SSNCSE @ LT - EDI -2025:Detecting Misogyny Memes using Pretrained Deep Learning models (2025)

Abstract: Misogyny meme detection is identifying memes that are harmful or offensive to women. These memes can hide hate behind jokes or images, making them difficult to identify. It’s important to detect them for a safer and respectful internet for everyone. Our model proposed a multimodal method for misogyny meme detection in Chinese social media by combining both textual and visual aspects of memes. The training and evaluation data were part of a shared task on detecting misogynistic content. We used a pretrained ResNet-50 architecture to extract visual representations of the memes and processed the meme transcriptions with BERT. The model fused modality-specific representations with a feed-forward neural net for classification. The selected pretrained models were frozen to avoid overfitting and to enhance generalization across all classes, and only the final classifier was fine-tuned on labelled meme recollection. The model was trained and evaluated using test data to achieve a macro F1-score of 0.70345. As a result, we have validated lightweight combining approaches for multimodal fusion techniques on noisy social media and how they can be validated in the context of hostile meme detection tasks.

## introduction the rise of misogynistic content on social media contexts is increasingly problematic, particularly as social media platforms adopt more multimodal formats such as memes that combine text and images to propagate problematic, exclusionary, or unfair messages. in the context of multilingual and multicultural online spaces that include languages such as chinese, tamil, malayalam, and hindi-english code-mixed communities, identifying misuse introduces additional challenges associated with language, culture, and multimodal resources. recently, researchers have reported the difficulties in identifying misogynistic memes. the authors, @xcite introduced the toxicn-mm dataset, the first large-scale collection of harmful memes in chinese, and proposed a multimodal knowledge enhanced (mke) model tailored for culture-specific meme detection. their findings demonstrate the complicated nature of identification models and consider the value of contextual and cultural knowledge in detection models. similarly, shared tasks and resources such as the @xcite and @xcite have taken this research into low-resource dravidian languages such as tamil and malayalam, collecting and collaborating on annotated datasets such as mdmd, which advance the development of systems that can identify code-mixed, as well as monolingual, data. the mimic project also released a large set of human-annotated examples that will detect misogyny in hindi-english code-mixed memes and can further multimodal hate speech research in minority languages. these two projects show not only the need for effective image-text fusion-based models but also the need to consider cultural aspects of misogyny and multimodal aspects of the data when attempting to detect misogyny in online discourse. the rest of the paper is organized as follows: section 2 analyzes the related works done in the previous research, and section 3 discusses the misogyny meme corpus in the current work. section 4 contains a detailed discussion of the proposed models used in the current work. section 5 explains the experimental results. section 6 discusses the limitations. in section 7 concludes the paper. ## related work the arrival of social media has brought awareness to the issue of misogyny in multimodal content, including memes. @xcite provides the overview of the misogyny meme detection shared task for chinese social media. the task of detecting misogyny in multimodal content with the mami dataset was handled @xcite , who found that multimodal models combining bert for text and vision transformer (vit) for images, when pretrained on hate speech data, substantially outperformed unimodal models. as noted here, @xcite introduced roberta and specifically focused on building on bert by reviewing the positive aspect of pretraining on a significant amount of large-scale data and longer in both iterations and time. in that evaluation, roberta beat bert in a variety of language tasks, reinforcing the trend of using pretrained models, frozen as feature extractors. in a similar vein, @xcite proposed the cold benchmark to identify offensive language in chinese and established the claim that @xcite bertbased models could identify nuance, including biases related to race, gender, and region. as multimodal approaches matured, the use of pretrained models for both textual and visual understanding became increasingly common. memotion 2.0 was introduced in a recent study @xcite , and prior studies showed improved meme sentiment and emotion classification by combining resnet-50 for visual features and bert for textual features. a recent contribution to the field @xcite developed a benchmark dataset for meme-based misogyny detection (both direct and indirect) and emphasized the importance of expert annotations in overcoming sociocultural barriers and interpreting hostile or ironic content. another study @xcite illustrated that lightweight classifiers such as mlps are effective when used with frozen feature extractors in multilingual meme classification. while much of the research has focused on english and indian languages, there is limited work on misogyny detection in multimodal chineselanguage content. this study addresses that gap by establishing a pipeline that combines resnet-50 and bert, both used in frozen mode, with an mlp classifier-achieving a macro-averaged f1-score of 0.70345. this demonstrates the feasibility of developing a robust misogyny detection model for chinese social media using pretrained models and lightweight classification. this study expands on previous work by applying multimodal detection of misogyny to chinese, a linguistically and culturally important domain that has rarely been addressed in existing literature. many previous studies were primarily based on fine-tuning of pretrained models. in this study, we employed frozen pretrained models (bert and resnet-50) along with a minimal mlp classifier, to demonstrate that there's an effective and efficient usage of pretrained models in this, and many other, domains. additionally, our strong performance in identifying misogyny in chinese memes indicates that multimodal pretrained are generalizable to domains beyond english and indian languages. this provides more breadth and scalability to online multilingual safety prevention research. ## dataset description the task aims to develop a model for misogyny meme detection. the dataset for the shared task on misogyny meme detection found at lt-edi@ldk 2025 @xcite , @xcite has been designed to support multimodal and multilingual research on chinese social media data. the dataset is comprised of memes, which consist of both an image and an accompanying textual component that are typically captured from an overlay caption or a comment historically associated with the image. the image contains a variety of meme formats shown on online forums. each of the memes is assisted by a binary label, "misogynistic" or "nonmisogynistic," which conveys whether the meme generates or expresses any form of gender hatred or bias. the dataset consists of three (3) datasets for training, development, and testing. the label distribution for training and development data is mentioned in fig1 and fig2. figure 2: development data distribution lenges of integrating visual and textual information on low-context social media images. ## experimental result the proposed multimodal misogyny classification model, using both visual and text features, using resnet50 and bert respectively, shows that the model is learning sufficiently over the training periods of interest. the loss steadily decreases over each of the epochs, which indicates sufficient learning or convergence; for example, the training loss drops from approximately 0.55 in the first epoch to approximately 0.37 in the last epoch. to assess the generalizability of the model, performance was evaluated on a development dataset, as shown in ## limitations the proposed method is efficient, but it has the following limitations: ## conclusion this paper described a multimodal deep learning approach to the detection of misogyny in memes by utilizing visual and textual modalities together. we took visual features from resnet-50 and textual features from bert. both models used were frozen to save computational cost and training time, and we used a light-weight multilayer perceptron (mlp) to fuse the features and perform binary classification. overall, the results exhibited that, even with minimal fine-tuning, the architecture was capable of capturing the implicit and explicit clues of misogyny that exist within memes. the method also proved to be very robust and simple to use for real-world deployments for harmful content moderation. future work could include end-toend training, different data augmentations or using cross-modal attention for a more complex fusion.