@inproceedings{kushwaha-etal-2026-curiousvectors,
    title = "{C}urious{V}ectors@{LT}-{EDI} 2026: Detection of Homophobic and Transphobic Memes on Social Media Using a Hybrid Multimodal Approach",
    author = "Kushwaha, Saloni  and
      Bandyopadhyay, Jishnu  and
      Sharma, Deepawali  and
      Singh, Aakash",
    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-1.15/",
    pages = "155--160",
    ISBN = "979-8-89176-424-8",
    abstract = "The rapid growth of social media has also led to a rise in abusive and harmful content, which negatively affects the online environment for users. The frequent use of offensive language and hate speech contributes to making these platforms increasingly hostile. In particular, homophobic and transphobic remarks target members of the LGBT+ community. Detecting such comments is therefore essential so that they can be flagged promptly and appropriate warnings can be given to users involved in such behaviour. The problem becomes more serious when such content appears in other forms of communication used by younger generations, such as memes. This work tries to address this issue. We propose a method to detect such content using the meme dataset from the LT-EDI 2026 challenge and secured 8th rank for English and 6th rank for Chinese language dataset in the shared task. Our approach uses a multimodal technique that processes both image and text information. The dataset has limited data, which creates a challenge. To handle this, we pre{--}fine-tune the models on a similar dataset called PrideMM. The proposed multimodal approach achieved Macro F1-scores of 0.24 and 0.57 for English and Chinese memes respectively."
}