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home / papers / 2026 / Never Care For What They Say ? Platform vs Genre Rules in Online Horror Narratives (2007–2024)

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

Paper · 2026 · Alexandre Lionnet-Rollin and Florian Cafiero

Summary

This study asks whether stylistic regularities in online writing reflect the genre itself or the platform that hosts it. The authors test this with creepypasta, a horror genre born on the internet. They compare about 23,500 English stories (2007–2024) from Reddit's /r/nosleep and the Creepypasta Fandom wiki. First, they read each site's rules closely. r/NoSleep regulates narration and form, while Fandom focuses on community conduct and originality. From this they derive hypotheses about how style should differ. They then compare the two corpora on readability, lexical richness, syntactic proxies, perplexity and emotion scores. The analysis uses univariate tests, a cross-fit L1 logistic classifier and feature ablations.

The platform signal turns out to be narrow. It is driven mostly by r/NoSleep's mandatory first-person narration. Other differences are small and do not form distinct platform styles. Both corpora share an accessible, experiential, fear-dominated prose core. This suggests genre conventions largely persist across platforms. It also supports cross-platform designs in computational literary studies.

Key points

  • Corpus: about 23,500 texts (27.6M tokens). Fandom stories were scraped with Selenium and BeautifulSoup, and /r/nosleep posts came from Pushshift. Metadata include date, author, score and comment count.
  • Features:
    • eight readability indices (Flesch, SMOG, LIX, etc.);
    • MATTR, Honoré's R, lexical density and hapax ratio;
    • spaCy v3.7 (en_core_web_sm) syntactic proxies;
    • GPT-2 (117M) perplexity;
    • DistilRoBERTa emotion scores fine-tuned on GoEmotions.
  • Statistics:
    • Welch t-tests with Benjamini–Hochberg FDR, Cohen's d and Bayes factors;
    • cross-fit L1 logistic regression with held-out GLM inference.
  • Ablation: dropping the first-person ratio alone lowers AUC only to about 0.78. Residualising all other features on it reduces AUC to about 0.70, showing narrator stance is the main organising signal.
  • Shared profile: both platforms sit around grade 5–8 readability and have nearly identical mean sentence length (≈14.41 words). Fear and disgust dominate the emotion scores on both. Code is public, but data are available only on request.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Université Paris Sciences et LettresParis · France21 works · 3,290 citations🎓École Nationale des ChartesParis · France3 works · 14 citations

Also on the byline: École Pratique des Hautes Études.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksLiterature and Literary Theory · 519 worksCultural Studies · 20 works

Cite

Cite as Alexandre Lionnet-Rollin and Florian Cafiero. 2026. Never Care For What They Say ? Platform vs Genre Rules in Online Horror Narratives (2007–2024). Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.nlp4dh-1.31

@inproceedings{lionnetrollin2026never,
  title = {{Never Care For What They Say ? Platform vs Genre Rules in Online Horror Narratives (2007–2024)}},
  author = {Alexandre Lionnet-Rollin and Florian Cafiero},
  booktitle = {Proceedings of the 6th International Conference on Natural Language Processing for the Digital Humanities},
  year = {2026},
  doi = {10.18653/v1/2026.nlp4dh-1.31},
  url = {https://doi.org/10.18653/v1/2026.nlp4dh-1.31}
}

References

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