###### Abstract  
The growth of social platforms has lowered the barrier of entry into the media sector, allowing for the spread of false information and putting democratic politics and social security at peril. Preliminary analysis shows that posts sharing real news and fake news are disseminated on social media. Moreover, posts pointing to fake news spread faster, so this paper aims to predict the impact of posts citing fake news on social platforms. In this study, we take into account that exogenous factors, in addition to endogenous factors, can potentially determine how influential a post is. For example, the occurrence of social events can generate public resonance and discussion, thereby increasing the impact of relevant posts. Given that Google Trends can obtain search trends that reflect social popularity, this work aims to use Google Trends as the source of our exogenous factors. We propose a deep learning model called the deep exogenous aid in fake news (ExoFIA) model, which combines multi-modal features and utilizes an attention mechanism to provide model interpretability and analyze the influencing factors. Applying the model to real-world data from Twitter demonstrates that our model outperforms existing diffusion models. Furthermore, further examination of the relevant aspects of true and fake news reveals that the two are influenced by distinct variables. fake news; popularity prediction; exogenous factors; multi-modal learning; model interpretability +
Footnote †: journal: Medical  
not the news level (one or multiple cascades with a singular origin). An example is shown in Figure 2. Fake news is spread via links in posts. The estimator should have the ability to gauge the influence of each post and sort all posts by their estimated influence, which executives can use as a measure of priority for reviewing posts. This can help organizations mitigate the spread of disinformation at an early stage. For example, online service providers such as Twitter or other forums can use the estimator to detect possible outbreaks of posts containing fake news, thereby safeguarding healthy conversations. Therefore, this work can be used in subsequent efforts to detect false news. This work faces the following challenges. (1) Data collection and processing: the API limitations of Twitter make data acquisition time-consuming. Features used for forecasting are heterogeneous and are of multiple types, including social relationships, fake news content, news metadata, and user contexts. These types of features should be captured from different sources and require different retrieval processes and fusion methods to link the data together to reconstruct the whole picture. (2) Exogenous stimuli: the factors that cause large cascades are complex and are both endogenous (internal) and exogenous (external). Endogenous factors come from social platforms. However, exogenous factors are uncertain events outside the social platform that stimulate cascaded diffusion. To conclude, exogenous factors make modeling difficult. (3) Unbalanced distributions: the number of retweets of posts in real life exhibits a power-law distribution, with the bulk of posts not being retweeted and only a few posts receiving a large number of retweets. This characteristic makes the training process difficult. Recently, several efforts have been devoted to popularity prediction on social media, which can be categorized into three main categories: generative approaches, feature-based approaches, and deep-learning-based approaches. Probabilistic statistical generative approaches, such as the Poisson process and Hawkes process, aim to model the  
Figure 1: Modified examples (for anonymity) of tweets. Figure 2: Illustration of fake news propagating on social media. arrival/occurrence of event sequences or the participation time series, e.g., information retweeting [7; 8; 9; 10; 11].