United States

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 \textit{creepypasta}, a digital-born horror genre circulating across heterogeneous infrastructures. Using a corpus of $\sim$23,000 English-language stories published from 2007 to 2024 on Reddit&#39;s \texttt{/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&#39;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.

ACL 2026

Never Care For What They Say ? Platform vs Genre Rules in Online Horror Narratives (2007--2024)

creepypasta

platform studies

computational stylistics

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EMNLP 2024

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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 \textit{creepypasta}, a digital-born horror genre circulating across heterogeneous infrastructures. Using a corpus of $\sim$23,000 English-language stories published from 2007 to 2024 on Reddit's \texttt{/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.

workshop paper

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This study proposes and tests loudness standard
deviation (σ) of fictional sound events as an
acoustically grounded proxy for detecting ex-
plicit content in romance fiction. Working with
a subcorpus of novels from the Harlequin Men
Made in America series, scenes were annotated
for character and ambient sound with loudness
levels. Additionally, the scenes were annotated
on a ternary severity scale with two content ad-
visory categories drawn from the PG-STORY
taxonomy by (Tsai et al.) (SEX & NUDITY and
VIOLENCE & SCARINESS), and tested whether
within-scene loudness σof character and ambi-
ent sound correlates with either category. Loud-
ness standard deviation analyses of character
and ambient sounds in scenes featuring explicit
content reveal that erotic scenes are acousti-
cally marked by significantly higher variability
in character-produced sounds, reflecting the dy-
namic range from whispered dialogue to vocal-
ized arousal.

Between Whispers and Screams: Loudness Standard Deviation as a Proxy for Explicit Content Detection in US Romance Novels

This study introduces and analyzes a novel authorship attribution case: the children’s stories published by Oscar and Constance Wilde. We analyzed the corpus of stories with both supervised (SVM with string kernel) and unsupervised (Hierarchical Clustering via Rank Distance) methods and found a strong stylistic similarity between the story "The Selfish Giant" published by Oscar Wilde and the stylometric profile of Constance Wilde. Starting from this baseline, we also explored the the capabilities of LLMs in authorship attribution via Perplexity. Our finding suggests that the story "The Selfish Giant" might be the result of a collaboration between Oscar and Constance Wilde. Moreover, our results pointed to the distinct stylistic fingerprints of the two authors with regards to the rest of the corpus, confirming that their respective styles are separable despite shared genre and period.

Computational Authorship Attribution in the Children’s Tales of Oscar and Constance Wilde: The Case of "The Selfish Giant"

This work investigates the extent to which open-source Large Language Models (LLMs) can improve accessibility of unstructured historical documents by performing abstractive summarization and Named Entity Recognition (NER). We evaluate seven open-source LLMs in zero-shot settings and apply these tasks to witness testimonies collected by the South African Truth and Reconciliation Commission (TRC), which archived a large body of text documenting human rights violations during apartheid. Despite their historical significance, these texts are difficult to access due to their length, lack of standardized structure, and the absence of systematic indexing. Open-source LLMs show strong performance in summarization, with most models surpassing non-LLM baselines (maximum BERTScore 0.77), while NER performance remains limited (maximum F1-score 0.61). Results suggest a trade-off in which stylistic fluency is prioritized over factual precision. A two-stage pipeline, summarization followed by NER on LLM summaries, leads to measurable improvements.

Evaluating Open-Source LLMs for Text Summarization and Named Entity Recognition in Long, Unstructured Text

This paper introduces \textit{Perspectives}, an interactive extension of a qualitative data analysis tool suite developed at our university, designed to empower Digital Humanities (DH) scholars to explore and organize large, unstructured document collections. \textit{Perspectives} implements a flexible, aspect-focused document clustering pipeline with human-in-the-loop refinement capabilities.
We showcase how this process can be initially steered by defining analytical lenses through document rewriting prompts and instruction-based embeddings, and further aligned with user intent through tools for refining clusters and mechanisms for fine-tuning the embedding model. The demonstration highlights a typical workflow, illustrating how DH researchers can leverage \textit{Perspectives}'s interactive document map to uncover topics, sentiments, or other relevant categories, thereby gaining insights and preparing their data for subsequent in-depth analysis.

Perspectives -- Interactive Document Clustering for Qualitative Data Analysis

Recent advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) have enabled diverse retrieval methods. However, existing retrieval methods often fail to accurately retrieve results for negation and exclusion queries. To address this limitation, prior approaches rely on embedding adaptation or fine-tuning, which introduce additional computational cost and deployment complexity. We propose Direct Embedding Optimization (DEO), a training-free method for negation-aware text and multimodal retrieval. DEO decomposes queries into positive and negative components and optimizes the query embedding with a contrastive objective. Without additional training data or model updates, DEO outperforms baselines on NegConstraint, with gains of +0.0738 nDCG@10 and +0.1028 MAP@100, while improving Recall@5 by +6\% over OpenAI CLIP in multimodal retrieval. These results demonstrate the practicality of DEO for negation- and exclusion-aware retrieval in real-world settings. The code is publicly available at GitHub.

