An Overview of the Misogyny Meme Detection Shared Task for C hinese Social Media (2025)

Abstract: The increasing prevalence of misogynistic content in online memes has raised concerns about their impact on digital discourse. The culture specific images and informal usage of text in the memes present considerable challenges for the automatic detection systems, especially in low-resource languages. While previous shared tasks have addressed misogyny detection in English and several European languages, misogynistic meme detection in the Chinese has remained largely unexplored. To address this gap, we introduced a shared task focused on binary classification of Chinese language memes as misogynistic or non-misogynistic. The task featured memes collected from the Chinese social media and annotated by native speakers. A total of 45 teams registered, with 8 teams submitting predictions from their multimodal models integrating textual and visual features through diverse fusion strategies. The best-performing system achieved a macro F1-score of 0.93035, highlighting the effectiveness of lightweight pretrained encoder fusion. This system used the Chinese BERT and DenseNet-121 for text and image feature extraction, respectively. A feedforward network was trained as a classifier using the features obtained by concatenating text and image features.

## introduction the proliferation of social media and other online platforms provides users with powerful means to share visual (image and video), audio, and textual content as a form of expressing opinions and beliefs @xcite . memes are typically used for expressing one's opinion through humorous or satirical images overlaid with text @xcite , @xcite , @xcite . however, the dual-mode characteristic, which contains both text and images, and the speed at which the memes are spread make them a powerful tool for communication, which in turn makes them carriers of offensive and hate content, including misogyny @xcite , @xcite . furthermore, misogynistic memes frequently trivialize the violence against women and discriminatory attitudes by employing humor and irony @xcite . misogynistic memes are difficult to detect due to the multimodal ambiguity @xcite , @xcite , @xcite . moreover, the models that are used for detecting the misogynistic memes should be capable of capturing the cultural nuances, sarcasm, and symbolism, mainly because of the diverse linguistic and social settings @xcite . much of the prior research has focused on english and european languages @xcite . for example, the mami shared task at semeval-2022 targeted misogyny detection in english memes using image-text combinations, while semeval-2024 addressed multimodal persuasion techniques in memes across multiple languages, including english and bulgarian. further, recent shared tasks such as lt-edi@eacl 2024 and dravid-ianlangtech@naacl 2025 have explored this problem in tamil and malayalam @xcite . despite these advancements, misogynistic meme detection in chinese-one of the most widely spoken languages globally (julian, 2020)-has remained largely unexplored. to address this gap, we introduced the shared task on misogyny meme detection in the chinese. this task focuses on the binary classification of memes sourced from real-world chinese social media platforms, annotated by native speakers as ei-ther misogynistic or non-misogynistic. the memes reflect various forms of gender-based prejudice, often expressed through culturally grounded visual cues and colloquial language. this shared task encouraged participants to develop multimodal models capable of jointly interpreting visual and textual cues. the multilingual complexity of chinese, the presence of colloquial and coded language, and the use of culturally specific imagery added rich layers of difficulty. by bringing together researchers across nlp, computer vision, and digital humanities, the task aims to foster tools and insights that are socially aware, linguistically grounded, and technically robust. moreover, code-mixing between chinese and english is common on chinese social media platforms @xcite . the code-mixing involves vocabulary substitution, where english words, abbreviations, brand names, or proprietary terms are inserted into chinese sentences @xcite . this creates a lot of challenges for building ai models for code-mixed chinese social media data. the language identification and segmentation is one of the major challenges for models, particularly embedding models. the unavailability of the highquality, large-scale corpus is another challenge for chinese-english code-mixed data modeling. pretrained language models, even large multilingual ones, often perform poorly on code-mixed inputs compared to monolingual or standard multilingual data @xcite . a total of 45 teams registered for the shared task, of which 8 