构建首个融合互动结构与语义的争议性对话分析基准，助力研究网络极化。
ControBench: An Interaction-Aware Benchmark for Controversial Discourse Analysis on Social Networks
- 融合用户-帖子-评论的异构图与丰富文本语义，保留论点上下文。
- 包含7370名用户、1783篇帖子、26525次互动，跨议题同质性低（如特朗普话题-0.77）。
- 适合研究极化、虚假信息与内容治理的学者及平台算法团队使用。
理解人们在网络意识形态分歧中如何辩论，对研究政治极化、虚假信息和内容审核至关重要。现有数据集仅部分覆盖该问题：一些仅保留文本而忽略互动结构，一些建模结构但缺乏丰富语义，另一些虽呈现对话却无稳定用户意识形态标识。我们提出ControBench，一个结合异构社交互动图与丰富文本语义的争议性对话分析基准。基于Reddit上关于特朗普、堕胎和宗教三个话题的讨论构建，包含7,370名用户、1,783篇帖子和26,525次互动。图中包含用户与帖子节点，通过语义增强边连接；特别地，用户-评论-用户边既表示回复关系，也记录所回应的父评论，保留局部论点上下文。用户标签源自其自报的Reddit旗帜（flairs），提供无需人工标注的可扩展意识形态代理。结果数据集在三个话题上的调整同质性分别为：特朗普（-0.77）、堕胎（0.06）、宗教（0.04），体现真实辩论的交叉结构。我们在ControBench上评估图神经网络、预训练语言模型和大语言模型，发现不同话题与模型类型间性能差异显著，尤其在意识形态边界模糊时。这些结果表明ControBench是具有挑战性且真实的争议性对话分析基准。
原文摘要 · Abstract (English)
Understanding how people argue across ideological divides online is important for studying political polarization, misinformation, and content moderation. Existing datasets capture only part of this problem: some preserve text but ignore interaction structure, some model structure without rich semantics, and others represent conversations without stable user-level ideological identity. We introduce ControBench, a benchmark for controversial discourse analysis that combines heterogeneous social interaction graphs with rich textual semantics. Built from Reddit discussions on three topics, Trump, abortion, and religion, ControBench contains 7,370 users, 1,783 posts, and 26,525 interactions. The graph contains user and post nodes connected by semantically enriched edges; in particular, user-comment-user edges encode both a reply and the parent comment that it responds to, preserving local argumentative context. User labels are derived from self-declared Reddit flairs, providing a scalable proxy for ideological identity without manual annotation. The resulting datasets exhibit low or negative adjusted homophily (Trump: -0.77, Abortion: 0.06, Religion: 0.04), reflecting the cross-cutting structure of real-world debate. We evaluate graph neural networks, pretrained language models, and large language models on ControBench and observe distinct performance patterns across topics and model families, especially when ideological boundaries are ambiguous. These results position ControBench as a challenging and realistic benchmark for controversial discourse analysis.
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