Culturally Aware Content Moderation for F acebook Reels: A Cross-Modal Attention-Based Fusion Model for B engali Code-Mixed Data (2025)

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.

## introduction in recent years, there has been a rapid development in web users and sufficient bandwidth. internet connectivity, being very cheap, makes the sharing of information such as text, audio, and video more common and faster. video is most popular among them. by 2025, it is estimated that 82% of internet traffic will be video content foot_0 . for both entertainment and information purposes, social media users across all age groups engage with videos. short videos such as reels have gained massive popularity and currently dominate social media. their growth has accelerated even more with 5g. reels are small in duration but rich in content. they also have better delivery and higher engagement compared to text and images. facebook reels, a prime example, are short, engaging videos shared on facebook. typically lasting a few seconds to a minute, they allow users to enhance content with music, text overlays, filters, and visual effects. however, a darker side exists-videos, reels that violate community guidelines and spread harmful narratives. the failure to remove toxic content can lead to hostile online environments, echo chambers of hateful users, revenue loss, fines, and legal issues. while human moderators are employed to filter such content, the sheer volume of user-generated posts poses a significant challenge, especially with 5.24 billion social media users worldwide foot_2 . additionally, content moderation can take an emotional and psychological toll on moderators. legal regulations further demand the rapid removal of harmful content, adding to the complexity of the issue. another issue that has recently drawn attention from researchers is that, social media content moderation should consider the cultural variations. a content which can be suitable for a culture but inappropriate for another culture. the "one-size-fitsall" approach for content moderation of social networks such as facebook, instagram, etc. has been criticized by @xcite . in their paper, they discovered that a unique community guideline often does not satisfy cultural expression when making decisions. they also find out that marginal communities often adapt to the platform's policies to evade moderation. the findings in this paper are indeed consistent with reality. in case of bengali culture, a lot of unsafe reels are available, which often do not go with the cultural standard of bangladesh (some examples have been shown in figure 1 . to eradicate the problem of cultural variation in content moderation, @xcite suggested enhancing content moderation by finetuning language models with culturally specific data. current research in harmful content detection is predominantly focused on text-based models @xcite . there is, however, limited exploration in image-based methodologies @xcite , and even fewer studies on video data @xcite . these studies are mostly in monolingual english or high-resource languages. in the context of bengali, a language spoken by 237 million native speakers foot_3 , the exploration is particularly scarce in the multimodal domain. notable works include @xcite , which focuses on text and images of memes for detecting hateful content, and @xcite , which deals with three modalities (text, audio, video) but solely for emotion classification. additionally, islam and rony, 2024 explores toxic speech detection in code-mixed bengali-english language across text, audio, and video domains by incorporating 431 videos from youtube. however, this study processes individual utterances in isolation, ignoring the broader context within the video, and has quite a limited data set. the banvatllm framework also demands significant gpu resources for its multiple encoders (whisper, videomae, chatgpt-3.5), making it impractical for real-time moderation or deployment on low-resource systems. our research involves introducing a novel dataset of 1,111 facebook reels, short informative videos, which include audio, visual content, and text overlays. we have also formulated and developed efficient, effective frameworks for contextual analysis of these videos. since there is no suitable tool for video data annotation available, we have developed a tool to ease the process of annotation. it helps annotators focus on every piece of information, resulting in better-quality datasets. we have also proposed a multimodal multiclass classification framework for this dataset and classified the content into four categories: safe, adult, harmful, and suicidal. our framework has achieved good performance and obtained the weighted average f1 score of 0.75. the major contributions include: ## background study the widespread emergence of multimodal data has resulted in the development of multimodal deeplearning techniques. however, their development is severely lagging behind compared to the unimodal approaches. ## unber: a new benchmark dataset we have developed unber: a novel multimodal video dataset for bengali-english unsafe reel classification, which is firmly based on the bengali culture. unber dataset contains short videos represented with their audio, visual, and text modality. for the text modality, only the texts that are visible in the short videos/reels have been considered. this section discusses about the creation, annotation, and analytics of unber. ## unber: benchmark evaluation this section provides a detailed discussion about the experimental setting, comparative results and the error analysis done by us to evaluate our dataset. ## conclusion safe use of social media in the bengali cultural context is a significant demand of a good cultural society. during this generation, where reel/short videos are on top of the trend with high manipulation power, an efficient, culturally-aware moderation process in every language is necessary to pull the reins of the decay of moral values. our dataset "unber" is a crucial contribution to the bengali facebook reel filtering process. this paper describes clearly the creation, annotation, and preparation process of this dataset and also shows an abstract methodology to evaluate the nature and efficiency of the dataset and to create a baseline for this dataset for later use. also, the proposed framework is adaptable to larger datasets and can suit on different cultural contexts beyond bengali, making it scalable.