CUET ’s_ W hite_ W alkers@ LT - EDI -2025: A Multimodal Framework for the Detection of Misogynistic Memes in C hinese Online Content (2025)

Abstract: Memes, combining visual and textual elements, have emerged as a prominent medium for both expression and the spread of harmful ideologies, including misogyny. To address this issue in Chinese online content, we present a multimodal framework for misogyny meme detection as part of the LT-EDI@LDK 2025 Shared Task. Our study investigates a range of machine learning (ML) methods such as Logistic Regression, Support Vector Machines, and Random Forests, as well as deep learning (DL) architectures including CNNs and hybrid models like BiLSTM-CNN and CNN-GRU for extracting textual features. On the transformer side, we explored multiple pretrained models including mBERT, MuRIL, and BERT- base-chinese to capture nuanced language representations. These textual models were fused with visual features extracted from pretrained ResNet50 and DenseNet121 architectures using both early and decision-level fusion strategies. Among all evaluated configurations, the BERT-base-chinese + ResNet50 early fusion model achieved the best overall performance, with a macro F1-score of 0.8541, ranking 4th in the shared task. These findings underscore the effectiveness of combining pretrained vision and language models for tackling multimodal hate speech detection.

## introduction in recent years, meme culture has become a dominant form of expression on chinese social media platforms. memes are often humorous or satirical, but like any medium, they are not immune to misuse @xcite . increasingly, this format is being exploited to disseminate discriminatory or hateful ideas-misogyny among the most concerning. identifying such harmful content is essential in creating safer digital communities. while the detection of offensive content in memes has seen growing attention @xcite , research that targets misogynistic content specifically, particularly in the chinese context, is still in its infancy. prior studies have largely focused on high-resource languages such as english, hindi, and arabic where comprehensive datasets and pretrained models are more readily available. by contrast, the chinese language, despite its vast user base, remains comparatively underrepresented in this domain @xcite . to address this shortfall, we introduce a multimodal detection framework tailored to the nuances of chinese meme content. central to our approach is a newly curated dataset, annotated with misogynistic and non-misogynistic labels. the dataset reflects the linguistic and cultural diversity of chinese online spaces, ensuring the model learns from contextually relevant examples. our methodology combines textual analysis, leveraging powerful transformer-based models to parse captions and embedded text. to complement textual analysis, we extract visual features using pretrained convolutional networks like resnet50 and densenet121. these features are then integrated with text representations using early and decision-level fusion strategies. our best-performing configuration, which combines bert-base-chinese and resnet50 via early fusion, demonstrates the effectiveness of this multimodal approach in capturing the complex and often subtle nature of misogynistic content in memes @xcite . our main contributions are as follows: the implementation details have been pro-vided in the following github repository:- https://github.com/mubasshirnaib/misogynymeme-detection . ## related work the rise of misogynistic @xcite and harmful content @xcite in online memes has sparked growing concern and has become a significant area of research. as these memes typically combine both text and images, researchers have increasingly turned to multimodal learning techniques to improve detection capabilities. these techniques aim to process and interpret both the visual and linguistic components of a meme simultaneously-a task that becomes particularly complex in the chinese context, given its rich cultural references, nuanced language, and diverse writing systems. although studies directly targeting misogynistic memes in chinese are limited, various recent works offer solid ground for building suitable approaches. for example, @xcite introduced the mistra model, which merges text features with image embeddings using variational autoencoders (vaes) to compress the image data effectively. this fusion allows the system to capture deeper semantic correlations between text and visuals. expanding on this idea, (srivastava, 2022) developed momenta, a deep neural network that looks at both broad and detailed features within memes. by analyzing overall structure alongside fine-grained details, the model is better equipped to spot nuanced forms of hate speech or misogyny. addressing the multilingual nature of memes, @xcite compiled a large code-mixed dataset in hindi and english, aimed specifically at identifying misogynistic content. they showed that multimodal approaches, especially those trained on code-switched language, are better suited for the mixed-language realities of social media-an insight that is also applicable to chinese content, which often blends mandarin with dialects, slang, or romanized expressions. a notable contribution by @xcite is the scare framework, which emphasizes strong alignment between textual and visual data. the model works by maximizing mutual information across the two modalities, making their shared representation more cohesive and informative. at the same time, it refines how each modality is represented on its own. meanwhile, @xcite took a different approach by combining multiple models into an ensemble. this method benefits from the strengths of each individual model, helping to offset their weaknesses and improve overall detection rates of misogynistic content. the importance of linguistic and cultural diversity in meme detection was also highlighted in the dravidianlangtech-2022 shared task @xcite , where teams focused on memes in languages like tamil and malayalam. their findings reinforced the value of fusing image and text data, especially in low-resource languages. supporting studies by @xcite and @xcite echoed these results, offering evidence that multilingual, multitask frameworks can effectively capture offensive and misogynistic content across different languages and contexts.despite these strides, chinese memes remain an underexplored territory. the combination of symbolic imagery, character-based writing, sarcasm, and internet-specific language poses unique challenges. for any framework designed to detect misogyny in chinese memes, it's crucial to handle visual-linguistic humor, character-level nuance, and even cultural cues that may not be obvious without context. ## task and dataset description this study addresses the task of misogyny meme detection in chinese social media as part of the lt-edi@ldk 2025 shared task @xcite . the given dataset @xcite is multimodal, comprising image-text meme pairs labeled as misogyny or not-misogyny. it is divided into training (1190 samples), validation (170 samples), and a test set (340 samples). the training set includes 841 non-misogynistic and 349 misogynistic examples, while the validation set includes 123 and 47 respectively. the textual data consists of 4,553 total words and 3,902 unique words, reflecting rich linguistic diversity. this setup enables the development of multimodal models that capture both visual cues and nuanced language patterns essential for detecting gender-based harmful content. ## methodology several ml, dl, and transformer-based models were investigated to construct a robust framework for misogyny meme detection (figure 1 ).the implementation details of the models have been opensourced to ensure reproducibility 1 .appendix a presents the system requirements. ## result analysis the performance evaluation of various unimodal and multimodal models on the chinese misogyny meme detection task, which are presented in multimodal fusion approaches significantly outperformed unimodal methods. early fusion models, especially bert-base-chinese combined with resnet-50, achieved the highest performance with an f1-score of 0.8541, indicating strong synergy between visual and textual features. late fusion models also improved results but were slightly less effective than early fusion, emphasizing the value of integrating modalities early in the learning process. these findings affirm that combining vision and language models is crucial for accurately detecting misogynistic content in memes.appendix b presents the error analysis. ## conclusion in this study, we proposed a robust multimodal framework for misogyny meme detection in chinese social media content, developed as part of the lt-edi@ldk 2025 shared task. by combining pretrained transformer-based textual encoders such as bert-base-chinese with visual feature extractors like resnet-50 and densenet-121, our approach effectively captured the nuanced interplay between language and imagery. among all tested configurations, the early fusion model of bertbase-chinese and resnet-50 achieved the best overall performance, demonstrating the strength of deep multimodal representation learning in tackling hate speech detection tasks. these results reinforce the importance of using culturally and linguistically aligned pretrained models for context-sensitive applications like misogyny detection.