ExoFIA: Deep Exogenous Assistance in the Prediction of the Influence of Fake News with Social Media Explainability

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.

Keywords

• fake news
• popularity prediction
• exogenous factors
• multi-modal learning
• model interpretability