T eam_ L uminaries_0227@ LT - EDI -2025: A Transformer-Based Fusion Approach to Misogyny Detection in C hinese Memes (2025)

Abstract: Memes, originally crafted for humor or cultural commentary, have evolved into powerful tools for spreading harmful content, particularly misogynistic ideologies. These memes sustain damaging gender stereotypes, further entrenching social inequality and encouraging toxic behavior across online platforms. While progress has been made in detecting harmful memes in English, identifying misogynistic content in Chinese remains challenging due to the language’s complexities and cultural subtleties. The multimodal nature of memes, combining text and images, adds to the detection difficulty. In the LT-EDI@LDK 2025 Shared Task on Misogyny Meme Detection, we have focused on analyzing both text and image elements to identify misogynistic content in Chinese memes. For text-based models, we have experimented with Chinese BERT, XLM-RoBERTa and DistilBERT, with Chinese BERT yielding the highest performance, achieving an F1 score of 0.86. In terms of image models, VGG16 outperformed ResNet and ViT, also achieving an F1 score of 0.85. Among all model combinations, the integration of Chinese BERT with VGG16 emerged as the most impactful, delivering superior performance, highlighting the benefit of a multimodal approach. By exploiting these two modalities, our model has effectively captured the subtle details present in memes, improving its ability to accurately detect misogynistic content. This approach has resulted in a macro F1 score of 0.90355, securing 3rd rank in the task.

## introduction the rise of social media has transformed communication but also contributed to the spread of harmful content, including misogynistic memes. these memes combine text and images to reinforce negative gender stereotypes @xcite . while research has focused on english memes @xcite , misogyny is increasing in tamil and malayalam memes @xcite . often humorous @xcite , they still normalize disrespect toward women @xcite , highlighting the need for multimodal detection models @xcite . the lt-edi@ldk 2025 shared task on misogyny meme detection tackles the challenge of identifying misogynistic content in memes from chinese social media, requiring models to analyze both text and images. the task's goal is to classify memes into misogynistic and non-misogynistic categories @xcite . in this study, we have proposed a multimodal framework that integrates textual and visual features for detecting misogynistic content. integrating bert (bert-base-chinese) for text representation and vgg16 for visual feature extraction, the fusion of bert (bert-base-chinese) + vgg16 results in remarkable advancements in performance. it achieves an impressive f1 score of 0.92 on the validation set, underscoring the power of combining both textual and visual data for more accurate hate speech detection. for text-based models, we have experimented with chinese bert, xlm-roberta and distilbert, with chinese bert yielding the highest performance. in terms of image models, vgg16 outperformed resnet and vit, demonstrating superior ability in extracting crucial features. the combination of chinese bert and vgg16 has proven to be the most effective, yielding the best results in the task. the core contributions of our research work are as follows- for a comprehensive guide on the implementation process and to access the complete codebase, please visit the github repository: https://github.com/ajfaisal002/misogynymeme-detection. ## related work misogyny detection has evolved into a critical area of research, initially concentrating on identifying misogynistic content in english memes (farinango cuervo and parde, 2022), but gradually expanding to include multilingual contexts and more complex forms of content. transformer-based models like bert and roberta have shown strong performance in understanding nuanced language, especially for multilingual tasks @xcite . early meme detection relied on unimodal models processing text or images separately, limiting their effectiveness. multimodal approaches like embedding-level fusion @xcite and dual-stage fusion in memefier @xcite enhanced performance. benchmarks from memotion (2020, 2022) and multioff @xcite have driven progress in offensive content detection. the mdmd dataset by @xcite focuses on misogyny in tamil and malayalam memes, providing detailed gender bias annotations. the multitask meme classification shared task by @xcite explored misogyny and troll content detection, specifically in tamil and malayalam, offering insights and benchmarks for current approaches. @xcite provide an overview of misogyny meme detection methods and results for chinese social media, setting benchmarks for this task. ## data description the misogyny meme detection dataset, derived from chinese social media, has combined text and image data, split into train, dev and test sets. each train and dev sample includes an image, label and transcription, while the test set provides only the image and text for classification. the data distribution is shown in ## results and analysis this task has evaluated models using text and image data to detect misogyny. while training performance was strong, test results have revealed overfitting, highlighting the need for better generalization and fusion techniques. ## conclusion the lt-edi@ldk 2025 shared task highlighted challenges in detecting misogynistic chinese memes, where traditional models struggled with subtle language and visuals. chinese bert performed well but overfitting remained an issue. multimodal fusion of text and images improved detection by capturing nuanced patterns, helped by regularization and fine-tuning. these results stress the importance of multimodal methods and diverse datasets. although late fusion showed promise, the model's use beyond misogyny detection is limited by domain and cultural factors. future work should explore broader datasets for better generalization. this system can be integrated into real-time moderation to improve automated harmful content detection and intervention.