@inproceedings{rajiakodi-etal-2026-overview,
    title = "Overview of the Shared Task on Multilevel Political Meme Classification in {T}amil and {M}alayalam",
    author = "Rajiakodi, Saranya  and
      Chinnan, Shunmuga Priya Muthusamy  and
      B, Premjith  and
      CN, Subalalitha  and
      Ponnusamy, Rahul  and
      A, Anshid K  and
      Sivagnanam, Bhuvaneswari  and
      Chakravarthi, Bharathi Raja",
    editor = "Chakravarthi, Bharathi Raja  and
      Priyadharshini, Ruba  and
      Madasamy, Anand Kumar  and
      Thavareesan, Sajeetha  and
      Rajiakodi, Saranya  and
      Navaneethakrishnan, Subalalitha  and
      Chinnappa, Dhivya  and
      Palani, Balasubramanian  and
      Subramanian, Malliga  and
      Shanmugavadivel, Kogilavani  and
      Rajalakshmi, Ratnavel",
    booktitle = "Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for {D}ravidian Languages",
    month = jul,
    year = "2026",
    address = "Underline (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.dravidianlangtech-1.12/",
    pages = "101--113",
    ISBN = "979-8-89176-401-9",
    abstract = "This paper presents an overview of the Multi-Level Political Meme Classification shared task conducted at DravidianLangTech{--}ACL 2026. The task introduces a hierarchical two-level classification framework for Tamil and Malayalam political memes: Level 1 focuses on stance detection (Support/Praise vs. Troll/Oppose), while Level 2 identifies the political target (individual or party), conditioned on the predicted stance. The dataset was curated from social media platforms and manually annotated with strong inter-annotator agreement. A total of 64 teams registered and 19 teams submitted their results using diverse multimodal approaches combining transformer-based text encoders, vision models, OCR pipelines, and hierarchical architectures. Results show that stance detection achieves high macro-F1 scores across both languages, whereas target identification remains more challenging, particularly in Malayalam. The findings highlight the importance of multimodal fusion, hierarchical reasoning, and robustness to OCR noise and class imbalance in political meme analysis."
}