CUET _12033@ LT - EDI -2025: Misogyny Detection (2025)

Abstract: Misogynistic memes spread harmful stereotypes and toxic content across social media platforms, often combining sarcastic text and offensive visuals that make them difficult to detect using traditional methods. Our research has been part of the the Shared Task on Misogyny Meme Detection - LT- EDI@LDK 2025, identifying misogynistic memes using deep learning-based multimodal approach that leverages both textual and visual information for accurate classification of such memes. We experiment with various models including CharBERT, BiLSTM, and CLIP for text and image encoding, and explore fusion strategies like early and gated fusion. Our best performing model, CharBERT + BiLSTM + CLIP with gated fusion, achieves strong results, showing the effectiveness of combining features from both modalities. To address challenges like language mixing and class imbalance, we apply preprocessing techniques (e.g., Romanizing Chinese text) and data augmentation (e.g., image transformations, text back-translation). The results demonstrate significant improvements over unimodal baselines, highlighting the value of multimodal learning in detecting subtle and harmful content online.

## introduction memes are a popular way to share jokes, opinions, and emotions online. however, they can also spread harmful ideas like misogynyhatred or dislike toward women @xcite @xcite . unlike plain text, memes combine images and captions, making it harder to detect if they are offensive. a meme might look funny at first, but it can be offensive when the image and text are viewed together @xcite @xcite . misogynistic memes are harder to detect than regular hate speech because they mix images and text and often rely on subtle cultural context that simple methods can miss. these challenges were the focus of the misogyny meme detection shared task, part of the lt-edi@ldk 2025 workshop, in which we participated @xcite . to better detect misogynistic memes, we apply three main strategies: robust training strategy with optimization techniques: we use amp, gradient clipping, dropout, learning rate scheduling, and early stopping to improve convergence and model stability across training stages. detailed implementation information is available in the linked github repository belowhttps: //github.com/bountyhunter12/ misogyny-meme-detection. ## related work existing works of misogyny meme detection can be categorized into text-based, image-based, and multimodal approaches, using ml, dl, or transformer-based methods. in the past years, text-focused misogyny detection has been done in @xcite using statistical classification models, including variations of support vector machines (svm) @xcite . recently , multimodal learning has gained considerable attention in this field. starting with general harmful content detection @xcite , specific detection of misogynistic meme gained popularity later. cuervo and parde, 2022 utilized (clip)-like architectures, though their approach faced challenges due to ocr noise, dataset bias and unfair influence from certain words (e.g., "woman"), leading to false positives. recent advancements have explored more robust architectures for misogynistic meme detection. in chinivar et al., 2024 ,v-ltcs (vision-language transformer combination search) is one such framework that systematically combines various state-of-the-art vision (swin, convnext, vit) and language (charbert, alcharbert, xlm-r) transformer models to find the bestperforming multimodal pairs. among all these english contents, @xcite shifts the focus to tamil language. they have used mchar-bert and indiccharbert for text data, and vit, resnet, and efficientnet for image data. for multimodal detection, these are combined using concatenation. while research on english datasets has progressed, chinese harmful meme detection still needs further attention. the study @xcite addresses this by constructing the toxicn-mm dataset with fine-grained harmful type annotations. it proposes multimodal knowledge enhancement (mke), a baseline detector that integrates llm-generated context to improve classification. ## data we have utilized the dataset provided under the shared task on misogyny meme detection -lt-edi@ldk 2025 @xcite . the dataset has been segmented into training, development, and test sets containing 1,190, 170, and 340 samples, respectively. it primarily consists of codemixed chinese-english memes, the type of language commonly observed in online communication. the dataset consists of a significantly lower number of misogynistic memes compared to nonmisogynistic ones, as shown in ## results and analysis this section presents the outcomes of our misogyny meme classification task, comparing unimodal and multimodal approaches to assess their effectiveness in detecting image-text-based hate content. performance is evaluated using weighted precision (p), recall (r), and f1-score (f1), with macro-f1 serving as the primary metric for classification efficacy. ## conclusion in this study, we explored different ways to detect misogynistic memes using text, images, and a combination of both. by experimenting with various deep learning models like charbert for text and clip or vit for images, we found that combining both text and image features gives the best results. in particular, the model that used charbert + bilstm + clip with early fusion stood out by accurately detecting harmful content, achieving a macro f1 score of 0.70. we also used data augmentation techniques to handle the imbalance in the dataset, and tried to reduce errors where models misclassified subtle or sarcastic memes. despite these improvements, challenges still remain, particularly with overlapping categories and class imbalance. future work will focus on better data labeling and smarter model strategies to improve fairness and accuracy.