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Document Topic Alignment Metrics for Evaluating Topic Models of Short-Text Public Health Communications on Social Media

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Topic models are widely used to analyze public health-related social media short texts, yet their evaluation remains dominated by metrics that focus entirely on generated topics alone. There is a lack of metrics that quantitatively assess whether assigned topics meaningfully represent the corresponding short-text posts. We propose Document-Topic Alignment metrics (DoTA), an assignment-aware evaluation framework comprising metrics that measure semantic alignment between documents (posts) and their assigned topics. We also introduce margin-based and discriminative variants that capture topic ass

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