@inproceedings{muti-etal-2026-acid,
title = "{ACID}: On the Perception of Online Classism",
author = "Muti, Arianna and
Bassignana, Elisa and
Cercas Curry, Amanda and
Durante, Federica and
Hovy, Dirk and
Nozza, Debora",
editor = "Piperidis, Stelios and
Bel, N{\'u}ria and
van den Heuvel, Henk and
Ide, Nancy and
Krek, Simon and
Toral, Antonio",
booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
month = may,
year = "2026",
address = "Palma de Mallorca, Spain",
publisher = "ELRA Language Resource Association",
url = "https://aclanthology.org/2026.lrec-1.857/",
doi = "10.63317/2myisgn9aju6",
pages = "10953--10969",
abstract = "Socioeconomic status (SES) structures social inequality and underlies class-based discrimination that is often rationalised through stereotypes expressed in public discourse. However, despite extensive research on hate speech detection in Natural Language Processing, classism detection remains an underexplored phenomenon. We introduce ACID, a cross-cultural corpus with over 1.15 million instances, to investigate classism across YouTube and Twitter from 14 English-speaking countries. We examine (i) which stereotypes are invoked towards lower-SES, (ii) whether blame for lower-SES is attributed to individuals or structural factors, and (iii) whether these people are portrayed offensively. Across platforms, explanations are predominantly framed in terms of individual responsibility. Across countries, class stereotypes consistently revolve around moralized notions of dependency, laziness, and ignorance, revealing a shared global structure of class-based stigma. Our dataset and analysis are a foundation to advance research on class-based discrimination and its representation in online discourse."
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<abstract>Socioeconomic status (SES) structures social inequality and underlies class-based discrimination that is often rationalised through stereotypes expressed in public discourse. However, despite extensive research on hate speech detection in Natural Language Processing, classism detection remains an underexplored phenomenon. We introduce ACID, a cross-cultural corpus with over 1.15 million instances, to investigate classism across YouTube and Twitter from 14 English-speaking countries. We examine (i) which stereotypes are invoked towards lower-SES, (ii) whether blame for lower-SES is attributed to individuals or structural factors, and (iii) whether these people are portrayed offensively. Across platforms, explanations are predominantly framed in terms of individual responsibility. Across countries, class stereotypes consistently revolve around moralized notions of dependency, laziness, and ignorance, revealing a shared global structure of class-based stigma. Our dataset and analysis are a foundation to advance research on class-based discrimination and its representation in online discourse.</abstract>
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%0 Conference Proceedings
%T ACID: On the Perception of Online Classism
%A Muti, Arianna
%A Bassignana, Elisa
%A Cercas Curry, Amanda
%A Durante, Federica
%A Hovy, Dirk
%A Nozza, Debora
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F muti-etal-2026-acid
%X Socioeconomic status (SES) structures social inequality and underlies class-based discrimination that is often rationalised through stereotypes expressed in public discourse. However, despite extensive research on hate speech detection in Natural Language Processing, classism detection remains an underexplored phenomenon. We introduce ACID, a cross-cultural corpus with over 1.15 million instances, to investigate classism across YouTube and Twitter from 14 English-speaking countries. We examine (i) which stereotypes are invoked towards lower-SES, (ii) whether blame for lower-SES is attributed to individuals or structural factors, and (iii) whether these people are portrayed offensively. Across platforms, explanations are predominantly framed in terms of individual responsibility. Across countries, class stereotypes consistently revolve around moralized notions of dependency, laziness, and ignorance, revealing a shared global structure of class-based stigma. Our dataset and analysis are a foundation to advance research on class-based discrimination and its representation in online discourse.
%R 10.63317/2myisgn9aju6
%U https://aclanthology.org/2026.lrec-1.857/
%U https://doi.org/10.63317/2myisgn9aju6
%P 10953-10969
Markdown (Informal)
[ACID: On the Perception of Online Classism](https://aclanthology.org/2026.lrec-1.857/) (Muti et al., LREC 2026)
ACL
- Arianna Muti, Elisa Bassignana, Amanda Cercas Curry, Federica Durante, Dirk Hovy, and Debora Nozza. 2026. ACID: On the Perception of Online Classism. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 10953–10969, Palma de Mallorca, Spain. ELRA Language Resource Association.