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        <title>Culturally Aware Content Moderation for Facebook Reels: A Cross-Modal Attention-Based Fusion Model for Bengali Code-Mixed Data</title>
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    <abstract>The advancement of high-speed internet and affordable bandwidth has led to a significant increase in video content and has brought challenges in content moderation due to the spread of unsafe or harmful narratives quickly. The rise of short-form videos like “Reels”, which is easy to create and consume, has intensified these challenges even more. In case of Bengali culture-specific content, the existing content moderation system struggles. To tackle these challenges within the culture-specific Bengali codemixed domain, this paper introduces “UNBER” a novel dataset of 1,111 multimodal Bengali codemixed Facebook Reels categorized into four classes: Safe, Adult, Harmful, and Suicidal. Our contribution also involves the development of a unique annotation tool “ReelAn” to enable an efficient annotation process of reels. While many existing content moderation techniques have focused on resource-rich or monolingual languages, approaches for multimodal datasets in Bengali are rare. To fill this gap, we propose a culturally aware cross-modal attention-based fusion framework to enhance the analysis of these fast-paced videos, which achieved a macro F1 score of 0.75. Our contributions aim to significantly advance multimodal content moderation and lay the groundwork for future research in this area.</abstract>
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%0 Conference Proceedings
%T Culturally Aware Content Moderation for Facebook Reels: A Cross-Modal Attention-Based Fusion Model for Bengali Code-Mixed Data
%A Labib, Momtazul Arefin
%A Rahman, Samia
%A Murad, Hasan
%Y Alam, Mehwish
%Y Tchechmedjiev, Andon
%Y Gracia, Jorge
%Y Gromann, Dagmar
%Y di Buono, Maria Pia
%Y Monti, Johanna
%Y Ionov, Maxim
%S Proceedings of the 5th Conference on Language, Data and Knowledge
%D 2025
%8 September
%I Unior Press
%C Naples, Italy
%@ 978-88-6719-333-2
%F labib-etal-2025-culturally
%X The advancement of high-speed internet and affordable bandwidth has led to a significant increase in video content and has brought challenges in content moderation due to the spread of unsafe or harmful narratives quickly. The rise of short-form videos like “Reels”, which is easy to create and consume, has intensified these challenges even more. In case of Bengali culture-specific content, the existing content moderation system struggles. To tackle these challenges within the culture-specific Bengali codemixed domain, this paper introduces “UNBER” a novel dataset of 1,111 multimodal Bengali codemixed Facebook Reels categorized into four classes: Safe, Adult, Harmful, and Suicidal. Our contribution also involves the development of a unique annotation tool “ReelAn” to enable an efficient annotation process of reels. While many existing content moderation techniques have focused on resource-rich or monolingual languages, approaches for multimodal datasets in Bengali are rare. To fill this gap, we propose a culturally aware cross-modal attention-based fusion framework to enhance the analysis of these fast-paced videos, which achieved a macro F1 score of 0.75. Our contributions aim to significantly advance multimodal content moderation and lay the groundwork for future research in this area.
%U https://aclanthology.org/2025.ldk-1.13/
%P 118-129

Markdown (Informal)

[Culturally Aware Content Moderation for Facebook Reels: A Cross-Modal Attention-Based Fusion Model for Bengali Code-Mixed Data](https://aclanthology.org/2025.ldk-1.13/) (Labib et al., LDK 2025)

• Culturally Aware Content Moderation for Facebook Reels: A Cross-Modal Attention-Based Fusion Model for Bengali Code-Mixed Data (Labib et al., LDK 2025)

ACL

• Momtazul Arefin Labib, Samia Rahman, and Hasan Murad. 2025. Culturally Aware Content Moderation for Facebook Reels: A Cross-Modal Attention-Based Fusion Model for Bengali Code-Mixed Data. In Proceedings of the 5th Conference on Language, Data and Knowledge, pages 118–129, Naples, Italy. Unior Press.