SlowGuess/NAACL_2022_OCR huggingface dataset:
In terms of data partitioning, for Twitter15 and Twitter16 we follow previous studies (Ma et al., 2015, 2016) and report the average performance based on 5-fold cross-validation. For CoAID and WEIBO, we reserve  $20\%$  data as test and split the rest in a ratio of 4:1 for training and development partitions and report the average test performance over 5 runs (initialised with different random seeds). We use the development set of each dataset for tuning hyper-parameters. $^{6}$
Additional implementation details for all models
5https://developer.twitter.com/en/docs/twitter-api/v1
For Twitter15/16 during tuning we use only one of the folds and reserve 1/4 of the training data as development set and train the model using the rest (3/4) of the training data.
are given in the Appendix.
We first present results for each of the modules (comment tree, comment chain and user tree) separately to understand the best approach for modelling them, and then present the final results where we compare our full model DUCK to a number of benchmark systems. For the first set of results where we evaluate each module independently, we feed their representations (i.e.  $z_{ct}$ ,  $z_{cc}$  and  $z_{ut}$ ) to an MLP layer to do classification. Specifically, we are interested in the following questions:
- Q1 [Comment tree]: Does incorporating BERT to analyse the relation between parent and child posts help modelling the comment network, and what is the best way to aggregate comment-pair encodings to represent the comment graph?
- Q2 [Comment chain]: Does incorporating more comments help rumour detection when modelling them as a stream of posts?
- 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?
For the first three questions, we present development performance using Twitter16 and CoAID as the representative datasets (as the trends are largely the same for the other datasets), while for the final question we report the test performance for all datasets. In terms of evaluation metrics, we present F1 scores for each class and macro-averaged F1 scores as the aggregate performance. All results are an average over 5 runs (5-fold cross-validation for Twitter15/16 and 5 independent runs with different random seeds for WEIBO and CoAID following Ma et al. (2016, 2017); Cui and Lee (2020)).
5.2.1 Comment Tree
To understand the impact of using BERT for processing a pair of parent-child posts, we present an alternative method ("unpaired") where we use BERT to process each post independently before feeding their [CLS] representation to the GAT. That is, Equation 1 is now modified to:
$$
h _ {p} = \mathrm {B E R T} (\mathrm {e m b} ([ C L S ], c _ {p}))
$$
Variants	Twitter16	CoAID
F1	FR	TR	NR	UR	F1	T	F
unpaired	0.83	0.92	0.87	0.73	0.82	0.83	0.98	0.67
root	0.86	0.85	0.92	0.85	0.83	0.85	0.98	0.71
-root	0.80	0.82	0.91	0.77	0.79	0.80	0.97	0.64
▲	0.87	0.89	0.95	0.74	0.88	0.86	0.99	0.74
all	0.88	0.89	0.94	0.79	0.90	0.87	0.98	0.75
Table 3: Results for the comment tree. "FR", "TR", "NR" and "UR" in Twitter16 denote false, true, non- and unverified rumours respectively; and "T" and "F" in CoAID means true and fake classes.
(a) Twitter16
(b) CoAID
Figure 3: Results (macro-F1) for the comment chain over increasing number of comments.
where  $h$  will be used as the initial node representation  $(h^{(0)})$  in the GAT (Equation 2). We report the performance of this alternative model ("unpaired") and the different aggregation methods ("root", "¬root", "▲" and "all"; equations 3, 4, 5 and 6 respectively) in Table 3.
Comparing the aggregation methods, "all" performs the best, followed by "▲" 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 -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.
5.2.2 Comment Chain
Recall that we explore using transformer models - one-tier transformer, longformer and two-tier trans
7The "unpaired" approach uses "all" as the aggregation method.
Variants	Twitter16	CoAID
F1	FR	TR	NR	UR	F1	T	F
GATrnd	0.47	0.57	0.38	0.48	0.47	0.61	0.59	0.46
GATpref	0.63	0.64	0.67	0.56	0.60	0.80	0.97	0.62
GATpref+rel	0.69	0.74	0.72	0.64	0.68	0.85	0.98	0.71
Table 4: Results for the user tree.
former – for modelling the comments of a story as a sequence. Fig. 3 plots the results where we vary the number of included comments to answer Q2. $^{8}$  Note that for longformer we always use all the comments, since it is designed to process long sequences.
