近年來，社群媒體逐漸成為人們交流資訊的主要平台，其資訊流通快速和參與者眾多的特點，使其成為傳播假新聞的絕佳媒介。此外，新聞在社交媒體上的傳播結構也成為了判斷該新聞是否為假的重要特徵。因此，在本論文中，我們將研究標的從新聞內容本身擴展到其在社群媒體上的傳播監督，提出了一個名為SGNN的假新聞偵測模型，該模型利用新聞內容和新聞在社交媒體上的傳播結構，融合兩種模態的資料進行假新聞偵測。我們使用現實生活中的數據集進行幾項實驗，而實驗結果表明，我們的模型優於現有的假新聞偵測模型。

Social media has gradually become the central platform for people to exchange information in recent years. The characteristics of rapid information circulation and gathering by many participants make it an excellent medium for disseminating fake news. Moreover, the dissemination structure of a piece of news on a social medium is an important feature for deciding whether the news is fake or not. Therefore, in this thesis, we proposed a new model named SGNN, which exploited the news content and the news's dissemination structure on a social medium for fake news detection. We conducted several experiments using real-life datasets. The experimental results showed that our model excelled the existing models for fake news detection.

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