DEO: Training-Free Direct Embedding Optimization for Negation-Aware Retrieval

The rapid growth of scientific publications makes it increasingly difficult to track and synthesize research progress. While Large Language Models (LLMs) can support automated survey generation, existing methods retrieve unstructured data and require models to infer conceptual, methodological, and taxonomic relations from raw text at generation time. We introduce StructSurvey, a hierarchical multi-agent framework that shifts structural reasoning from generation to retrieval by dynamically constructing graph-based representations of entities, relations, and topical taxonomies. We evaluate StructSurvey on a new reference-grounded benchmark of ACL survey papers for reproducible long-form scientific summarization. Compared with embedding-only retrieval baselines, StructSurvey improves ROUGE-1 recall by +2.9 and ROUGE-2 recall by +1.0 on average, without reducing precision. It also improves LLM-as-a-Judge ratings for logical structure, depth, and synthesis, showing that explicit structural retrieval yields surveys closer to human-written organization and reasoning.

StructSurvey: Structured Agentic Retrieval for Automatic Survey Paper Generation

Retrieval-Augmented Generation (RAG) represents a significant advancement in artificial intelligence combining a retrieval phase with a generative phase, with the latter typically being powered by Large Language Models (LLMs). Common wisdom and practices in RAG involve using "instructed" LLMs, which are fine-tuned with supervised training to enhance their ability to follow instructions and are aligned with human preferences using state-of-the-art techniques. However, contrary to this popular belief, our study demonstrates that base models outperform their instructed counterparts in RAG tasks by 20\% on average under our experimental settings. This finding challenges the prevailing assumptions about the superiority of instructed LLMs in RAG applications. Further investigations reveal a more complex situation, questioning fundamental aspects of RAG and suggesting the need for broader discussions on the topic; or, as Fromm would have it, "Seldom is a glance at the statistics enough to understand the meaning of the figures".

A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems

Retrieval-Augmented Generation (RAG) grounds language-model output in external knowledge, yet its application to dense technical documentation remains largely unexplored. Engineering software manuals pose compounding challenges: formulae are corrupted during PDF extraction, heterogeneous content types require different parsing treatment, and queries demand cross-document synthesis across multiple reference volumes. We present an end-to-end RAG system for OpenFOAM, an open-source computational fluid dynamics toolkit, operating in two modes. In single-query mode, a formula-preserving parser (Marker), adaptive header-aware chunking, two-stage dense-then-rerank retrieval, and a citation-enforcement prompt produce grounded, source-attributed answers across a 20-question benchmark. In report mode, a user prompt is decomposed into sub-questions via LLM planning; each sub-question undergoes independent retrieval and cross-encoder re-ranking, and the deduplicated chunk set is passed to a long-context generation call that produces a structured, multi-section report with inline citations. Evaluated on a 10-prompt golden set with a six-dimension LLM-as-a-judge framework, both pipelines achieve overall scores above 4.6/5.0 with perfect citation correctness (5.0/5.0). The decomposed pipeline demonstrates superior robustness (90% vs 70% judge success rate). Retrieval analysis using page-level ground truth reveals low absolute recall (<14%), identifying retrieval breadth as the primary bottleneck.

Decompose, Retrieve, Cite: A RAG Pipeline for Structured Report Generation from Technical Documentation

We introduce $\textbf{EncouRAGe}$, a comprehensive Python library designed to streamline the development and evaluation of Retrieval-Augmented Generation (RAG) systems using Large Language Models (LLMs) and Embedding Models. EncouRAGe comprises five modular and extensible components: $\textit{Type Manifest}$, $\textit{RAG Factory}$, $\textit{Inference}$, $\textit{Vector Store}$, and $\textit{Metrics}$, facilitating flexible experimentation and extensible development. Each component helps to make development RAG evaluation and emphasizes $\textbf{scientific reproducibility}$, $\textbf{diverse evaluation metrics}$, and $\textbf{local deployment}$, enabling researchers to efficiently assess datasets within RAG workflows. This paper presents implementation details and an extensive evaluation across multiple benchmark datasets, including $\textit{25k QA pairs}$ and $\textit{over 51k documents}$. Our results show that RAG still underperforms compared to the $\textit{Oracle Context}$, while $\textit{Hybrid BM25}$ consistently achieves the best results across all four datasets. $\textbf{Code}$: https://github.com/uhh-hcds/encourage

EncouRAGe: Evaluating RAG Local, Reliable and Efficient

Operational safety in mission-critical environments requires AI systems that are accurate, interpretable, and resistant to hallucination. We present an agentic Retrieval-Augmented Generation (RAG) framework, REFSafe, for grounded hazard analysis and automated safety report generation. The system integrates Large Language Models (LLMs) with structured operational data, historical incident repositories, policy documents, and external authoritative sources. Through iterative agentic reasoning, the framework retrieves, verifies, and synthesizes evidence prior to generation, enforcing citation-backed outputs with explicit source attribution (documents, links, and prior events) to ensure traceability and trust. To mitigate hallucinations and unsupported claims, all risk assessments and forecasts are constrained to retrieved evidence, with confidence signals derived from retrieval relevance and source consistency. A transparent pipeline enables subject matter experts (SMEs) to validate predictions, and provide structured feedback, forming a continuous performance calibration loop. Preliminary deployment demonstrates improved reliability in hazard detection and safety/vulnerability report generation. This work advances trustworthy, evidence-grounded AI for predictive safety intelligence in mission-critical operations.

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