@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Overview of the Shared Task on Multilevel Political Meme Classification in Tamil and Malayalam
%A Rajiakodi, Saranya
%A Chinnan, Shunmuga Priya Muthusamy
%A B, Premjith
%A CN, Subalalitha
%A Ponnusamy, Rahul
%A A, Anshid K.
%A Sivagnanam, Bhuvaneswari
%A Chakravarthi, Bharathi Raja
%Y Chakravarthi, Bharathi Raja
%Y Priyadharshini, Ruba
%Y Madasamy, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Rajiakodi, Saranya
%Y Navaneethakrishnan, Subalalitha
%Y Chinnappa, Dhivya
%Y Palani, Balasubramanian
%Y Subramanian, Malliga
%Y Shanmugavadivel, Kogilavani
%Y Rajalakshmi, Ratnavel
%S Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages
%D 2026
%8 July
%I Association for Computational Linguistics
%C Underline (Virtual)
%@ 979-8-89176-401-9
%F rajiakodi-etal-2026-overview
%X 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.
%U https://aclanthology.org/2026.dravidianlangtech-1.12/
%P 101-113
Markdown (Informal)
[Overview of the Shared Task on Multilevel Political Meme Classification in Tamil and Malayalam](https://aclanthology.org/2026.dravidianlangtech-1.12/) (Rajiakodi et al., DravidianLangTech 2026)
ACL
- Saranya Rajiakodi, Shunmuga Priya Muthusamy Chinnan, Premjith B, Subalalitha CN, Rahul Ponnusamy, Anshid K A, Bhuvaneswari Sivagnanam, and Bharathi Raja Chakravarthi. 2026. Overview of the Shared Task on Multilevel Political Meme Classification in Tamil and Malayalam. In Proceedings of the Sixth Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, pages 101–113, Underline (Virtual). Association for Computational Linguistics.