# MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
###### Abstract
As for fake news detection, multiple approaches are categorized according to their features for prediction: (1) content-based: using text information to spot and verify fake news [20; 21; 22; 23]. (2) user-based: relying on involved users' characteristics to detect fake news [24; 25; 26; 27]. (3) network-based: encoding the propagation structure and feeding it into neural network as features [21; 28; 29; 30; 31]. (4) hybrid-based: integrating some of the above-mentioned features to perform news classification. [32; 33; 34]. Unfortunately, most studies about fake news focus on detection rather than gauging the influence of fake news (i.e., fake news popularity) on social media, which is also crucial for mitigating its impact on our society. In addition, no comprehensive framework captures the dynamics of multi-modal attributes and preserves the model explainability. To achieve our goal, we have to overcome the following challenges: (1) Data collection and fusion: The features used for prediction consist of various types, including social relationships, fake news content, news metadata, and user characteristics, etc. These types of information are stored in different sources and require a distinct retrieving process. Additionally, most of the data is user-generated and irregular, so it takes a lot of effort to link data from various domains together to rebuild the whole picture. (2) Imbalanced distribution: Due to the power-law distribution in the data of social media platforms, only a few posts have massive shares, and most posts have little to no shares. The same phenomenon exists on other social-related data, such as the users' followers, historical posts, etc. The characteristics make it difficult for the prediction model to capture the traits and perform an accurate prediction. To achieve our goal and overcome the challenges mentioned above, in this study, we propose a comprehensive framework MUFFLE to model the dynamics from various domains: social network, user timeline, user profile, and textual content. In MUFFLE, we construct a combination of a Graph Convolution Network (GCN) and Recurrent Neural Network (RNN) to encode the propagation of fake news in social media. As for textual data such as fake news, tweets, and user self-descriptions, we use DistilBERT [35] to encode the representation of text. Additionally, temporal and graph features that proved to be effective in related problems [33; 36; 37; 38] are also used in MUFFLE. To further increase the explainability of our framework and raise the performance, we develop an attention mechanism that learns the importance of each aspect of feature representation. To evaluate the proposed framework, we compare MUFFLE with other popularity prediction approaches, and the result shows that our model outperforms state-of-art models. Another experiment also indicates that the features fed into MUFFLE are effective and correlate to the propagation of fake news on social media. Finally, the feature importance recorded during the experiment shows that our proposed model has explainability in predicting the impact of fake news. To summarize, our main contributions are:  
1. We study a novel problem of predicting the influence of fake news on social media in an early stage, which is also crucial for mitigating the impact of false information on our society. Most studies on fake news focus on ascertaining the veracity of news only. 2. A comprehensive framework MUFFLE is proposed, which jointly models the multi-modal features including news content, social network, and user characteristics. By leveraging the strength of state-of-the-art deep learning models, MUFFLE is able to capture textual, temporal, and structural dynamics along the propagation of fake news. 3. Our proposed framework provides explainability by constructing an attention mechanism. Through analyzing learned attention weights, we further investigate the importance of features in our research problem. 4.