DUCK 謠言檢測《DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks》( 三 )
$h_{p}=\operatorname{BERT}\left(\operatorname{emb}\left([C L S], c_{p}\right)\right)$
其中，$h$ 將用作 GAT 中的初始節點表示（$h^{(0)}$） 。這里報告了這個替代模型（“unpaired”）及不同的聚合方法（“root”、“?root”、“$\bigtriangleup $” 和 “all”）的性能 。
文章插圖
Comparing the aggregation methods, "all" performs the best, followed by "$\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 繪制了我們改變所包含的評論數量來回答 Q2 的結果：
文章插圖
4.2.3 User Tree
文章插圖
4.2.4 Overall Rumour Detection Performance
文章插圖
【DUCK 謠言檢測《DUCK: Rumour Detection on Social Media by Modelling User and Comment Propagation Networks》】
推薦閱讀
- RDEA 謠言檢測《Rumor Detection on Social Media with Event Augmentations》
- 謠言檢測《Data Fusion Oriented Graph Convolution Network Model for Rumor Detection》
- ClaHi-GAT 謠言檢測《Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks》
- PLAN 謠言檢測——《Interpretable Rumor Detection in Microblogs by Attending to User Interactions》
- 謠言檢測——《Debunking Rumors on Twitter with Tree Transformer》
- 如何使用 Yolov4 訓練人臉口罩檢測模型
- 自身 如何在linux下檢測IP沖突
- 華為watch3pro支持血糖檢測嗎_華為watch3pro有測血糖功能嗎
- Notebook交互式完成目標檢測任務
- PSA 謠言檢測——《Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks》