首个针对孟加拉语讽刺性仇恨内容的多模态数据集与检测模型
Detecting Hate and Inflammatory Content in Bengali Memes: A New Multimodal Dataset and Co-Attention Framework
- 构建多模态共注意力框架，联合分析图片与文字特征
- 在3247张标注孟加拉语表情包上实现超越现有模型的准确率
- 首次区分仇恨言论与煽动性内容，适合低资源语言研究者
互联网表情包已成为孟加拉语社区社交平台的主要表达形式。尽管常具幽默性，但亦可能被用于传播针对个人或群体的攻击性、有害及煽动性内容。此类内容因讽刺性强、隐含微妙且具有文化特定性，检测难度极高，尤其对孟加拉语等低资源语言更为严峻。为填补此研究空白，本文提出Bn-HIB（Bangla Hate Inflammatory Benign）数据集，包含3,247张经人工标注的孟加拉语表情包，分为良性、仇恨和煽动三类。该数据集是首个在孟加拉语中区分煽动性内容与直接仇恨言论的数据集。同时提出MCFM（多模态共注意力融合模型），通过共注意力机制协同分析图像与文本中的关键特征，提升分类精度。实验表明，MCFM在Bn-HIB数据集上显著优于多个前沿模型。为促进可复现性与后续研究，数据集已通过Mendeley Data公开。注意：本研究涉及可能令人不适的内容，建议谨慎观看。
原文摘要 · Abstract (English)
Internet memes have become a dominant form of expression on social media, including within the Bengali speaking community. While often humorous, memes can also be exploited to spread offensive, harmful, and inflammatory content targeting individuals and groups. Detecting this type of content is exceptionally challenging due to its satirical, subtle, and culturally specific nature. This problem is magnified for low-resource languages like Bengali, as existing research predominantly focuses on high-resource languages. To address this critical research gap, we introduce Bn-HIB (Bangla Hate Inflammatory Benign), a novel dataset containing 3,247 manually annotated Bengali memes categorized as Benign, Hate, or Inflammatory. Significantly, Bn- HIB is the first dataset to distinguish inflammatory content from direct hate speech in Bengali memes. Furthermore, we propose the MCFM (Multi-Modal Co-Attention Fusion Model), a simple yet effective architecture that mutually analyses both the visual and textual elements of a meme. MCFM employs a co-attention mechanism to identify and fuse the most critical features from each modality, leading to a more accurate classification. Our experiments show that MCFM significantly outperforms several state-of-the-art models on the Bn-HIB dataset, demonstrating its effectiveness in this nuanced task. To facilitate reproducibility and future research, the Bn-HIB dataset has been made publicly available through Mendeley Data. Warning: This work contains material that may be disturbing to some audience members. Viewer discretion is advised
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