### Present Work and Contributions  
In this paper, we propose a multi-modal attention model called the ExoFIA model that predicts the influence of a post by forecasting the size of the cascade using both endogenous data (i.e., the post itself, the social network, and user information) and exogenous data (i.e., the trend of the news to which the post has linked). We adopt a graph convolution network  
Figure 3: The Google search trends of three keywords of two source tweets.  
(GCN) [25] and gated recurrent unit (GRU) [26] to encode the dynamic propagation of the post, and we use Google Trends as exogenous sources and extract trends with 1D convolutional neural networks (1D-CNN) [27]. We further adopt attention mechanisms and extract the learned weights to enhance the interpretability of the model and explain why a post sharing fake news causes a large cascade. To evaluate our proposed model, we use large-scale real-world Twitter data.  
Diffusion Patterns of Tweets Linked to Fake News vs. Posts Linked to Real News  
First, we looked into the numerous ways that verified real news and fake news were being distributed on Twitter. Although many works have analyzed the dissemination differences between fake and real news [28; 29; 30], most of the studies performed their analysis at the news level (Figure 2). The cascade size of the tweet post related to real news is larger than fake news, as shown in Figure 4c. The average time difference between adjacent nodes (retweet nodes) indicates how fast a post is retweeted. Figure 4a shows that posts citing fake news are retweeted faster. On average, 40% of fake news cascades have a time difference exceeding 100 min, compared to 60% of real news cascades. In Figure 4b, it can be seen that the time difference between the source tweet and the first retweet is similar across different veracity sets.  
Due to the aforementioned differences, we also investigated the difference in variable importance between real and fake news posts by testing the framework on a dataset of'real' news propagation.  
To summarize, the main contributions of this paper are four-fold:  
* This work studies a novel topic of predicting the influence of fake news on social media in an early stage, which is crucial for mitigating the impact of fake news.
* We propose a comprehensive framework named ExoFIA, which jointly models multimodal features, including exogenous factors, such as public trends, and endogenous factors, such as the contents of posts, user characteristics, and the social network being posted on. ExoFIA is able to capture temporal and structural dynamics along the propagation of posts.
* Our proposed framework provides explainability with the aid of an attention mechanism for better understanding. We further examine the difference of feature importance between real and fake news.
* Extensive experiments are conducted on a real-world Twitter dataset, demonstrating the effectiveness of our proposed model. A comparison with existing prediction methods shows the superiority of ExoFIA.  
Figure 4: CCDFs for different veracities. (**a**) Average time difference between adjacent nodes (retweet nodes). Cascades related to fake news have an average of 381.01, with a median of 64.44; cascades related to real news have an average of 390.99, with a median of 103.69. (**b**) Time difference between the source tweet and the first retweet. Cascades related to fake news have an average of 248.25, with a median of 9.58; cascades related to real news have an average of 229.30, with a median of 9.50. (**c**) Cascade size. Cascades related to fake news have an average of 2.92, with a median of 1; cascades related to real news have an average of 4.40, with a median of 1.