@inproceedings{dutta-etal-2026-cuet,
    title = "{CUET}-2567@{D}ravidian{L}ang{T}ech-{ACL} 2026: Multimodal Stance and Target Identification in {D}ravidian Political Memes",
    author = "Dutta, Arka  and
      Majumder, Anindya  and
      Faisal, Adnan  and
      Murad, Hasan",
    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-1.26/",
    pages = "196--200",
    ISBN = "979-8-89176-401-9",
    abstract = "In Dravidian languages, political memes progressively shape public opinion and political discourse, influencing digital conversations andpublic narratives. Our paper proposes a multilevel multimodal framework for political meme classification in Tamil and Malayalam as part of the Multi Level Political Meme ClassificationDravidianLangTech@ACL 2026 shared task. The task has involved two levels: Level 1 has identified whether a meme expresses Troll/Oppose or Support/Praise, while Level 2 has determined the specific target category (Individual, Party, or Intersection). We have evaluated unimodal and multimodal architectures to analyze the impact of textual and visual representation. Experimental results have highlighted the importance of a multimodal approach over unimodal approaches. This workconfirms the effectiveness of combining image and text features in meme understanding. Among the evaluated models, the mBERT+ViTarchitecture has achieved the best overall performance across both languages and classification levels. According to the evaluation of shared task we achieved average F1 score of 0.72 securing the 2nd rank in Malayalam task and F1 score of 0.76 in Tamil task securing the 6th rank. However after our experimental evaluation we got best average F1 score of 0.62 for Tamil and 0.49 for Malayalam. Despite moderate results, challenges have remained mainly due to the dataset size, class imbalance, and noisy text extraction from images."
}