由於網路的便利，有許多社群網站或網路新聞會大量傳播錯誤的假新聞，這些假新聞會引起社會的恐慌和不安，或達成政治上的目的。自動假新聞偵測可以快速的對假新聞進行分類，並在事件發生時迅速幫助社會澄清訊息的真假，而無需進行長時間繁雜的人工檢查。通過分析群眾或新聞立場來幫助判斷新聞的真實性是目前主流的方法之一。因此，立場偵測已成為近年來受到重視的研究領域，如何準確的檢測立場已成為檢測假新聞的首要目標。
這項研究的目標是準確地偵測新聞的立場，以指導對假新聞的識別。本文提出了一種基於預訓練的BERT語言模型的立場偵測網路，BERT在外部語料庫上使用無監督的學習訓練，從而獲得通用的語言知識。近年來，基於BERT的遷移學習方法已被廣泛使用，並取得了優異的成績。下游任務可以受益於預訓練模型中所學到的先驗語言知識。先前的大多數的方法在對立場進行分類時僅使用了單一方向的推理資訊，這可能會遺漏一些重要信息。因此，我們提出了一種雙向推理立場偵測模型，該模型可以利用雙向觀點的訊息來總結出更加全面的資訊來幫助立場分類。最後，我們將立場偵測任務定義為階層結構的任務，並使用階層分類系統以及加入文本的主題資訊來幫助立場的分類。實驗結果表明，我們的模型能夠更加準確地分類立場。

Because of the convenience of the Internet, there are many websites or online news spread misinformation, cause panic and trepidation in society. Automatic fake news detection can classify fake news and help the society to clarify the information is true or false without human checking. Detecting fake news by analyzing the stance is one of the mainstream methods, stance detection has become a new popular research field in recent years. How to accurately detect stance has become the primary goal of detecting fake news.
This research aims to detect the news stance accurately, and we propose a method based on a pre-trained BERT language model. Most of the previous work only used the knowledge of single inference direction when classifying the stance, which may lose some important information. Therefore, we propose a bidirectional inference stance detection model, which can leverage bidirectional perspective information to classify the stance more comprehensively. We also define the stance detection task as a hierarchical structure task, and use the hierarchical classification and incorporate the topic information to help the stance classification. Experiment results show that our model can classify the stance more accurately.

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