為了減輕社群媒體上假新聞傳播所造成的損害，在本研究中的研究問題聚焦假新聞影響力的預測以及分析，透過分析多面向的資料特徵，預測一篇分享假新聞的貼文未來在該社群平台的熱門程度。為了達到本研究的研究目標，我們提出了一個全面的預測框架 MUFFLE，透過對新聞相關的社群網路結構、參與傳遞的使用者資料以及新聞文章的文本內容進行編碼，能夠精確萃取出影響預測結果的決策因子。此外，在模型中我們實作的Attention 機制也能提供可解釋性 (Explainability)，這對於後續的社群分析以及社會科學研究有重要的參考價值。為了印證我們提出的方法的有效性，本研究在具有指標性的真實資料集上進行詳盡的實驗。實驗結果顯示出本研究方法在Top-K之NDCG以及Hit Rate上皆優於目前最先進的popularity prediction 研究所提出的方法。此外，在現實應用中，本研究可以做為假新聞檢測的輔助工具。在防治偽造資訊流通的工作流中，如果一則新聞被預測識別為假新聞，MUFFLE可以估計其傳播的影響幅度，並作為更嚴格的人工審核的處理優先順序，進一步提高管理者的行政效能。

To mitigate the damage caused by diffusion of online fake news articles on social media platforms, 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 as well as content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. We conducted extensive experiments on real-world datasets and as a result, our proposed MUFFLE outperforms both state-of-the-art method of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Moreover, our work could be a complement tool of fake news detection. If a post is recognized (possibly, but not sure) as fake news by AI detection, MUFFLE can estimate its impacts of spreading, and remind the manager whether to conduct a rigorously manual review.

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