---
title: "ACID: On the Perception of Online Classism — Muti 2026"
description: "In NLP, hate speech research has largely ignored discrimination based on socioeconomic status (SES)."
authors: ["Arianna Muti", "Elisa Bassignana", "Amanda Cercas Curry", "Federica Durante", "Dirk Hovy", "Debora Nozza"]
year: 2026
venue: "Proceedings of the Fifteenth Language Resources and Evaluation Conference"
source: "https://aclanthology.org/2026.lrec-1.857/"
doi: "10.63317/2myisgn9aju6"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/muti-2026-acid-perception-online-classism/"
---
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# ACID: On the Perception of Online Classism

**Authors:** [Arianna Muti](https://md.research.elamur.ai/free/authors/arianna-muti/), Elisa Bassignana, [Amanda Cercas Curry](https://md.research.elamur.ai/free/authors/amanda-cercas-curry/), Federica Durante, [Dirk Hovy](https://md.research.elamur.ai/free/authors/dirk-hovy/), Debora Nozza · **Year:** 2026 · **Venue:** Proceedings of the Fifteenth Language Resources and Evaluation Conference · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.lrec-1.857/) · [PDF](https://aclanthology.org/2026.lrec-1.857.pdf) · [DOI 10.63317/2myisgn9aju6](https://doi.org/10.63317/2myisgn9aju6) · [OpenAlex](https://openalex.org/W7160175338)

**Citations:** 0 citations (openalex-snapshot · 2026-10-14)

## Summary
In NLP, hate speech research has largely ignored discrimination based on socioeconomic status (SES). Few existing hate speech definitions even mention class. To fill this gap, the authors build ACID (Automatic Classism Identification Dataset), a corpus of more than 1.15 million English YouTube comments and Twitter posts from 14 English-speaking countries. Texts were gathered through lower-SES keywords covering both poverty and working-class identity.

The analytical framework draws on the Stereotype Content Model and a grounded-theory approach. It combines a small manually annotated subset with unsupervised topic modeling and LLM-based classification. Each SES-related text is examined on three dimensions:
- which stereotypes it invokes,
- whether it blames individuals or structural conditions,
- whether it is offensive.

Across countries, the stereotypes look strikingly alike. They are mostly moral judgments that cast lower-SES people as lazy, ignorant, dependent or criminal. Individual blame dominates overall, but Twitter shows more structural explanations than YouTube. Offensiveness is high, especially when it accompanies individual blame. The resource gives researchers in NLP and the social sciences a basis for detecting classism and studying it across cultures.

## Key points
- **Corpus size and sources:** ACID contains 505,584 YouTube and 646,957 Twitter instances. The Twitter portion reuses the DRAX dataset (tweets from August to November 2022). YouTube comments were collected in September 2025 through the YouTube Data API.
- **YouTube filtering:** videos had to have titles combining an SES keyword and a country name, plus at least 100 comments. English texts were kept using fasttext-langdetect. The US is the largest country subset on both platforms.
- **Annotation consistency:** a pilot annotation of 150 YouTube comments by three annotators was used to check agreement.
- **Country differences:** individual blame is strongest in the UK and US, both highly individualistic societies. Structural explanations are more visible in less individualistic countries such as India.
- **Availability and caveats:** data and code are released on GitHub (elisabassignana/acid-online-classism). The authors note that keyword sampling, English-only data and LLM classification errors mean the findings probably underestimate how common online classism is.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{muti2026acid,
  title = {{ACID: On the Perception of Online Classism}},
  author = {Muti, Arianna and Bassignana, Elisa and Curry, Amanda Cercas and Durante, Federica and Hovy, Dirk and Nozza, Debora},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/2myisgn9aju6},
  url = {https://doi.org/10.63317/2myisgn9aju6}
}
```

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