SlowGuess/ACL_2020_OCR huggingface dataset:
Source-Propagation Co-attention. The process to generate the co-attention feature vectors,  $\hat{\mathbf{s}}_2\in \mathbb{R}^{1\times d}$  and  $\hat{\mathbf{c}}\in \mathbb{R}^{1\times d}$ , for the source story and user propagation, respectively, is the same as source-interaction co-attention, i.e., creating another proximity matrix to transform them into each other's space. We skip the repeated details due to the page limit.
Note that the GRU-based user representations are not used to learn the interactions with the source tweet. The reason is that how user profiles in the retweet sequence look like is also important, as suggested by CRNN (Liu and Wu, 2018), and should
Table 2: Statistics of two Twitter datasets.
Twitter15	Twitter16
# source tweets	742	412
# true	372	205
# fake	370	207
# users	190,868	115,036
avg. retweets per story	292.19	308.70
avg. words per source	13.25	12.81
be emphasized separately. Nevertheless, the CNN-based user representations (i.e., features that depict the sequence of user profiles) has been used in the co-attention mechanism to learn their interactions with source tweet.
4.6 Make Prediction
We aim at predicting fake news using the source-interaction co-attention feature vectors  $\hat{\mathbf{s}}_1$  and  $\hat{\mathbf{g}}$ , the source-propagation feature vectors  $\hat{\mathbf{s}}_2$  and  $\hat{\mathbf{c}}$ , and the sequential propagation feature vector  $\mathbf{h}$ . Let  $\mathbf{f} = [\hat{\mathbf{s}}_1, \hat{\mathbf{g}}, \hat{\mathbf{s}}_2, \hat{\mathbf{c}}, \mathbf{h}]$  which is then fed into a multi-layer feedforward neural network that finally predicts the label. We generate the binary prediction vector  $\hat{\mathbf{y}} = [\hat{y}_0, \hat{y}_1]$ , where  $\hat{y}_0$  and  $\hat{y}_1$  indicate the predicted probabilities of label being 0 and 1, respectively. It can be derived through
$$
\hat {\mathbf {y}} = \operatorname {s o f t m a x} \left(\operatorname {R e L U} \left(\mathbf {f} \mathbf {W} _ {f} + \mathbf {b} _ {f}\right)\right), \tag {7}
$$
where  $\mathbf{W}_f$  is the matrix of learnable parameters, and  $\mathbf{b}_f$  is the bias term. The loss function is devised to minimize the cross-entropy value:
$$
\mathcal {L} (\Theta) = - y \log \left(\hat {y} _ {1}\right) - (1 - y) \log \left(1 - \hat {y} _ {0}\right) \tag {8}
$$
where  $\Theta$  denotes all learnable parameters in the entire neural network. We choose the Adam optimizer to learn  $\Theta$  as it can determine the learning rate abortively.
We conduct experiments to answer three questions: (1) whether our GCAN model is able to achieve satisfactory performance of fake news detection, compared to state-of-the-art methods? (2) how does each component of GCAN contribute to the performance? (3) can GCAN generate a convincing explanation that highlights why a tweet is fake?
5.1 Datasets and Evaluation Settings
Data. Two well-known datasets compiled by Ma et al. (2017), Twitter15 and Twitter16, are utilized. Each dataset contains a collection of source
tweets, along with their corresponding sequences of retweet users. We choose only "true" and "fake" labels as the ground truth. Since the original data does not contain user profiles, we use user IDs to crawl user information via Twitter API.
Competing Methods. We compare our GCAN with the state-of-the-art methods and some baselines, as listed below. (1) DTC (Castillo et al., 2011): a decision tree-based model combining user profiles and the source tweet. (2) SVM-TS (Ma et al., 2015): a linear support vector machine classifier that utilizes the source tweet and the sequence of retweet users' profiles. (3) mGRU (Ma et al., 2016): a modified gated recurrent unit model for rumor detection, which learns temporal patterns from retweet user profile, along with the source's features. (4) RFC (Kwon et al., 2017): an extended random forest model combining features from retweet user profiles and the source tweet. (5) CSI (Ruchansky et al., 2017): a state-of-the-art fake news detection model incorporating articles, and the group behavior of users who propagate fake news by using LSTM and calculating the user scores. (6) tCNN (Yang et al., 2018): a modified convolution neural network that learns the local variations of user profile sequence, combining with the source tweet features. (7) CRNN (Liu and Wu, 2018): a state-of-the-art joint CNN and RNN model that learns local and global variations of retweet user profiles, together with the resource tweet. (8) dEFEND (Shu et al., 2019a): a state-of-the-art co-attention-based fake news detection model that learns the correlation between the source article's sentences and user profiles.
