偵測假新聞是一項十分艱鉅的任務，包含偵測假新聞(Rumour Detection)、假新聞追蹤(Rumour Tracking)及立場分類(Stance Classification)，從這些方法最終對假新聞作驗證(Rumour Verification)。欲做到辨識新聞的驗證以使讀者能閱讀到正確的新聞及資訊，本研究希望探索以多重任務學習(Multi-Task Learning, MTL)用於處理數量龐大的假新聞資料上，並比較與傳統深度學習的差異，達到自動辨識及判別假新聞的目的。
本研究使用RumourEval、PHEME兩種假新聞資料集來進行深度遞歸神經網路(Recurrent Neural Network, RNN)中的GRU（Gate Recurrent Unit）演算法實作，並進行多重任務學習的訓練，對假新聞進行分類，進而找出處理識別假新聞的最佳參數。最後透過各種模擬實驗來比較改良過後的深度學習演算法(即GRU)與傳統深度學習的差異，並依據實驗結果進行量化與質化的分析。

Detecting fake news is a very difficult task, including Rumour Detection,Rumour Tracking and Stance Classification, and finally leading to Rumour Verification. To identify the authenticity of news so that readers can read the correct news and information, this research hopes to explore the use of Multi-Task Learning technology for processing a large number of fake news datasets and compare it with traditional deep learning, to achieve the purpose of automatically identifying and distinguishing fake news.
This research uses two fake news datasets, RumourEval and PHEME,to implement the GRU (Gated Recurrent Unit) algorithm of the Recurrent Neural Network (RNN), and trains for multiple tasks to perform fake news classification to find the best parameters for handling fake news. Finally, through various simulation experiments, the differences between the improved and traditional deep learning algorithm will be compared, and quantitative and qualitative analysis is performed based on the experimental results

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