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Overview of the Multimodal Homophobia and Transphobia Meme Classification Shared Task

Paper · 2026 · Kishore Kumar Ponnusamy, Bharathi Raja Chakravarthi, Mahesh Susaladi et al.

Summary

This overview describes a shared task on recognising anti-LGBTQ+ hate in memes. Memes carry meaning through the interplay of picture and caption, and research on homophobia and transphobia had so far focused mostly on plain-text comments. The organisers released a new multimodal benchmark in English, Hindi and Chinese. Each meme is labelled Homophobia, Transphobia or Non-LGBT. Content was gathered through hashtag searches on Instagram, X, Facebook, Reddit and Pinterest. The data was then expanded with synthetic memes built through the Imgflip API. For Hindi, captions came from an existing hate-speech collection and were paired by hand with templates whose mood fit the remark. Systems were ranked by Macro F1. Most submissions combined OCR-extracted text with visual features, using CLIP-style encoders, VLM prompting, or gated fusion. The top-performing systems achieved 0.8377 on the English track, while Hindi peaked at 0.8081 and Chinese at 0.7535. Image-only models lagged far behind. The work matters because it links multimodal, multilingual and LGBTQ+-focused hate detection, an intersection that previous research had left largely unexplored.

Key points

  • Train/test split per Table 1 (as extracted from the PDF):
    • English: 560/141
    • Hindi: 798/200
    • Chinese: 956/239
  • Class balance varies strongly by language. Chinese has very few Transphobia examples.
  • Nine teams appear in the system table:
    • BiasBreakers, MemeSentinel, MemeScouts, EthosAI, CuriousVectors
    • SAJI, SigJBS, susmitha, DLRG
  • MemeScouts used prompted weak supervision. A VLM answered 89 structured yes/no or either/or questions per meme, and those answers fed a Random Forest classifier.
  • Chinese track results:
    • The top system reached 0.7535.
    • MemeScouts placed second (0.7527) and susmitha third (0.7371).
    • The image-only DLRG system reached just 0.2993.
  • The authors list several limitations: a relatively small dataset, possible platform bias, and cultural and sarcastic ambiguity that even human annotators find hard.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Ollscoil na Gaillimhe – University of GalwayGalway · Ireland23 works · 2,229 citations🎓Amrita Vishwa VidyapeethamCoimbatore · India7 works · 31 citations🎓Kerala University of Digital Sciences, Innovation and TechnologyThiruvananthapuram · India2 works · 4 citations

Also on the byline: SRM Institute of Science and Technology · Gandhigram Rural Institute.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksPsychology · 1,552 worksSocial Psychology · 306 works

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1,220 worksVision & language · 657 works

Cite

Cite as Kishore Kumar Ponnusamy, Bharathi Raja Chakravarthi, Mahesh Susaladi, Junru Ren, Prasanna Kumar Kumaresan, Premjith B, Durairaj Thenmozhi, Ruba Priyadharshini and Subalalitha Chinnaudayar Navaneethakrishnan. 2026. Overview of the Multimodal Homophobia and Transphobia Meme Classification Shared Task. Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.ltedi-1.13

@inproceedings{ponnusamy2026overview,
  title = {{Overview of the Multimodal Homophobia and Transphobia Meme Classification Shared Task}},
  author = {Kishore Kumar Ponnusamy, Bharathi Raja Chakravarthi, Mahesh Susaladi, Junru Ren, Prasanna Kumar Kumaresan, Premjith B, Durairaj Thenmozhi, Ruba Priyadharshini and Subalalitha Chinnaudayar Navaneethakrishnan},
  booktitle = {Proceedings of the Sixth Workshop on Language Technology for Equality, Diversity, Inclusion},
  year = {2026},
  doi = {10.18653/v1/2026.ltedi-1.13},
  url = {https://doi.org/10.18653/v1/2026.ltedi-1.13}
}

References

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