Both one-tier and two-tier transformers see a performance gain when the number of comments increases and a drop when there are too many comments (noting that the trend is flatter in CoAID). However, due to one-tier transformer's sequence length limit, it can take no more than 60 comments on average. Two-tier transformer is able to process more comments, and produces the best performance. Interestingly, even though longformer is able to include most of the comments, it performs worse than both one-tier and two-tier transformer, suggesting that the sparser attention pattern that longformer introduces has a negative impact. With these results, we will use the two-tier transformer to model the comment chain in DUCK.
5.2.3 User Tree
Recall that we use GAT to represent the reply and repost user network, and we investigate different node encodings to initialise GAT: (1) random initialisation  $\mathrm{(GAT_{rnd})}$ ; (2) user profile metadata  $\mathrm{(GAT_{prf})}$ ; and (3) user profile metadata and social relations  $\mathrm{(GAT_{prf + rel})}$ . Results are shown in Table 4. Unsurprisingly, random initialisation performs the worst, and we see a substantial improvement when user profile information is incorporated, and again an improvement when we incorporate user social relations (6% and 5% increase in macro-F1 for Twitter16 and CoAID). Our results highlight the importance of incorporating social relations, and DUCK therefore uses  $\mathrm{GAT_{prf + rel}}$  for modelling the
reply and retweet user network. $^{9}$
5.2.4 Overall Rumour Detection Performance
We next compare the rumour detection performance of DUCK that combines comment tree, comment chain and user tree models (Figure 1) to the following state-of-the-art methods: (1) RvNN (Ma et al., 2018) $^{10}$ : uses a GRU to process text content and recursive networks to model the comment network; (2) RNN+CNN (Liu and Wu, 2018): uses CNN and RNN to model the retweet user network where user representations are initialised with user profile features; (3) stance-BERT (Tian et al., 2020): fine-tunes a BERT pretrained with stance annotations for rumour detection and comments are modelled as a chain (similar to our one-tier transformer model); (4) Bi-GCN (Bian et al., 2020) $^{11}$ : uses a bidirectional graph convolutional network to model the comment network in a top-down (i.e. nodes are combined starting from the leaf comments) and bottom-up manner (i.e. nodes are combined starting from the root); and (5) GCAN (Lu and Li, 2020) $^{12}$ : uses graph networks to model the retweet user network and a CNN to model the source post with co-attention between the two networks. For a summary of the different features used by these benchmark systems and our model, see Table 1.
All benchmark results are produced using the author-provided code, with the exception of RNN+CNN and stance-BERT where we implement ourselves. Note that we only have English results (Twitter15, Twitter16 and CoAID) for stance-BERT as it uses stance annotations from SemEval2016 (Mohammad et al., 2016), and GCAN and RNN+CNN do not have results for CoAID as it does not contain retweets.
We present the results in Table 5. DUCK (our model) performs very strongly, outperforming all benchmark systems consistently over all datasets, creating a new state-of-the-art for rumour detection. In terms of datasets, WEIBO appears to be the "easier" dataset, where most systems produce a macro-F1 over  $90\%$ . We also observe that models that use the comment texts (stance-BERT and Bi-GCN) tend to do better than those that only
For one-tier and two-tier transformers, if the number of comments is set to 10, that means we will concatenate 10 comments (with the source post) into a long string, and any text that exceeds BERT's maximum sequence length will be truncated (and so for some stories the models may use less than 10 comments, if earlier comments are very long).
<sup>9</sup>With the exception of WEIBO where we can't crawl users' followers, and so it uses  $\mathrm{GAT}_{\mathrm{prf}}$ .
