政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/128993

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/128993

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政大機構典藏 > 資訊學院 > 資訊科學系碩士在職專班 > 學位論文 > Item 140.119/128993

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題名:	以深度動態卷積神經網路實施多重任務學習偵測假新聞 Deep Dynamic Convolutional Neural Network with Multi-Task Learning for Fake News Detection
作者:	林佑駿 Lin, Yu-Chun
貢獻者:	胡毓忠 Hu, Yuh-Jong 林佑駿 Lin, Yu-Chun
關鍵詞:	假新聞深度學習社群媒體動態卷積神經網路多重任務學習 Fake News Detection Deep Learning Social Media Dynamic CNN Multi-Task Learning
日期:	2020
上傳時間:	2020-03-02 11:38:27 (UTC+8)
摘要:	傳統的假新聞偵測主要區分為知識庫比對、專家人工辨識與特徵機器學習等3大方式，但是隨著資料數據的日益龐大、新聞來源的多樣化以及惡意變造新聞的手法推層出新，傳統假新聞偵測方法已出現瓶頸，逐漸不敷現況使用，為了突破此一困境，於是出現以深度學習找尋未知特徵的偵測方式。以往深度學習受限於硬體效能，不易針對模型的調整與優化進行全方位驗測，所幸隨著科技進步與莫爾定律推演，硬體效能持續以指數性程度成長，進而使深度學習的研究邁向了全新領域。本論文除了研究如何以深度學習中的深度動態卷積神經網路進行假新聞偵測外，同時也探討超參數在深度學習中對於優化的影響及資料集特徵在模型中所扮演的角色。運用多重任務學習框架，將推文立場、假新聞偵測與假新聞驗證等3個任務相互搭配，分析各任務彼此之間的關連影響。另針對深度動態卷積神經網路在處理假新聞偵測應用問題上的特性進行分析。 Traditional fake news detection is mainly divided into three major methods, knowledge based comparison, expert manual identification, and feature machine learning. With the increasing data, the diversification of news sources, and the malicious method of altering news, the traditional methods of detecting fake news has become a bottleneck, and it is gradually inadequate to use it. To break through this dilemma, there is a detection method that uses deep learning to find unknown features. In the past, deep learning was limited by hardware performance, and it was not easy to conduct comprehensive testing for model adjustment and optimization. Fortunately, with the advancement of science and technology and Moore`s Law, hardware performance continued to grow exponentially, deep learning towards a whole new field. In addition to studying how to detect fake news with deep dynamic convolutional neural networks in deep learning, this paper also explores the impact of hyperparameters on deep learning optimization and the role of data set features in the model. Besides, a multi-task learning framework is used to match three tasks, such as tweet position, fake news detection, and fake news verification, to analyze the impact of each task on each other. It also analyzes the characteristics of the deep dynamic convolutional neural network in dealing with the application of fake news detection.
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描述:	碩士國立政治大學資訊科學系碩士在職專班 106971004
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0106971004
資料類型:	thesis
DOI:	10.6814/NCCU202000234
顯示於類別:	[資訊科學系碩士在職專班] 學位論文

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