SlowGuess/ACL_2020_OCR huggingface dataset:
- SVM integrating semantics from claim content and comments outperforms traditional neural networks only capturing semantics from claim content, like CNN and TE, with at least  $4.75\%$  and  $6.96\%$  boost in accuracy on the two datasets respectively, which indicates that semantics of comments are helpful for claim verification.
- On the whole, most neural network models with semantic interaction between comments and claims, such as TRNN and Bayesian-DL, achieve from  $4.77\%$  to  $9.53\%$  improvements in accuracy on the two datasets than SVM without any interaction, which reveals the effectiveness of the interaction between comments and claims.
- TRNN, Bayesian-DL, and DTCA enable claims and comments to interact, but the first two models get the worse performance than DTCA (at least  $1.06\%$  and  $1.19\%$  degradation in accuracy respectively). That is because they integrate all comments indiscriminately and might introduce some noise into their models, while DTCA picks more valuable comments by DTE.
- Multi-task learning models, e.g. MTL-LSTM and Sifted-MTL leveraging stance features show
at most  $3.29\%$  and  $1.86\%$  boosts in recall than DTCA on the two datasets respectively, but they also bring out noise, which achieve from  $1.06\%$  to  $21.11\%$  reduction than DTCA in the other three metrics. Besides, DTCA achieves  $3.11\%$  and  $2.41\%$  boosts than the latest baseline (sifted-MTL) in F1-score on the two datasets respectively. These elaborate the effectiveness of DTCA.
4.4.1 The impact of comments on DTCA
In Section 4.3, we find that the use of comments can improve the performance of models. To further investigate the quantitative impact of comments on our model, we evaluate the performance of DTCA and CaSa with  $0\%$ ,  $50\%$ , and  $100\%$  comments. The experimental results are shown in Table 3. We gain the following observations:
- Models without comment features present the lowest performance, decreasing from  $5.08\%$  to  $9.76\%$  in accuracy on the two datasets, which implies that there are a large number of veracity-indicative features in comments.
- As the proportion of comments expands, the performance of models is improved continuously. However, the rate of comments for CaSa raises from  $50\%$  to  $100\%$ , the boost is not significant, only achieving  $1.44\%$  boosts in accuracy on RumourEval, while DTCA obtains better performance, reflecting  $3.90\%$  and  $3.28\%$  boosts in accuracy on the two datasets, which fully proves that DTCA can choose valuable comments and ignore unimportant comments with the help of DTE.
4.4.2 The impact of decision conditions of DTE on DTCA (RQ2)
To answer RQ2, we analyze the changes of model performance under different decision conditions.
(a) Different semantic similarity
(b) Different credibility of reviewers
Figure 3: The accuracy comparison of DTCA in different decision conditions. Broken lines represent the performance difference (D-value) between the current decision condition and the previous decision condition.
(c) Different credibility of comments
No (0%) Comments
A	P	R	F1
RumourEval	CaSa	72.78	67.03	72.87	69.83
DTCA	72.78	67.03	72.87	69.83
PHEME	CaSa	73.21	71.26	74.74	72.96
DTCA	73.21	71.26	74.74	72.96
50% Comments
RumourEval	CaSa	76.42	70.21	76.78	73.35
DTCA	78.64	73.43	80.06	76.60
PHEME	CaSa	77.65	74.18	78.11	76.09
DTCA	79.18	75.24	80.66	77.86
All (100%) Comments
RumourEval	CaSa	77.86	71.92	79.24	75.40
DTCA	82.54	78.25	85.60	81.76
PHEME	CaSa	79.85	75.06	80.35	77.61
DTCA	82.46	79.08	86.24	82.50
Table 3: The performance comparison of models on different number of comments.
Different decision conditions can choose different comments as evidence to participate in the model learning. According to the performance change of the model verification, we are capable of well explaining the process of evidence selection through decision conditions. Specifically, we measure different values (interval [0, 1]) as thresholds of decision conditions so that DTE could screen different comments. Figure 3(a), (b), and (c) respectively present the influence of semantic similarity (simi), the credibility of reviewers ( $r\_cred$ ), and the credibility of comments ( $c\_cred$ ) on the performance of DTCA, where the maximum thresholds are set to 0.7, 0.7, and 0.6 respectively because there are few comments when the decision threshold is greater than these values. We observe that:
- When simi is less than 0.4, the model is continually improved, where the average performance improvement is about  $2\%$  (broken lines) on the two datasets when simi increases by 0.1. Especially, DTCA earns the best performance when simi is set to  $0.5 (< 0.5)$ , while it is difficult to improve performance after that. These exemplify that DTCA can provide more credibility features under appropriate semantic similarity
for verification.