Model Configuration. Our model is termed "GCAN". To examine the effectiveness of our graph-aware representation, we create another version "GCAN-G", denoting our model without the graph convolution part. For both our models and competing methods, we set the number of training epochs to be 50. The hyperparameter setting of GCAN is: number of retweet users  $= 40$ , word embedding  $\dim = 32$ , GRU output  $\dim = 32$ , 1-D CNN output filter size  $= 3$ , 1-D CNN output  $\dim = 32$ , and GCN output  $\dim = 32$ . The hyperparameters of competing methods are set by following the settings mentioned in respective studies.
Metrics & Settings. The evaluation metrics include Accuracy, Precision, Recall, and F1. We randomly choose  $70\%$  data for training and  $30\%$  for testing. The conducted train-test is repeated 20
Table 3: Main results. The best model and the best competitor are highlighted by bold and underline, respectively.
Twitter15	Twitter16
Method	F1	Rec	Pre	Acc	F1	Rec	Pre	Acc
DTC	0.4948	0.4806	0.4963	0.4949	0.5616	0.5369	0.5753	0.5612
SVM-TS	0.5190	0.5186	0.5195	0.5195	0.6915	0.6910	0.6928	0.6932
mGRU	0.5104	0.5148	0.5145	0.5547	0.5563	0.5618	0.5603	0.6612
RFC	0.4642	0.5302	0.5718	0.5385	0.6275	0.6587	0.7315	0.6620
tCNN	0.5140	0.5206	0.5199	0.5881	0.6200	0.6262	0.6248	0.7374
CRNN	0.5249	0.5305	0.5296	0.5919	0.6367	0.6433	0.6419	0.7576
CSI	0.7174	0.6867	0.6991	0.6987	0.6304	0.6309	0.6321	0.6612
dEFEND	0.6541	0.6611	0.6584	0.7383	0.6311	0.6384	0.6365	0.7016
GCAN-G	0.7938	0.7990	0.7959	0.8636	0.6754	0.6802	0.6785	0.7939
GCAN	0.8250	0.8295	0.8257	0.8767	0.7593	0.7632	0.7594	0.9084
Improvement	15.0%	20.8%	18.1%	18.7%	19.3%	15.9%	3.8%	19.9%
times, and the average values are reported.
Main Results. The main results are shown in Table 3. We can clearly find that the proposed GCAN significantly outperforms the best competing methods over all metrics across two datasets, improving the performance by around  $17\%$  and  $15\%$  on average in Twitter15 and Twitter16, respectively. Even without the proposed graph-aware representation, GCAN-G can improve the best competing method by  $14\%$  and  $3\%$  on average in Twitter15 and Twitter16, respectively. Such promising results prove the effectiveness of GCAN for fake news detection. The results also imply three insights. First, GCAN is better than GCAN-G by  $3.5\%$  and  $13\%$  improvement in Twitter15 and Twitter16, respectively. This exhibits the usefulness of graph-aware representation. Second, the dual co-attention mechanism in GCAN is quite powerful, as it clearly outperforms the best non-co-attention state-of-the-art model CSI. Third, while both GCAN-G and dEFEND are co-attention-based, additional sequential features learned from the retweet user sequence in GCAN-G can significantly boost the performance.
Early Detection. We further report the performance (in only Accuracy due to page limit) by varying the number of observed retweet users per source story (from 10 to 50), as exhibited in Figure 2 and Figure 3. It can be apparently found that our GCAN consistently and significantly outperforms the competitors. Even with only ten retweeters, GCAN can still achieve  $90\%$  accuracy. Such results tell GCAN is able to generate accurate early detection of the spreading fake news, which is cru
Figure 2: Accuracy by # retweet users in Twitter15.
Figure 3: Accuracy by # retweet users in Twitter16.
cial when defending misinformation.
