Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes

Rakshitha Rao Ailneni; Sanda Harabagiu

Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes

Rakshitha Rao Ailneni, Sanda M. Harabagiu

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Abstract

The ability of Large Visual Language Models (LVLMs) to identify hate speech in memes across several cultures requires (1) access to cultural knowledge that is not readily available to them; and (2) the ability to reason with this knowledge. This paper shows how culturally-aware Hate Frames can be acquired to inform hate speech identification across various cultures. The paper also shows that counterfactual reasoning is ideal for identifying hate speech and generating explanations. Promising results show not only improved hate speech identification in each culture, but also reduction of the performance gap of hate speech identification between different cultures.

Anthology ID:: 2026.emnlp-main.2538
Volume:: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Hung-Yi Lee, Sunipa Dev, Jessy Li, Desmond Elliott
Venue:: EMNLP
SIG:: SIGDAT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 57713–57743
Language:
URL:: https://aclanthology.org/2026.emnlp-main.2538/
DOI:
Bibkey:
Cite (ACL):: Rakshitha Rao Ailneni and Sanda M. Harabagiu. 2026. Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57713–57743, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes (Ailneni & Harabagiu, EMNLP 2026)
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PDF:: https://aclanthology.org/2026.emnlp-main.2538.pdf

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@inproceedings{ailneni-harabagiu-2026-discerning,
    title = "Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes",
    author = "Ailneni, Rakshitha Rao  and
      Harabagiu, Sanda M.",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.2538/",
    pages = "57713--57743",
    ISBN = "979-8-89176-459-0",
    abstract = "The ability of Large Visual Language Models (LVLMs) to identify hate speech in memes across several cultures requires (1) access to cultural knowledge that is not readily available to them; and (2) the ability to reason with this knowledge. This paper shows how culturally-aware Hate Frames can be acquired to inform hate speech identification across various cultures. The paper also shows that counterfactual reasoning is ideal for identifying hate speech and generating explanations. Promising results show not only improved hate speech identification in each culture, but also reduction of the performance gap of hate speech identification between different cultures."
}

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%0 Conference Proceedings
%T Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes
%A Ailneni, Rakshitha Rao
%A Harabagiu, Sanda M.
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F ailneni-harabagiu-2026-discerning
%X The ability of Large Visual Language Models (LVLMs) to identify hate speech in memes across several cultures requires (1) access to cultural knowledge that is not readily available to them; and (2) the ability to reason with this knowledge. This paper shows how culturally-aware Hate Frames can be acquired to inform hate speech identification across various cultures. The paper also shows that counterfactual reasoning is ideal for identifying hate speech and generating explanations. Promising results show not only improved hate speech identification in each culture, but also reduction of the performance gap of hate speech identification between different cultures.
%U https://aclanthology.org/2026.emnlp-main.2538/
%P 57713-57743

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Markdown (Informal)

[Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes](https://aclanthology.org/2026.emnlp-main.2538/) (Ailneni & Harabagiu, EMNLP 2026)

Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes (Ailneni & Harabagiu, EMNLP 2026)

ACL

Rakshitha Rao Ailneni and Sanda M. Harabagiu. 2026. Discerning Culturally-Aware Hate Framings for Hate Speech Identification in Memes. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57713–57743, Budapest, Hungary. Association for Computational Linguistics.