teams successfully submitted their system outputs for final evaluation. the submitted systems employed a range of multimodal strategies, combining textual and visual features through various fusion techniques. the cuet_320 secured the first position by achieving the highest macro f1-score of 0.93035. this demonstrates the effectiveness of lightweight fusion of pretrained text and image encoders. teams cuet_ignite (0.91775) and team_luminaries_0227 (0.90355) attained the second and third positions. these teams showed that transformer-based language models and vision encoders can perform effectively when optimized with minimal compute. despite these successes, ongoing challenges include class imbalance, noisy visual-text alignment, and the handling of codemixed linguistic patterns in chinese memes. ## task description the shared task on misogyny meme detection is organized as part of the lt-edi workshop at ldk 2025 foot_0 . this task focuses on a multimodal classification problem that targets the automatic detection of misogynistic content in memes. memes present a unique challenge for automated hate speech detection due to their multimodal nature, combining both images and text to convey meaning. this shared task aims to address the rising concern of misogynistic content, particularly in chinese online social media platforms. couple of examples from the dataset can be seen in the figure 1 . the task emphasizes the need for robust computational models that can effectively integrate textual and visual information to identify misogyny. participants are required to develop systems that classify memes into two categories: ## methodology of participants this competition included eight entrants, each presenting a distinct approach to misogynist meme identification. most teams employed various preprocessing methods, data augmentation strategies, and model architectures to build multimodal pipelines that integrated textual and visual features. the approaches ranged from deep learning and transformer-based models to traditional machine learning, with different fusion techniques applied to effectively combine modalities. a summary of each team's core methodology is provided in the following section. team_luminaries_0227 (adnan faisal, 2025) used a multimodal fusion strategy to tackle the misogyny meme detection task. after testing with xlm-roberta @xcite and distil-bert @xcite , they used the chinese bert model (bert-base-chinese) for textual analysis. they evaluated vgg16, vit, and resnet in the visual domain before deciding on vgg16 because to its effectiveness. the imagess were preprocessed to 224x224 pixels and enhanced with rotations and random flips. jieba @xcite was used to clean and tokenize the textual data. in order to merge text and image information and feed them into a classifier for the ultimate prediction, the researchers used early fusion. the adam optimizer was used for training, with a batch size of 32, a learning rate of 2e-5, and early stopping determined by validation loss. weighted loss functions were used to remedy class imbalance. on the malayalam dataset, their method produced a macro f1-score of 0.87, and on the tamil dataset, 0.73. cuet_12033 (mehreen rahman, 2025) proposed a compact yet effective multimodal pipeline for detecting misogyny in chinese memes. they preprocessed text by cleaning, converting to simplified chinese, tokenizing with jieba, and transliterating using pypinyin. images were resized to 224×224 rgb and enhanced for brightness and contrast. to address class imbalance, misogynistic samples were augmented with brightness adjustment, grayscale conversion, and posterization. for unimodal models, charbert-base-chinese was enhanced with a bilstm, while image features were extracted using clip, vit, resnet-50, and efficientnet-b0. their best multimodal model fused charbert+bilstm @xcite and vit embeddings using a gated multimodal unit (gmu), followed by a classifier. training included class-weighted loss, mixed precision, learning rate scheduling, dropout, early stopping, and gradient clipping for stability. cuet_ignite (md.mahadi rahman, 2025) proposed a robust multimodal pipeline for misogyny detection in chinese memes. their preprocessing involved cleaning and transliterating text (using jieba and pypinyin) and enhancing 224×224 rgb images. to address class imbalance, they applied augmentation only on misogynistic samples-using image transformations and backtranslation. for modeling, they used charbert with or without bilstm for text and explored clip, vit, resnet-50, and efficientnet-b0 for image features. text and image embeddings were projected into a joint 512-dim space and fused via multi-head attention, followed by a two-layer mlp. training used adam/adamw with class-weighted loss and optimization strategies like mixed precision, scheduling, and