@inproceedings{ailneni-harabagiu-2026-exploration,
title = "Exploration of How Hate Is Framed on Social Media",
author = "Ailneni, Rakshitha Rao and
Harabagiu, Sanda",
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-1.663/",
doi = "10.63317/38db94nbbg2e",
pages = "8400--8414",
abstract = "Understanding how hate is framed in multimodal social media content is crucial for developing interpretable and robust hate detection systems. We present the MM-HateFrames Dataset, a large-scale resource encoding 2,298 Hate Frames (HFs) and their corresponding rationales discovered from two benchmark datasets{---}Hateful Memes and MMHS150K{---}comprising over 11K+ social media multimodal posts. This allowed us to explore several generative and non-generative methods to automatically discover the way hate is framed when relying on MM-HateFrames, including clustering-based methods and large multimodal models (LMMs) under zero-shot and few-shot settings. Experimental evaluations show that few-shot LMMs prompting generates the most coherent and sound frame articulations. The MM-HateFrames Dataset provides a valuable foundation for future research in hate speech understanding, frame articulation, and explainable multimodal NLP, enabling models to interpret not only whether content is hateful but also how hate is conceptually framed."
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<abstract>Understanding how hate is framed in multimodal social media content is crucial for developing interpretable and robust hate detection systems. We present the MM-HateFrames Dataset, a large-scale resource encoding 2,298 Hate Frames (HFs) and their corresponding rationales discovered from two benchmark datasets—Hateful Memes and MMHS150K—comprising over 11K+ social media multimodal posts. This allowed us to explore several generative and non-generative methods to automatically discover the way hate is framed when relying on MM-HateFrames, including clustering-based methods and large multimodal models (LMMs) under zero-shot and few-shot settings. Experimental evaluations show that few-shot LMMs prompting generates the most coherent and sound frame articulations. The MM-HateFrames Dataset provides a valuable foundation for future research in hate speech understanding, frame articulation, and explainable multimodal NLP, enabling models to interpret not only whether content is hateful but also how hate is conceptually framed.</abstract>
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%0 Conference Proceedings
%T Exploration of How Hate Is Framed on Social Media
%A Ailneni, Rakshitha Rao
%A Harabagiu, Sanda
%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 ailneni-harabagiu-2026-exploration
%X Understanding how hate is framed in multimodal social media content is crucial for developing interpretable and robust hate detection systems. We present the MM-HateFrames Dataset, a large-scale resource encoding 2,298 Hate Frames (HFs) and their corresponding rationales discovered from two benchmark datasets—Hateful Memes and MMHS150K—comprising over 11K+ social media multimodal posts. This allowed us to explore several generative and non-generative methods to automatically discover the way hate is framed when relying on MM-HateFrames, including clustering-based methods and large multimodal models (LMMs) under zero-shot and few-shot settings. Experimental evaluations show that few-shot LMMs prompting generates the most coherent and sound frame articulations. The MM-HateFrames Dataset provides a valuable foundation for future research in hate speech understanding, frame articulation, and explainable multimodal NLP, enabling models to interpret not only whether content is hateful but also how hate is conceptually framed.
%R 10.63317/38db94nbbg2e
%U https://aclanthology.org/2026.lrec-1.663/
%U https://doi.org/10.63317/38db94nbbg2e
%P 8400-8414
Markdown (Informal)
[Exploration of How Hate Is Framed on Social Media](https://aclanthology.org/2026.lrec-1.663/) (Ailneni & Harabagiu, LREC 2026)
ACL
- Rakshitha Rao Ailneni and Sanda Harabagiu. 2026. Exploration of How Hate Is Framed on Social Media. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 8400–8414, Palma de Mallorca, Spain. ELRA Language Resource Association.