This paper is in the following e-collection/theme issue:

Public (e)Health, Digital Epidemiology and Public Health Informatics (1022) LGBTQ Issues (544) Mass Media/Social Media Communication and Campaigns (578) Social Media in Public Health informatics (67) Machine Learning (2095)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/72822, first published .
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

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

Authors of this article:

Thu T Nguyen1 Author Orcid Image ;   Xiaohe Yue1 Author Orcid Image ;   Heran Mane1 Author Orcid Image ;   Kyle Seelman2 Author Orcid Image ;   Penchala Sai Priya Mullaputi1 Author Orcid Image ;   Elizabeth Dennard1 Author Orcid Image ;   Amrutha S Alibilli1 Author Orcid Image ;   Junaid S Merchant1 Author Orcid Image ;   Shaniece Criss3 Author Orcid Image ;   Yulin Hswen4 Author Orcid Image ;   Quynh C Nguyen1 Author Orcid Image
Article Authors Cited by (1) Tweetations Metrics

Journals

  1. Agrawal M, Deshmukh P. Tri-dimensional perspectives in hate speech detection: a review of multilingual, multimodal and fairness-aware approaches. Discover Applied Sciences 2025 View