Ablation Analysis. We report how each of GCAN component contributes by removing each one from the entire model. Below "ALL" denotes using all components of GCAN. By removing dual co-attention, GRU-based representation, graph-aware representation, and CNN-based representation, we have sub-models "-A", "-R", "-G",
Figure 4: GCAN ablation analysis in Accuracy.
Figure 5: Highlighting evidential words via word cloud. Larger font sizes indicate higher co-attention weights.
and “-C”, respectively. Sub-model “-S-A” denotes the one without both source tweet embeddings and dual co-attention. The results are presented in Figure 4. We can find every component indeed plays a significant contribution, especially for dual co-attention (“-A”) and the representation learning of user propagation and interactions (“-R” and “-G”). Since the source tweet provides fundamental clues, the accuracy drops significantly without it (“-S-A”).
5.3 GCAN Explainability
The co-attention weights derived from Section 4.5 attended on source tweet words and retweet users (source-propagation co-attention) allow our GCAN to be capable of explainability. By exhibiting where attention weights distribute, evidential words and users in predicting fake news can be revealed. Note that we do not consider source-interaction co-attention for explainability because user interaction features learned from the constructed graph cannot be intuitively interpretable.
Explainability on Source Words. To demonstrate the explainability, we select two source tweets in the test data. One is fake ("breaking: ks patient at risk for ebola: in strict isolation at ku med center in kansas city #kwch12"), and the other is real ("confirmed: this is irrelevant. rt @ks
Figure 6: Visualization of attention weights for user propagations of 3 fake (upper F1-F3) and 3 true source tweets. From left to right is retweet order. Dark colors refer to higher attention weights.
Figure 7: Evidential words highlighted by GCAN in source tweet (upper) and suspicious users highlighted by GCAN in retweet propagation (bottom), in which each column is a user characteristic. Note that only few user characteristics are presented.
dknews: confirmed: #mike-brown had no criminal record. #ferguson"). We highlight evidential words with higher co-attention weights in font sizes of word clouds, as exhibited in Figure 5. GCAN predicts the former to be fake with stronger attention on words "breaking" and "strict", and detects the latter as real since it contains "confirmed" and "irrelevant." Such results may correspond to the common knowledge (Rashkin et al., 2017; Horne and Adali, 2017) that fake news tends to use dramatic and obscure words while real news is attended by confirmed and fact checking-related words.
Explainability on Retweet Propagation. We aim to exploit the retweet order in propagations to unfold the behavior difference between fake and real news. We randomly pick three fake (F1-F3) and three true (T1-T3) source stories, and plot their
weights from source-propagation co-attention (Section 4.5), as exhibited in Figure 6, in which the horizontal direction from left to right denotes the order of retweet. The results show that to determine whether a story is fake, one should first examine the characteristics of users who early retweet the source story. The evidences of fake news in terms of user characteristics may be evenly distributed in the propagation.
Explainability on Retweeter Characteristics. The source-propagation co-attention of our GCAN model can further provide an explanation to unveil the traits of suspicious users and the words they focus on. A case study is presented in Figure 7. We can find that the traits of suspicious users in retweet propagation can be: accounts are not verified, shorter account creation time, shorter user description length, and shorter graph path length to the user who posts the source tweet. In addition, what they highly attend are words "breaking" and "pipeline." We think such kind of explanation can benefit interpret the detection of fake news so as to understand their potential stances.
In this study, we propose a novel fake news detection method, Graph-aware Co-Attention Networks (GCAN). GCAN is able to predict whether a short-text tweet is fake, given the sequence of its retweeters. The problem scenario is more realistic and challenging than existing studies. Evaluation results show the powerful effectiveness and the reasonable explainability of GCAN. Besides, GCAN can also provide early detection of fake news with satisfying performance. We believe GCAN can be used for not only fake news detection, but also other short-text classification tasks on social media, such as sentiment detection, hate speech detection, and tweet popularity prediction. We will explore model generalization in the future work. Besides, while fake news usually targets at some events, we will also extend GCAN to study how to remove event-specific features to further boost the performance and explainability.
This work is supported by Ministry of Science and Technology (MOST) of Taiwan under grants 109-2636-E-006-017 (MOST Young Scholar Fellowship) and 108-2218-E-006-036, and also by Academia Sinica under grant AS-TP-107-M05.
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