- DTCA continues to improve with the increase of  $r\_cred$ , which is in our commonsense, i.e., the more authoritative people are, the more credible their speech is. Analogously, DTCA boosts with the increase of  $c\_cred$ . These show the reasonability of the terms of both the credibility of reviewers and comments built by meta data.
- When simi is set to 0.5 (<0.5),  $r\_cred$  is 0.7 (<0.7),  $c\_cred$  is 0.6 (<0.6), DTCA wins the biggest improvements, i.e., at least 3.43%, 2.28%, and 2.41% on the two datasets respectively. At this moment, we infer that comments captured by the model contain the most powerful evidence for claim verification. This is, the optimal evidence is formed under the conditions of moderate semantic similarity, high reviewer credibility, and higher comment credibility, which explains the selection process of evidence.
4.4.3 Explainability Analysis (RQ3)
To answer RQ3, we visualize comments (evidence) captured by DTE and the key semantics learned by CaSa when the training of DTCA is optimized. Figure 4 depicts the results based on a specific sample in PHEME, where at the comment level, red arrows represent the captured evidence and grey arrows denote the unused comments; at the word level, darker shades indicate higher weights given to the corresponding words, representing higher attention. We observe that:
- In line with the optimization of DTCA, the comments finally captured by DTE contain abundant evidence that questions the claim, like 'presumably Muslim? How do you arrive at that?', 'but it should be confirmed 1st before speculating on air', and 'false flag', to prove the falsity of the claim (the label of the claim is false), which indicates that DTCA can effectively discover evidence to explain the results of claim verification.
Figure 4: The visualization of a sample (labeled false) in PHEME by DTCA, where the captured evidence (red arrows) and the specific values of decision conditions (blue) are presented by DTE, and the attention of different words (red shades) is obtained by CaSa.
Additionally, there are common characteristics in captured comments, i.e., moderate semantic similarity (interval [0.40, 0.49]), high reviewer credibility (over 0.66), and high comment credibility (over 0.62). For instance, the values of the three characteristics of evidence ‘@TimGriffiths85 where was that reported? #dickface’ are 0.47, 0.66, and 0.71 respectively. These phenomena explain that DTCA can give reasonable explanations to the captured evidence by decision conditions of DTE, which visually reflects the interpretability of DTCA method itself.
- At the word level, the evidence-related words 'presumably Muslim', 'made fun of', 'shooter', and 'isn't Islamist' in comments receive higher weights than the evidence-independent words 'surprised', 'confirmed 1st' and 'speculating', which illustrates that DTCA can earn the key semantics of evidence. Moreover, 'weekly Charlie Hebdo' in the claim and 'Islamist' and 'Muhammad' in comments are closely focused, which is related to the background knowledge, i.e., weekly Charlie Hebdo is a French satirical comic magazine which often publishes bold satire on religion and politics. 'report say' in claim is queried in the comments, like 'How do you arrive at that?' and 'false flag'. These visually demonstrate that DTCA can uncover the questionable and even false parts in claims.
4.4.4 Error Analysis
Table 4 provides the performance of DTCA under different claims with different number of comments. We observe that DTCA achieves the satisfactory performance in claims with more than 8
Claims	Datasets	A (%)	P (%)	R (%)	F1 (%)
Claims with less than 3 comments	RumourEval	73.42	68.21	73.50	70.76
PHEME	74.10	72.45	75.32	73.86
Claims with comments ∈ [3, 8]	RumourEval	75.33	69.16	75.07	71.99
PHEME	77.26	74.67	79.03	76.79
Claims with more than 8 comments	RumourEval	80.25	75.61	83.45	79.34
PHEME	80.36	75.52	84.33	79.68
Table 4: The performance comparison of DTCA under different claims with different number of comments.
comments, while in claims with less than 8 comments, DTCA does not perform well, underperforming its best performance by at least  $4.92\%$  and  $3.10\%$  in accuracy on the two datasets respectively. Two reasons might explain the issue: 1) The claim with few comments has limited attention, and its false parts are hard to be found by the public; 2) DTCA is capable of capturing worthwhile semantics from multiple comments, but it is not suitable for verifying claims with fewer comments.
We proposed a novel framework combining decision tree and neural attention networks to explore a transparent and interpretable way to discover evidence for explainable claim verification, which constructed decision tree model to select comments with high credibility as evidence, and then designed co-attention networks to make the evidence and claims interact with each other for unearthing the false parts of claims. Results on two public datasets demonstrated the effectiveness and explainability of this framework. In the future, we will extend the proposed framework by considering more context (meta data) information, such as time, storylines, and comment sentiment, to further enrich our ex
plainability.
