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.
Highlights
Doina LogofătuWith the boom of social media platforms, there are tens of millions of user-generated information on social media platforms every day [1]
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
Because our research focuses on the influence of fake news on social media platforms, i.e., fake news popularity prediction, we will introduce information cascade prediction and fake news detection in the following two sub-sections, respectively, and discuss them in detail
Summary
Doina LogofătuWith the boom of social media platforms, there are tens of millions of user-generated information on social media platforms every day [1]. Due to the development of online communities, the world is better connected than ever before. Users are connected to other users by an average separation of 3.57 2016/02/three-and-a-half-degrees-of-separation/) (accessed on 25 December 2021).The short communication distance and ease of access make online social media an increasingly popular venue for information sharing. The convenience and low cost of social networking are conducive to collective intelligence, but at the same time, it leads to a negative byproduct: the propagation of misinformation such as fake news. Fake news is defined as a kind of news story relaying intentionally false information on social media [2,3]. The Pew Research Center announced that approximately 79% of US adults get news from social media in 2020, compared to only 49% in 2012
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