*Homophobia and Transphobia Meme Classification | LT-EDI @ ACL 2026*
We are pleased to invite the research community to participate in the
LT-EDI @ ACL 2026 shared task on Homophobia and Transphobia Meme
Classification, which addresses harmful multimodal content targeting LGBTQ+
individuals and communities.

Memes function as compact multimodal communication units that combine
visual and textual cues. They spread rapidly across cultures and languages.
This combination enables both subtle and explicit forms of discrimination.
The shared task focuses on the automatic identification of homophobic and
transphobic content in memes.


* 📝 Task Description*The shared task focuses on multiclass meme
classification for detecting anti-LGBT content. Participants are provided
with multimodal memes and are required to classify each meme into one of
the predefined categories based on the presence of discriminatory content.


*Labels:*Homophobia
Transphobia
Non-anti-LGBT


*Languages:*English, Hindi, and Chinese


*Description:*Separate datasets are released for each language, enabling
analysis across culturally distinct meme collections. The task requires
participants to identify discriminatory stereotypes, harmful visual
elements, and derogatory textual cues embedded in memes. All training and
test datasets are developed following culturally sensitive and ethical
annotation practices. The task emphasizes robust multimodal understanding
across diverse cultural contexts.



*  📚 Resources*
🔗 Competition link (Codabench):
https://www.codabench.org/competitions/11335/
🔗 Task website: https://sites.google.com/view/lt-edi-2026/shared-tasks


*🗓️ Important Dates*Task announcement: November 16, 2025
Training data release: November 25, 2025
Test data release: January 20, 2026
Run submission deadline: February 10, 2026
Results announcement: February 16, 2026
Paper submission deadline: March 5, 2026
Peer review notification: April 28, 2026
Camera-ready submission: May 12, 2026
Workshop dates: July 2–3, 2026


with regards,
Dr. Bharathi Raja Chakravarthi,
Assistant Professor / Lecturer-above-the-bar
Programme Director (MSc Computer Science - Artificial Intelligence)
<https://www.universityofgalway.ie/courses/taught-postgraduate-courses/computer-science-artificial-intelligence.html>
School of Computer Science, University of Galway, Ireland
Insight SFI Research Centre for Data Analytics, Data Science Institute,
University of Galway, Ireland
E-mail: [email protected] , [email protected]
<[email protected]>
Google Scholar: https://scholar.google.com/citations?user=irCl028AAAAJ&hl=en
Website:
https://research.universityofgalway.ie/en/persons/bharathi-raja-asoka-chakravarthi
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