The rapid expansion of social media has facilitated communication but also enabled the spread of misogynistic memes, reinforcing gender stereotypes and toxic online environments. Detecting such content is challenging due to the multimodal nature of memes, where meaning emerges from the interplay of text and images. The Misogyny Meme Detection shared task at DravidianLangTech@NAACL 2025 focused on Tamil and Malayalam, encouraging the development of multimodal approaches. With 114 teams registered and 23 submitting predictions, participants leveraged various pretrained language models and vision models through fusion techniques. The best models achieved high macro F1 scores (0.83682 for Tamil, 0.87631 for Malayalam), highlighting the effectiveness of multimodal learning. Despite these advances, challenges such as bias in the data set, class imbalance, and cultural variations persist. Future research should refine multimodal detection methods to improve accuracy and adaptability, fostering safer and more inclusive online spaces.

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.

In this digital era, memes have become a prevalent online expression, humor, sarcasm, and social commentary. However, beneath their surface lies concerning issues such as the propagation of misogyny, gender-based bias, and harmful stereotypes. To overcome these issues, we introduced MDMD (Misogyny Detection Meme Dataset) in this paper. This article focuses on creating an annotated dataset with detailed annotation guidelines to delve into online misogyny within the Tamil and Malayalam-speaking communities. Through analyzing memes, we uncover the intricate world of gender bias and stereotypes in these communities, shedding light on their manifestations and impact. This dataset, along with its comprehensive annotation guidelines, is a valuable resource for understanding the prevalence, origins, and manifestations of misogyny in various contexts, aiding researchers, policymakers, and organizations in developing effective strategies to combat gender-based discrimination and promote equality and inclusivity. It enables a deeper understanding of the issue and provides insights that can inform strategies for cultivating a more equitable and secure online environment. This work represents a crucial step in raising awareness and addressing gender-based discrimination in the digital space.