10https://github.com/majingCUHK/Rumor_RvNN
<sup>11</sup>https://github.com/TianBian95/BiGCN
$^{12}$ https://github.com/1852888/GCAN
Twitter15	Twitter16	CoAID	WEIBO
Model	F1	FR	TR	NR	UR	F1	FR	TR	NR	UR	F1	T	F	F1	NR	R
RvNN	0.72	0.76	0.82	0.68	0.65	0.74	0.74	0.84	0.66	0.71	0.78	0.98	0.57	0.91	0.91	0.91
RNN+CNN	0.53	0.51	0.30	0.36	0.64	0.56	0.54	0.40	0.59	0.67	-	-	-	0.92	0.91	0.92
stance-BERT	0.82	0.82	0.85	0.87	0.71	0.83	0.82	0.88	0.83	0.77	0.90	0.99	0.81	-	-	-
Bi-GCN	0.86	0.85	0.91	0.84	0.82	0.86	0.86	0.93	0.79	0.86	0.83	0.99	0.68	0.96	0.96	0.96
GCAN	0.69	0.75	0.75	0.63	0.68	0.72	0.73	0.78	0.67	0.72	-	-	-	0.92	0.92	0.92
DUCK-CT	0.82	0.72	0.91	0.82	0.85	0.84	0.88	0.81	0.88	0.79	0.91	0.99	0.82	0.93	0.93	0.93
DUCK-CC	0.85	0.91	0.86	0.81	0.82	0.85	0.84	0.91	0.78	0.87	0.87	0.98	0.75	0.94	0.94	0.94
DUCK-UT	0.88	0.92	0.84	0.91	0.85	0.89	0.91	0.91	0.87	0.88	0.91	0.99	0.83	0.97	0.97	0.97
DUCK	0.90	0.91	0.93	0.88	0.88	0.91	0.89	0.93	0.93	0.91	0.92	0.99	0.85	0.98	0.98	0.98
Table 5: Overall rumour detection results. "CT", "CC" and "UT" denote comment tree, comment chain and user tree respectively, and "R" and "NR" in WEIBO denote rumour and non-rumour.
use the user network (RNN+CNN and GCAN), although the strong performance of DUCK indicates that combining both types of information works best, suggesting that they complement each other. Another thing of note is CoAID, the only dataset where the class distribution is heavily imbalanced. Here we see that most systems struggle with the minority class ("F"), but our combined approach appears to handle this well.
To understand the impact of each module in DUCK, we present variants where we remove one module, e.g. DUCK-CT means comment tree removed. Results suggest that comment tree has the largest impact, followed by comment chain as they produce the largest performance drop when removed. This finding is similar to what we saw earlier, where systems like stance-BERT and BiGCN that use comments tend to perform better.
We presented DUCK, a social media rumour detection approach that models both the network of users who interact with a story as well as their comments/opinions. Our approach is unique in how we model the comment as a graph (with BERT and GAT) and also as a stream (with transformers) and the user networks together with their peer relations (with GAT and GAE). We conduct extensive experiments over four popular rumour benchmark datasets to evaluate DUCK. We found that the comment network contains the strongest signal for predicting rumours, and social relations are important for modelling the user network. DUCK substantially outperforms all benchmark methods consistently, creating a new state-of-the-art.
We contend that while automatic rumour detection systems can benefit combating the spread of misinformation, there are potential risks to them. If these systems are deployed on social media to monitor user posts without human oversight, there are implications when these systems misclassify (particularly in the false positive cases) and users are wrongfully accused for posting misinformation. As such, we recommend these tools to be used as an aid, e.g. by filtering the enormous volume of data and help human analysts to narrow down and detect harmful stories on social media.
This research is supported partially by the Australian Research Council Discovery Project DP200101441. Lin Tian is supported by the RMIT University Vice-Chancellor PhD Scholarship (VCPS).
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Our models are implemented in PyTorch using the HuggingFace library<sup>13</sup> and their pretrained
BERT $^{14}$  and Chinese-BERT $^{15}$ . Graph neural networks are implemented with the Geometric $^{16}$  package.
For the comment tree, we set maximum token length  $= 40$  and dropout rate  $= [0.5, 0.6]$  for GAT and 0.2 for BERT embeddings. Learning rate is tuned in the range  $[1e^{-5}, 5e^{-5}]$  for BERT and  $[1e^{-4}, 5e^{-4}]$  for GAT based on the development set. For the comment chain, the learning rate for two-tier transformer (comment chain) is tuned in the range  $[2e^{-5}, 5e^{-5}]$  with the maximum token length of 40. For the user tree, we set the dimension of each node hidden features as 256. All models use the Adam optimiser (Kingma and Ba, 2015), and our experiments are run using  $4\times \mathrm{A}100$  GPU with 40GB Memory.
<sup>13</sup>https://github.com/huggingface
$^{14}$ https://huggingface.co/bert-base-cased
15 https://huggingface.co/bert-base-chinese
$^{16}$ https://pytorch-geometric.readthedocs.io/en/latest/.