The research work is supported by National Key Research and Development Program in China (2019YFB2102300); The World-Class Universities (Disciplines) and the Characteristic Development Guidance Funds for the Central Universities of China (PY3A022); Ministry of Education Fund Projects (18JZD022 and 2017B00030); Shenzhen Science and Technology Project (JCYJ20180306170836595); Basic Scientific Research Operating Expenses of Central Universities (ZDYF2017006); Xi'an Navinfo Corp.& Engineering Center of Xi'an Intelligence Spatial-temporal Data Analysis Project (C2020103); BeiLin District of Xi'an Science & Technology Project (GX1803). We would like to thank the anonymous reviewers for their insightful comments.
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Element	Standard of criterion	Credibility score
Whether the elements exist or not	verified	True	3
geo	True	3
screen name	True	1
profile image	True	2
The value of elements	followers	[0, 100)	2
[100, 500)	5
[500,∞)	10
friends	[0, 100)	1
[100, 200)	2
[200,∞)	5
favourites	[0, 100)	2
[100, 200)	3
[200,∞)	5
Table 5: The credibility score of reviewers
Element	Standard of criterion	Credibility score
Whether the elements exist or not	geo source	True	3
True	3
favorite the comment	True	1
The value of elements	the number of favorites	[0, 100)	5
[100,∞)	7
the length of content	[0, 10)	3
[10,∞)	7
Table 6: The credibility score of comments
A The Details of Decision Conditions
The paper has introduced the details of semantic similarity between claims and comments, here we introduce the details of the other two decision conditions.
Table 5 shows some scores of meta data related to reviewer credibility. In elements 'whether the elements exist or not', if the element is false, the score will be zero. The credibility score of reviewer (r_cred) is formulated as follows:
$$
r \_ c r e d = \frac {A 1}{B 1} \tag {11}
$$
where A1 denotes the specific score accumulation of all metadata related to reviewer credibility and B1 means the total credibility score of reviewers.
Table 6 describes some credibility score of comments. Like the credibility score of reviewer, in elements 'whether the elements exist or not', if the element is false, the score will be zero. The credibility score of comments (c_cred) is as follows:
$$
c \_ c r e d = \frac {A 2}{B 2} \tag {12}
$$
where A2 denotes the specific score accumulation of all metadata related to comment credibility and B2 means the total credibility score of comments.
B Algorithm of DTCA
The training procedure of DTCA is shown in Algorithm 1.
Algorithm 1: Training Procedure of DTCA.
Require: Dataset  $S = \{C_i,R_i,y\} _1^T$  with  $T$  training samples, where  $C_i$  denotes one claim and  $R_{i}$  means tree comments, i.e.,  $R_{i} = r_{1},r_{2},\dots,r_{k}$ . Particularly,  $k$  is different for different claims; the thresholds of the three decision conditions are  $a_1,a_2,a_3$  respectively; the evidence set  $E$  model parameters  $\Theta$  ; learning rate  $\epsilon$
1 Initial parameters;
2 Repeat
3
// Part 1: DTE
4 A series of subtree sets S obtained by depth-first search of tree comments  $R_{i}$  i.e.,  $S = [S_1,S_2,\dots,S_n]$
5 | On each subtree  $S_{i}$ :
6 For  $r_i$  in  $S_{i}$  do
// The semantic similarity between // comments and claims
7 If simi  $(r_i,C_i)\in$  interval[a,b]: 8  $E = E + r_{i}$
// The credibility of reviewers
9 If  $r$ -cred  $(r_i) > a_2$
10  $E = E + r_{i};$
// The credibility of comments
11 If  $c\_cred(r_i) > a_3$
12  $E = E + r_{i};$
13 End For
14 By traversing all subtrees, the final evidence set  $E$  that meets the conditions is captured.
//Part 2: CaSa
Word embeddings of evidence  $E$
16  $X^{e} = BERT\_ embed(E)$
17 Word embeddings of claim  $C_i$ :
18 | BERT_embedding  $(C_i)$
19 Get representations of evidence and claim by Eq. (1-3), i.e.,  $R^e$  and  $R^c$ ;
20 | Get  $R_{pool}^{e}$  by maximum pooling operation of  $R^{e}$ ;
21 Get deep interaction semantics  $C$  of claim concerned by evidence through integrating  $R_{pool}^{e}$  into self-attention networks by  $R_{pool}^{e}$  Eq. (4-7);
22 Get deep interaction semantics  $E$  of evidence concerned by the claim through integrating  $C$  into self-attention networks by Eq. (4-7);
23 Get fused vectors between evidence and claim by Eq. (8);
24 Compute loss  $L(\Theta)$  using Eq. (9,10);
25 Compute gradient  $\nabla (\Theta)$
26 Update model:  $\Theta \gets \Theta -\epsilon \nabla (\Theta)$
27 End For
28 Update parameters  $a_1, a_2, a_3$ ;
29 Until  $a_1 = 0.8$ ,  $a_2 = 0.8$ , and  $a_3 = 0.7$ .