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    <abstract>Bangla memes are widely used on social media to express humor and social commentary, yet computational analysis of gender bias in Bangla memes remains largely unexplored. In this work, we present a multimodal framework for detecting gender bias in Bangla memes by jointly analyzing textual and visual con tent. We construct a dataset of 6,846 Bangla and Banglish code-mixed memes annotated into three categories: male-biased, female biased, and neutral. For textual representation, we use BanglishBERT, while visual features are extracted using ConvNeXt, and the two modalities are fused for final classification. Our best-performing model, ConvNeXt + BanglishBERT, achieves accuracy of 0.67 and an F1-score of 0.63, outperforming several multimodal baselines. The results demonstrate the effectiveness of multimodal learning for understanding culturally nuanced and code-mixed meme content in low-resource languages. Code and data available at this link</abstract>
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%0 Conference Proceedings
%T Behind the Laughter: Uncovering Gender Bias in Code-Mixed Bangla Memes
%A Ferdusi, Jannatul
%A Saha, Labanya
%A Chowdhury, Paria
%A Hossain, Jawad
%A Arnob, Noor Mairukh Khan
%Y Chakravarthi, Bharathi Raja
%Y B, Bharathi
%Y Buitelaar, Paul
%Y Thenmozhi, Durairaj
%Y García Cumbreras, Miguel Ángel
%Y Jiménez Zafra, Salud María
%S Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion
%D 2026
%8 July
%I Association for Computational Linguistics
%C Virtual (Online)
%@ 979-8-89176-424-8
%F ferdusi-etal-2026-behind
%X Bangla memes are widely used on social media to express humor and social commentary, yet computational analysis of gender bias in Bangla memes remains largely unexplored. In this work, we present a multimodal framework for detecting gender bias in Bangla memes by jointly analyzing textual and visual con tent. We construct a dataset of 6,846 Bangla and Banglish code-mixed memes annotated into three categories: male-biased, female biased, and neutral. For textual representation, we use BanglishBERT, while visual features are extracted using ConvNeXt, and the two modalities are fused for final classification. Our best-performing model, ConvNeXt + BanglishBERT, achieves accuracy of 0.67 and an F1-score of 0.63, outperforming several multimodal baselines. The results demonstrate the effectiveness of multimodal learning for understanding culturally nuanced and code-mixed meme content in low-resource languages. Code and data available at this link
%U https://aclanthology.org/2026.ltedi-1.1/
%P 1-9

Markdown (Informal)

[Behind the Laughter: Uncovering Gender Bias in Code-Mixed Bangla Memes](https://aclanthology.org/2026.ltedi-1.1/) (Ferdusi et al., LTEDI 2026)

• Behind the Laughter: Uncovering Gender Bias in Code-Mixed Bangla Memes (Ferdusi et al., LTEDI 2026)

ACL

• Jannatul Ferdusi, Labanya Saha, Paria Chowdhury, Jawad Hossain, and Noor Mairukh Khan Arnob. 2026. Behind the Laughter: Uncovering Gender Bias in Code-Mixed Bangla Memes. In Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion, pages 1–9, Virtual (Online). Association for Computational Linguistics.