-scale historical collections, and provides practical insights for adapting graph-based RAG pipelines to the specific constraints of digitized archives.

Authorship attribution is the process of identifying the writer of a disputed text by analyzing distinctive stylistic patterns. By comparing the disputed text against a baseline of known authorship, the task amounts to assessing how strongly the two texts are likely to share an author. However, topical confounding makes the task deceptively easy: two authors covering the same topic may look more alike than one author covering two topics. Scholarly prose offers a natural solution. Academic writers produce multiple papers on related but distinct topics while maintaining consistent stylistic habits. Thus, we introduce HALvest-Contrastive, an English benchmark that draws same-author passages from distinct papers within a disciplinary field to reduce topical overlap, while using different-author passages from the same field to discourage naive keyword matching. The benchmark’s design removes topical shortcuts: a strong lexical baseline fails under these constraints. As a by-product of our benchmark construction, we release the 17B-token multilingual corpus used to produce it. We then revisit how to score authorship: while standard systems compress each document into a single vector, we retain the sequence of token-level representations produced by a language model and compare them with late interaction. We also study patch-level late interaction, which groups neighboring tokens into patches before interaction. Matching at the token level increases Recall@20 by a factor of three to five. The optimal interaction granularity, however, depends on sequence length, with no single granularity performing best across all settings.

Stand-up comedy, and humor in general, are often studied through their verbal content. Yet live performance relies just as much on embodied presence and audience feedback. We introduce TIC-TALK, a multimodal resource with 5,400+ temporally aligned topic segments capturing language, gesture, and audience response across 90 professionally filmed stand-up comedy specials (2015–2024). The pipeline combines BERTopic for 60 s thematic segmentation with dense sentence embeddings, Whisper-AT for 0.8 s laughter detection, a fine-tuned YOLOv8-cls shot classifier, and YOLOv8s-pose for raw keypoint extraction at 1 fps. Raw 17-joint skeletal coordinates are retained without prior clustering, enabling the computation of continuous kinematic signals—arm spread, kinetic energy, and trunk lean—that serve as proxies for performance dynamics. All streams are aligned by hierarchical temporal containment without resampling, and each topic segment stores its sentence-BERT embedding for downstream similarity and clustering tasks. As a concrete use case, we study laughter dynamics across 24 thematic topics: kinetic energy negatively predicts audience laughter rate (r=−0.75,N= 24), consistent with a stillness-before-punchline pattern; personal and bodily content elicits more laughter than geopolitical themes; and shot close-up proportion correlates positively with laughter (r= +0.28), consistent with reactive montage.

Authorship attribution asks whether two texts were written by the same author. Models fine-tuned for this task with the same pretrained encoder, data, and loss can still differ four-fold in Recall@20 depending only on their scoring mechanism. We use mechanistic interpretability tools to explain this gap. Stylistic features such as word length, punctuation density, and function-word frequency are similarly available at every layer in every model we probe, including an off-the-shelf control encoder, suggesting that differences in feature availability do not explain the gap. Instead, causal intervention shows that the scoring mechanism determines where the encoder consolidates authorship signal. Mean pooling forces consolidation in the middle third of the network, while late interaction defers it to the upper third. We further derive this difference from the gradient structure of each scorer, and training dynamics reveal distinct learning trajectories that follow from that difference.

This study explores the potential of stylometric analysis in identifying Self-Defining Memories (SDMs) authored by individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) versus a control group. A sample of 198 SDMs were written by 66 adolescents and were then analysed using Support Vector Classifiers (SVC). The analysis included a variety of linguistic features such as character 3-grams, function words, sentence length, or lexical richness among others. It also included metadata about the participants (gender, age) and their SDMs (self-reported sentiment after recalling their memories). The results reveal a promising ability of linguistic analysis to accurately classify SDMs, with perfect prediction (F1=1.0) in the contextually simpler setup of text-by-text prediction, and satisfactory levels of precision (F1 = 0.77) when predicting individual by individual. Such results highlight the significant role that linguistic characteristics play in reflecting the distinctive cognitive patterns associated with ADHD. While not a substitute for professional diagnosis, textual analysis offers a supportive avenue for early detection and a deeper understanding of ADHD.