@inproceedings{ponnusamy-etal-2026-overview,
    title = "Overview of the Multimodal Homophobia and Transphobia Meme Classification Shared Task",
    author = "Ponnusamy, Kishore Kumar  and
      Chakravarthi, Bharathi Raja  and
      Kumaresan, Prasanna Kumar  and
      B, Premjith  and
      Durairaj, Thenmozhi  and
      Priyadharshini, Ruba  and
      Navaneethakrishnan, Subalalitha Chinnaudayar",
    editor = "Chakravarthi, Bharathi Raja  and
      B, Bharathi  and
      Buitelaar, Paul  and
      Thenmozhi, Durairaj  and
      Garc{\'i}a Cumbreras, Miguel {\'A}ngel  and
      Jim{\'e}nez Zafra, Salud Mar{\'i}a",
    booktitle = "Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion",
    month = jul,
    year = "2026",
    address = "Virtual (Online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.ltedi-1.13/",
    pages = "141--149",
    ISBN = "979-8-89176-424-8",
    abstract = "This paper presents an overview of the Shared Task on detecting homophobia and transphobia in meme datasets across three languages: Hindi, English, and Chinese. With the rapid growth of internet users worldwide, memes have become a widely used medium for expressing humor, satire, and sarcasm on social media platforms. However, their increasing popularity has also facilitated the spread of hate, misinformation, and propaganda targeting specific communities. Hateful memes often attack individuals or groups based on attributes such as physical appearance, language, ethnicity, religion, or sexual orientation. Among those affected, the LGBTQ+ community is particularly vulnerable and frequently targeted on social media platforms. To address this issue, we organized a shared task that focuses on identifying homophobic and transphobic hate in memes. The task aims to encourage the development of automated systems capable of detecting such harmful content across multiple languages. Evaluation was conducted using Macro F1-score as the primary metric. The top performing system achieved a Macro F1-score of 0.8377 for English, 0.8081 for Hindi, and 0.7535 for Chinese, demonstrating promising results for multilingual hate detection in memes."
}