This document presents a novel Claim-guided Hierarchical Graph Attention Network (ClaHi-GAT) model for rumor detection using undirected interaction graphs. The model uses multi-level attention - post-level attention considers the content of individual tweets, while event-level attention compares tweets responding to the same claim. This allows the model to better capture features indicative of rumors. Experimental results on three Twitter datasets show the proposed model achieves superior performance for rumor classification and early detection compared to previous structure-based methods.

1. Joo-Ho Lee
School of Computer Science and Information Engineering,
The Catholic University of Korea
E-mail: jooho414@gmail.com
2023-07-24

2. 1
Introduction
Problem Statement
• Social psychology literature defines a rumor as a story or a statement whose truth value is unverified or
intentionally false
• Rumor detection aims to determine the veracity of a given story or statement
• For automating rumor detection, previous studies focus on text mining from sequential microblog streams with
supervised classifiers based on feature engineering and feature learning

3. 2
Introduction
Problem Statement
• The structure-based methods however represent the conversation as a directed tree structure, following the
bottom-up or top-down information flows
• But such kind of structure, considering directed responsive relation, cannot enhance the representation
learning of each tweet by aggregating information in parallel from the other informative tweets

4. 3
Introduction
Problem Statement
• Intuitively, they exemplify a false rumor claim and illustrate its propagation on Twitter
• They observe that a group of tweets is triggered to reply to the same post (i.e., parent post) in the conversation
thread

5. 4
Introduction
Problem Statement
• In order to lower the weight of inaccurate responsive information (e.g., the supportive post 𝑥2 toward the false
claim r), coherent opinions need to be captured by comparing all responsive posts toward the same post
• To achieve this, their proposed interaction topology takes the correlations between sibling nodes such as the
dotted box portion into account

6. 5
Introduction
Contribution
• This is the first study of representing conversation structure as an undirected interaction graph
• They propose a novel ClaHi-GAT model to represent both tweet contents and the interaction graph into a latent
space
• Experimental results show that their model achieves superior performance on three real-world Twitter
benchmarks for both rumor classification and early detection tasks

7. 6
Method
Post-level Attention
𝑔𝑐→𝑥𝑖
(𝑙)
= 𝑠𝑖𝑔𝑚𝑜𝑖𝑑 𝑊
𝑔
(𝑙)
ℎ𝑥𝑖
(𝑙)
+ 𝑈𝑔
(𝑙)
ℎ𝑐
(𝑙)
ℎ𝑥𝑖
(𝑙)
= 𝑔𝑐→𝑥𝑖
(𝑙)
⨀ℎ𝑥𝑖
𝑙
+ (1 − 𝑔𝑐→𝑥𝑖
𝑙
)⨀ℎ𝑐
𝑙
ℎ𝑥𝑖
(𝑙)
= ℎ𝑥𝑖
(𝑙)
||ℎ𝑥𝑖
(𝑙)
𝛼𝑖,𝑗
𝑙
= 𝐴𝑡𝑡𝑒𝑛 ℎ𝑥𝑖
𝑙
, ℎ𝑥𝑖
𝑙

8. 7
Method
Event-level Attention
ℎ𝑥𝑖
𝑐 = tanh 𝐹𝐶 ℎ𝑐
𝐿
||ℎ𝑥𝑖
||ℎ𝑝𝑟𝑜𝑑
𝑙
||ℎ𝑑𝑖𝑓𝑓
𝑙
ℎ𝑝𝑟𝑜𝑑
𝑙
= ℎ𝑐
𝑙
⨀ℎ𝑥𝑖
𝑙
ℎ𝑑𝑖𝑓𝑓
𝑙
= ℎ𝑐
𝐿
− ℎ𝑥𝑖
𝐿

9. 8
Method
Final representation
𝑏𝑖 = tanh 𝐹𝐶 ℎ𝑥𝑖
𝑐
𝛽𝑖 =
exp 𝑏𝑖
𝑖 exp(𝑏𝑖)
𝑠 =
𝑖
𝛽𝑖ℎ𝑥𝑖
𝐿
𝑦 = 𝑠𝑜𝑓𝑡𝑚𝑎𝑥 𝐹𝐶 𝑠||𝑠

10. 9
Experiment
Rumor detection on TWITTER

11. 10
Experiment
Rumor detection on PHEME

12. 11
Experiment
Ablation Study

13. 12
Experiment
Ablation study

14. 13
Experiment
Ablation study

15. 14
Experiment
Early rumor detection accuracy at different checkpoints

16. 15
Experiment
Example of correctly detected false rumors
• we can see that for the false rumor, the inaccurate information like 𝑥2 and 𝑥4 could reduce their weights
and pay more attention to the claim-related denial or questioning posts that contradict the claim, which
may help us correctly predict the false rumor

17. 16
Conclusion
• In this paper, they propose a novel Claim-guided Hierarchical Graph Attention Network based on undirected
interaction graphs to learn graph attention-based embeddings that attend to user interactions for rumor detection
• Multi-level rumor indicative features could be better captured via the claim-aware attention at post level and the
inference-based attention at event level
• The results on three public benchmark datasets confirm the advantages of our model