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.

This paper offers a detailed overview of the first shared task on “Multitask Meme Classification - Unraveling Misogynistic and Trolls in Online Memes,” organized as part of the LT-EDI@EACL 2024 conference. The task was set to classify misogynistic content and troll memes within online platforms, focusing specifically on memes in Tamil and Malayalam languages. A total of 52 teams registered for the competition, with four submitting systems for the Tamil meme classification task and three for the Malayalam task. The outcomes of this shared task are significant, providing insights into the current state of misogynistic content in digital memes and highlighting the effectiveness of various computational approaches in identifying such detrimental content. The top-performing model got a macro F1 score of 0.73 in Tamil and 0.87 in Malayalam.