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Deep Dynamic Convolutional Neural Network with Multi-Task Learning for Fake News Detection
|Issue Date: ||2020-03-02 11:38:27 (UTC+8)|
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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|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0106971004|
|Data Type: ||thesis|
|Appears in Collections:||[資訊科學系碩士在職專班] 學位論文|
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