# MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
###### Abstract  
To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE. fake news; influence estimator; multi-model; text mining +
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estimator to predict the popularity of fake news in social media. The estimator will have the ability to predict which post of fake news has a high popularity potential, so it can help organizations identify and mitigate the propagation of false information in the early stage. Additionally, with the good generalization performance of our model, the methods and features proposed in this paper can also be applied to other situations, such as identifying events of viral marketing, recommendation systems, and online advertising. In the past decade, a series of efforts have been devoted to the problem of popularity prediction and fake news detection. Generally speaking, popularity prediction aims to predict future popularity when observing early adopters during a specific observation period. Some studies proposed to use generative approaches, such as the Poisson process or Hawkes process, to model the propagation process of information and estimate the scale of cascade [1; 5; 6; 7; 8]. Other approaches employed features from various aspects of social media, such as user characteristics [9; 10; 11], temporal information [12; 13], and the structure of propagation network [14; 15; 16]. These methods have shown significant improvement in prediction. However, due to the limitation that input features rely heavily on hand-crafting and domain knowledge, the models provide less generalization. To address this problem, deep learning-based methods were proposed to automatically capture the dynamics of information dissemination [1; 17; 18; 19]. By leveraging deep learning techniques, the methods achieved robust prediction performance without requiring strong prior knowledge and feature design. However, there is still room for improvement in some specific topics, e.g., predicting the influence of fake news. Predicting the influence of fake news is not exactly the same as popularity prediction. The reason is four-fold. Firstly, existing work for popularity prediction usually considers only posts to build the cascade network. However, we exploit retweet posts to build cascade networks, which is especially worth investigating for fake news. The reason is that user characteristics, i.e., node attributes, have more impacts on message dissemination than the post itself. Second, no existing work considers user timeline and self-description in the user's profile. These two sources are significantly important features for fake news analysis. Thirdly, most traditional work, including feature-based and deep learning-based methods, cannot provide explainability, while our designed attention mechanism can. Fourth, our work is easy to be incorporated with state-of-art fake news detection, while popularity prediction work does not focus on fake news. Our experiment confirms the prediction power of MUFFLE outperforms general popularity prediction works.