@inproceedings{ferdusi-etal-2026-behind,
    title = "Behind the Laughter: Uncovering Gender Bias in Code-Mixed {B}angla Memes",
    author = "Ferdusi, Jannatul  and
      Saha, Labanya  and
      Chowdhury, Paria  and
      Hossain, Jawad  and
      Arnob, Noor Mairukh Khan",
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.1/",
    pages = "1--9",
    ISBN = "979-8-89176-424-8",
    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"
}