CUET _320@ LT - EDI -2025: A Multimodal Approach for Misogyny Meme Detection in C hinese Social Media (2025)

Abstract: Detecting misogyny in memes is challenging due to their complex interplay of images and text that often disguise offensive content. Current AI models struggle with these cross-modal relationships and contain inherent biases. We tested multiple approaches for the Misogyny Meme Detection task at LT-EDI@LDK 2025: ChineseBERT, mBERT, and XLM-R for text; DenseNet, ResNet, and InceptionV3 for images. Our best-performing system fused fine-tuned ChineseBERT and DenseNet features, concatenating them before final classification through a fully connected network. This multimodal approach achieved a 0.93035 macro F1-score, winning 1st place in the competition and demonstrating the effectiveness of our strategy for analyzing the subtle ways misogyny manifests in visual-textual content.

## introduction the continuous rise of social media platforms has reshaped how people communicate and share content online. however, this digital transformation has also led to the proliferation of harmful content, including gender bias and sexism, often expressed through memes. memes-though academically debated-have become deeply embedded in everyday digital interactions. their combination of visual elements and textual content creates significant challenges for content moderation due to their multi-modal nature and the cultural context they carry @xcite @xcite . the shared task on misogyny meme detection in chinese social media at dravidianlangtech@ldk 2025 aims to address this issue by identifying misogynistic content within chinese memes. chinese memes are unique in that they often include complex characters, cultural references, and idioms, necessitating specialized approaches for accurate detection. given the limited research on multi-modal misog-yny detection in chinese social media, this task fills a significant gap in the field @xcite . our contribution to this shared task includes: our approach achieved top performance in the task, advancing the field of multi-modal content moderation for chinese social media and contributing to efforts aimed at creating safer online spaces. our implementation details are available online foot_0 . ## related work detecting misogynistic content in memes presents unique challenges due to their multimodal nature. research has approached this through text, images, or combined modalities. researchers in pamungkas et al., 2020 have focused on text-based misogyny detection using traditional machine learning, primarily employing svms to identify hateful language. other text-based approaches analyzed hate comments to better understand linguistic patterns in misogynistic content @xcite . however, textonly methods show limitations when targeting specific groups, highlighting the need for specialized misogyny detection datasets rather than generic hate speech systems. similarly, vision-based research using cnns and transformers has demonstrated promise, but visual cues alone often prove insufficient. integrating textual and visual features has yielded superior results. systems combining naive bayes classifiers with visual processing have improved detection in low-resource languages, while advanced fusion techniques with perceiver io, roberta, and vision transformers effectively address both binary and multi-label tasks @xcite . other approaches use pre-trained clip models to bridge the semantic gap between modalities @xcite . recent work has extended multimodal misogyny detection to low-resource languages like tamil and tulu, emphasizing the need for culturally aware systems @xcite . ## task and dataset description we used the dataset from the shared task on misogyny meme detection -lt-edi@ldk 2025 @xcite , which includes 1,190 training, 170 development, and 340 test samples. the data comprises codemixed chinese-english memes, with a notable imbalance-fewer misogynistic samples than nonmisogynistic ones (as shown in ## methodology the schematic representation of our approach is depicted in figure 2 . ## conclusion in this study, we started by experimenting with a few unimodal models, focusing separately on text and image data. while these gave us a decent starting point, it was the multimodal models, which combine both visual and textual features, that really stood out. in particular, our densenet + chinese bert model achieved the best results, reaching an f1 score of 0.93. despite working with a relatively limited dataset, these findings show that combining modalities is crucial for capturing the complex and often subtle nature of misogynistic content in memes. for future work, we plan to expand the dataset, explore better data augmentation, and fine-tune our multimodal fusion techniques to push performance even further.