early stopping. ssncse (sreeja k, 2025) developed a multimodal classification system using frozen pretrained models for efficient misogyny detection in chinese memes. text was cleaned and tokenized using bert-base, while images were resized and normalized before extracting 2048-dim features with resnet-50. the 768-dim text and 2048-dim image embeddings were concatenated into a 2816dim vector and passed through a lightweight feedforward classifier with relu and dropout. only classifier layers were trained using adam (@xmath0, batch @xmath1, optimizing crossentropy loss. final predictions were stored in csv format. cuet_320 (madiha ahmed chowdhury, 2025) developed an efficient multimodal system for misogyny meme detection by preprocessing text with regex cleaning, jieba tokenization, stopword removal, and truncation, while images were resized, histogram-enhanced, and normalized. to balance classes, misogynistic samples were augmented using brightness, grayscale, and posteriza-tion. features were extracted via chinese bert, xlm-r, mbert (text), and cnns like resnet-50, densenet-121, inception v3 (image). their best model combined chinese bert and densenet-121 features through concatenation and classified them using an adam-optimized feedforward network, showing the effectiveness of simple fusion. cvf_nitt (radhika k t, 2025) employed visual-language models (vlms), particularly clip, to detect sexist elements in chinese memes by aligning ocr-extracted text and image features in a shared 512-dimensional space. they used a lightweight logistic regression classifier on these embeddings (clip+lr) and compared it against traditional fusion setups like bert+resnet-50 and cnn+inception v3 followed by mlps. their early-fusion clip-based approach proved more effective for cross-modal integration. cuet's_white_walkers (md mubasshir naib, 2025) created a modular multimodal system for sexism meme identification by preprocessing text (cleaned, lowercased, tokenized to 128 tokens) and images (resized to 224×224, normalized). images were enhanced through flipping and cropping. they used transformer-based models (mbert, muril, bert-base-chinese), deep (glove/keras embeddings with cnn, bilstm-cnn, cnn-gru), and classical (tf-idf, bow with ml classifiers) to extract features from images and pretrained cnns (resnet50, densenet121, inceptionv3) for text. using an early fusion of bert-base-chinese and resnet50, their optimal multimodal setup outperformed all unimodal baselines with an f1 score of 0.8541. ## results and discussion all the participant's system were evaluated using a macro f1-score. among the participating teams, cuet_320 achieved the highest performance with a macro f1-score of 0.93035 which secured first rank with chinese bert and densenet-121 combination . this was followed by cuet_ignite which obtained a macro f1-score of 0.91775 and ranked second. the third position was held by team_luminaries_0227 which attained a macro f1-score of 0.90355 with chinese bert and vgg16 combination. these results indicate strong overall performance and highlight the effectiveness of the multimodal approaches used by the top teams. most teams froze the pretrained encoders (e.g., resnet50, bert, clip) and trained only the classification layers, which helped reduce overfitting and training cost. fusion techniques such as cross-attention and multi-head attention proved to be highly effective in modeling the relationship between images and text. several teams addressed class imbalance using weighted loss functions or data augmentation, which improved minority class recall. teams used optimization techniques such as fp16 training and batch accumulation to efficiently utilize limited compute resources (e.g., kaggle p100 gpus). these methods significantly improved the misogyny detection in low-resource high-noise meme datasets. ## conclusion in this shared task on misogynistic meme detection, we challenged participants to build a multimodal classification systems capable of identifying misogyny in memes by analyzing both textual and visual components. the participating teams explored a variety of deep learning architectures, including combinations of pretrained cnns, transformerbased text encoders, and fusion mechanisms. the top performing systems demonstrated the importance of careful preprocessing and enhancement, effective multimodal fusion, and efficient training pipelines to address imbalance and limited resources. these findings confirm that tackling harmful online content like misogynistic memes requires context-aware, cross-modal learning approaches. future work may explore zero-shot detection using large vision-language models (e.g., blip-2, llava), prompt-tuning for meme analysis, and generating explanations for detected misogyny to aid in interpretability and platform moderation.