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<p>This paper presents a supervised multi-label framework for detecting multidimensional perceived risk in HIV-related Reddit discourse. A longitudinal corpus of 329,707 texts collected from r/hivaids and r/HIV between 2015 and 2025 was analyzed to identify three risk dimensions: transmission risk, health deterioration risk, and social stigma risk. A stratified sample of 2,000 texts was annotated by domain experts, achieving substantial inter-annotator agreement (Cohen’s κ = 0.74–0.81). A RoBERTa-base model was fine-tuned using class-weighted binary cross-entropy loss and per-class threshold optimization. The proposed model achieved a macro-F1 score of 0.87 and a macro-AUC-ROC of 0.97, outperforming 12 baseline models, including traditional machine learning, neural network, and alternative transformer-based approaches. Ablation experiments confirmed the importance of transformer fine-tuning and class weighting, while also showing that handcrafted features provided only marginal gains. Applied to the full corpus, the model revealed significant upward trends in transmission risk and health deterioration risk, strong co-occurrence between transmission and stigma-related discourse, and distinct information-seeking patterns across risk categories. The findings demonstrate that transformer-based multi-label learning can support scalable, reproducible analysis of HIV-related health perceptions in online communities, with potential applications in public health surveillance, communication strategy design, and digital intervention planning.</p>

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Modeling multidimensional perceived risk in HIV-related social media: a multi-label transformers framework with longitudinal analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月11日

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  • Abdollah Abadian,
  • Abdullah,
  • Zulaikha Fatima,
  • Carlos Guzmán Sánchez Mejorada,
  • Miguel Torres-Ruiz,
  • Rolando Quintero Téllez

摘要

This paper presents a supervised multi-label framework for detecting multidimensional perceived risk in HIV-related Reddit discourse. A longitudinal corpus of 329,707 texts collected from r/hivaids and r/HIV between 2015 and 2025 was analyzed to identify three risk dimensions: transmission risk, health deterioration risk, and social stigma risk. A stratified sample of 2,000 texts was annotated by domain experts, achieving substantial inter-annotator agreement (Cohen’s κ = 0.74–0.81). A RoBERTa-base model was fine-tuned using class-weighted binary cross-entropy loss and per-class threshold optimization. The proposed model achieved a macro-F1 score of 0.87 and a macro-AUC-ROC of 0.97, outperforming 12 baseline models, including traditional machine learning, neural network, and alternative transformer-based approaches. Ablation experiments confirmed the importance of transformer fine-tuning and class weighting, while also showing that handcrafted features provided only marginal gains. Applied to the full corpus, the model revealed significant upward trends in transmission risk and health deterioration risk, strong co-occurrence between transmission and stigma-related discourse, and distinct information-seeking patterns across risk categories. The findings demonstrate that transformer-based multi-label learning can support scalable, reproducible analysis of HIV-related health perceptions in online communities, with potential applications in public health surveillance, communication strategy design, and digital intervention planning.