DUCK 谣言检测《DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks》( 三 ) _生活百科

Q3 [User tree]: Can social relations help modelling the user network?

Q4 [Overall performance]: Do the three different components complement each other and how does a combined approach compared to existing rumour detection systems?

4.2.1 Comment Tree为了理解使用BERT处理一对 parent-child posts 的影响，我们提出了另一种替代方法（“unpaired”），即使用 BERT 独立处理每个帖子，然后将其 [CLS] 表示提供给GAT 。
$h_{p}=\operatorname{BERT}\left(\operatorname{emb}\left([C L S], c_{p}\right)\right)$
其中，$h$ 将用作 GAT 中的初始节点表示（$h^{(0)}$）。这里报告了这个替代模型（“unpaired”）及不同的聚合方法（“root”、“?root”、“$\bigtriangleup $” 和 “all”）的性能。

DUCK 谣言检测《DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks》

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Comparing the aggregation methods, "all" performs the best, followed by "$\boldsymbol{\Delta}$ " and "root" (0.88 vs . 0.87 vs. 0.86 in Twitter16; 0.87 vs. 0.86 vs. 0.85 in CoAID in terms of Macro-F1). We can see that the root and its immediate neighbours contain most of the information, and not including the root node impacts the performance severely (both Twitter16 and CoAID drops to 0.80 with $\neg$ root).
Does processing the parent-child posts together with BERT help? The answer is evidently yes, as we see a substantial drop in performance when we process the posts independently: "unpaired" produces a macro-F1 of only 0.83 in both Twitter16 and CoAID. Given these results, our full model (DUCK) will be using "all"' as the aggregation method for computing the comment graph representation.
4.2.2 Comment ChainFig. 3 绘制了我们改变所包含的评论数量来回答 Q2 的结果：