This overview paper presents the findings of the Shared Task on Abusive Tamil and Malayalam Text Targeting Women on Social Media, organized as part of DravidianLangTech@NAACL 2025. The task aimed to encourage the development of robust systems to detectabusive content targeting women in Tamil and Malayalam, two low-resource Dravidian languages. Participants were provided with annotated datasets containing abusive and nonabusive text curated from YouTube comments. We present an overview of the approaches and analyse the results of the shared task submissions. We believe the findings presented in this paper will be useful to researchers working in Dravidian language technology.

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.

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.

We present an overview of the first shared task on “Caste and Migration Hate Speech Detection.” The shared task is organized as part of LTEDI@EACL 2024. The system must delineate between binary outcomes, ascertaining whether the text is categorized as a caste/migration hate speech or not. The dataset presented in this shared task is in Tamil, which is one of the under-resource languages. There are a total of 51 teams participated in this task. Among them, 15 teams submitted their research results for the task. To the best of our knowledge, this is the first time the shared task has been conducted on textual hate speech detection concerning caste and migration. In this study, we have conducted a systematic analysis and detailed presentation of all the contributions of the participants as well as the statistics of the dataset, which is the social media comments in Tamil language to detect hate speech. It also further goes into the details of a comprehensive analysis of the participants’ methodology and their findings.