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Decoding Digital Discourse Through Multimodal Text and Image Machine Learning Models to Classify Sentiment and Detect Hate Speech in Race- and Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, and Asexual Community–Related Posts on Social Media: Quantitative Study

Decoding Digital Discourse Through Multimodal Text and Image Machine Learning Models to Classify Sentiment and Detect Hate Speech in Race- and Lesbian, Gay, Bisexual, Transgender, Queer, Intersex, and Asexual Community–Related Posts on Social Media: Quantitative Study

In response, the Hate-CLIPper model by Kumar and Nandakumar [30] improved categorization by capturing the interactions between picture and word embeddings using a feature interaction matrix and Contrastive Language-Image Pretraining (CLIP; Open AI) features that better comprehend subtle contextual clues, such as sarcasm.

Thu T Nguyen, Xiaohe Yue, Heran Mane, Kyle Seelman, Penchala Sai Priya Mullaputi, Elizabeth Dennard, Amrutha S Alibilli, Junaid S Merchant, Shaniece Criss, Yulin Hswen, Quynh C Nguyen

J Med Internet Res 2025;27:e72822

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