通过用户关系图谱理解社区语境，提升在线社交内容检测效果。
GASTON: Graph-Aware Social Transformer for Online Networks
- 基于用户互动模式预训练社区嵌入，捕捉本地社交规范。
- 在压力识别、毒性评分等任务上优于现有方法。
- 适合研究社交网络内容安全与社区行为建模的学者。
在线社区已成为社交与支持的重要场所，但也存在毒性、回音室和虚假信息等问题。内容危害性检测困难，因在线互动的意义既来自文本内容，也取决于其所在的社交规范。本文提出GASTON（Graph-Aware Social Transformer for Online Networks），通过学习受本地社交规范约束的文本与用户嵌入，为下游任务提供必要上下文。核心是对比初始化策略，基于用户成员关系预训练社区嵌入，捕捉社区用户构成特征，从而在不依赖词汇相似性的前提下区分不同社区（如支持群组与仇恨群组）。在压力检测、毒性评分及规范违反等任务上的实验表明，GASTON生成的嵌入显著优于当前最优基线模型。
原文摘要 · Abstract (English)
Online communities have become essential places for socialization and support, yet they also possess toxicity, echo chambers, and misinformation. Detecting this harmful content is difficult because the meaning of an online interaction stems from both what is written (textual content) and where it is posted (social norms). We propose GASTON (Graph-Aware Social Transformer for Online Networks), which learns text and user embeddings that are grounded in their local norms, providing the necessary context for downstream tasks. The heart of our solution is a contrastive initialization strategy that pretrains community embeddings based on user membership patterns, capturing a community's user base before processing any text. This allows GASTON to distinguish between communities (e.g., a support group vs. a hate group) based on who interacts there, even if they share similar vocabulary. Experiments on tasks such as stress detection, toxicity scoring, and norm violation demonstrate that the embeddings produced by GASTON outperform state-of-the-art baselines.
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