← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-srw.47 · 记录 #1
Leakage-Aware User-Level ADHD Signal Classification from Social Media: When Graph Aggregation Helps, and When It Does Not
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
User-level ADHD-related text classification from social media is methodologically challenging because predictions must aggregate many short posts, performance can be inflated by direct diagnostic leakage, and screening-adjacent settings require calibrated probabilities rather than discrimination alone. We introduce a leakage-aware evaluation framework organized around two controlled axes: evidence budget, i.e., the number of tweets available per user, and leakage control. Within this setup, we compare document-level transformers, strong non-graph embedding-pooling baselines, and heterogeneous graph models combining semantic tweet embeddings, psycholinguistic features, and temporal structure. The main result is regime-dependent: graph aggregation is most useful when user evidence is scarce, whereas simple embedding pooling becomes highly competitive and often slightly stronger as more evidence becomes available. Overall, the main contribution is a controlled benchmarking framework and a clearer account of when graph-based aggregation is actually beneficial.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026-07 · 最新观测版本
Leakage-Aware User-Level ADHD Signal Classification from Social Media: When Graph Aggregation Helps, and When It Does Not
User-level ADHD-related text classification from social media is methodologically challenging because predictions must aggregate many short posts, performance can be inflated by direct diagnostic leakage, and screening-adjacent settings require calibrated probabilities rather than discrimination alone. We introduce a leakage-aware evaluation framework organized around two controlled axes: evidence budget, i.e., the number of tweets available per user, and leakage control. Within this setup, we compare document-level transformers, strong non-graph embedding-pooling baselines, and heterogeneous graph models combining semantic tweet embeddings, psycholinguistic features, and temporal structure. The main result is regime-dependent: graph aggregation is most useful when user evidence is scarce, whereas simple embedding pooling becomes highly competitive and often slightly stronger as more evidence becomes available. Overall, the main contribution is a controlled benchmarking framework and a clearer account of when graph-based aggregation is actually beneficial.
查看此版本解读 →查看此版本原文 ↗