CUET _ I gnite@ LT - EDI -2025: A Multimodal Transformer-Based Approach for Detecting Misogynistic Memes in C hinese Social Media (2025)

Abstract: Misogynistic content in memes on social me dia platforms poses a significant challenge for content moderation, particularly in languages like Chinese, where cultural nuances and multi modal elements complicate detection. Address ing this issue is critical for creating safer online environments, A shared task on multimodal misogyny identification in Chinese memes, or ganized by LT-EDI@LDK 2025, provided a curated dataset for this purpose. Since memes mix pictures and words, we used two smart tools: ResNet-50 to understand the images and Chinese RoBERTa to make sense of the text. The data set consisted of Chinese social media memes annotated with binary labels (Misogynistic and Non-Misogynistic), capturing explicit misogyny, implicit biases, and stereo types. Our experiments demonstrated that ResNet-50 combined with Chinese RoBERTa achieved a macro F1 score of 0.91, placing second in the competition and underscoring its effectiveness in handling the complex interplay of text and visuals in Chinese memes. This research advances multimodal misogyny detection and contributes to natural language and vision processing for low-resource languages, particularly in combating gender-based abuse online.

## introduction misogynistic content fuels hostility and discrimination, particularly targeting women, and poses a significant barrier to fostering safe and inclusive online spaces. these memes flooding chinese social media platforms like weibo aren't just harmless jokes-they're digital barbs that mock women, blending snarky text with images to spread hostility @xcite . detecting misogyny in these multimodal formats is complex, as the intent hinges on the interplay between visual and textual elements @xcite . subtle misogyny can dodge automated tools, or worse, those tools might flag innocent posts by mistake @xcite . this is not just a technological problem, it is a social one, as these memes shape attitudes and amplify harm. the misogynistic meme detection shared task at lt-edi@2025 took aim at this, challenging teams to spot harmful memes in chinese social media with precision. our team, cuet_ignite, participated in the lisn 2025 co-dalab competition to wrestle with these issues. we set out to build a system that could handle the tricky interplay of images and chinese text. our key contributions include the following: inspired by @xcite , which tackled abusive tamil text with transformers, we pushed their ideas into the multimodal world of chinese memes. this is our working way of making the internet less toxic. for more details, our code is available at https://github.com/mhd094/chinesemisogyny-meme-detection . ## related work social media is packed with harmful content such as misogyny and hate speech. in recent years, nlp researchers have been working on spotting trolled, hostility, and abusive content on social media. the early work was mostly about text alone @xcite . @xcite showed that hate speech tools struggle with different hate types, so misogyny needs its own focus. now, researchers are working on memes that mix text and images, making things trickier. recent research has investigated multimodal approaches to boost classification performance. for example, @xcite developed a method to label tamil and malayalam memes as "misogynistic" or "non-misogynistic" using multinomial naive bayes, merging results with weighted probabilities. @xcite used a clip model to see how text and pictures work together in misogynistic memes. @xcite studied tamil and malayalam memes, pairing mbert or muril with resnet-50, hitting f1-scores of 0.73 and 0.87. @xcite built a perceiver io system, blending vit for images and roberta for text, which performed well at catching misogyny in memes. @xcite launched multibullyex, a dataset for cyberbullying memes in mixed languages. a contrastive language-image pretraining (clip) projection based multimodal sharedprivate multitask approach has been proposed there for visual and textual explanation of a meme. @xcite shared mimosa, with 4,848 bengali memes, using a fusion method to sort aggression. @xcite introduced multi3hate, a multilingual meme dataset capturing cultural variability in hate interpretation, and evaluated several vision-language models on this task. similarly, @xcite proposed hate-clipper, which achieved state-of-the-art results by modeling cross-modal interactions between clip-encoded image and text features. these efforts show how hard it is to catch harmful memes in different languages and cultures, especially with subtle humor or jabs. our work at cuet_ignite@lt- @xcite stands out as we used resnet-50 and chinese roberta to nab misogynistic chinese memes, hitting an f1-score of 0.91. we addressed chinese slang, idioms, and cultural vibes, making our model relevant for china's social media and helping to keep online spaces safer. ## task and dataset description the pervasive spread of harmful content on social media, especially misogynistic material, has become increasingly common often hidden within memes that combine both text and images. these memes can reinforce negative stereotypes and promote gender-based hate speech. this work focuses on building automated systems that detect misogynistic memes by jointly analyzing visual and textual information, specifically in chinese language memes. it is a multimodal classification task, where each meme must be categorized as either: misogynistic: memes that contain content demeaning, targeting, or offending women. non-misogynistic: memes without harmful or offensive intent toward women. the dataset requires analyzing both the image and the accompanying chinese text, making the task challenging in the fields of natural language processing and computer vision. it also contributes toward advancing multimodal and multilingual ai systems for hate speech detection. this dataset builds upon the mdmd (misogyny detection meme dataset) originally introduced by @xcite , which focused on tamil and malayalam memes. the present dataset extends their methodology and annotation guidelines to chinese social media content. a detailed overview of dataset design and objectives is also provided in @xcite . to ensure consistency with the original task, the same annotation schema was adopted and adapted for chinese memes. the dataset shows moderate imbalance, with 500 misogynistic and 1,200 non-misogynistic samples across all subsets. a total of 1200 memes are included: 1190 for training, 170 for development, and 340 for testing. ## methodology the objective of this study is to detect misogynistic content in multimodal chinese memes by integrating visual and textual features. our approach begins with preprocessing the memes, followed by feature extraction from both modalities. the features are then fused using an attention-based mechanism, and a classifier predicts whether the meme is misogynistic or non-misogynistic. figure 2 provides a visualization of our methodology. ## results & discussion this section presents a comparative performance analysis of various experimental approaches for classifying chinese memes as misogynistic or nonmisogynistic. the effectiveness is primarily assessed based on the weighted f1-score, while precision and recall are also considered in some cases. ## conclusion this work presented the details of the methods and performance analysis of the models for detecting misogynistic memes in chinese, exploring visual, textual, and multimodal fusion techniques. the results revealed that the chinese-robertawwm-ext + resnet-50 model achieved the highest f1-score of 0.91, demonstrating that multimodal fusion with attention mechanisms significantly enhances model performance. the attention-based fusion effectively captured cross-modal interactions, leading to improved precision and recall compared to unimodal approaches. in the future, we plan to explore advanced fusion strategies, such as crossattention or graph-based methods, and extend the dataset to include more diverse meme content for better robustness, especially in handling chinese internet slang and culturally specific references. adding chinese cultural knowledge and reducing model biases enhances adaptability, fairness, and generalization.