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Title: 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
Authors: 林佑駿
Lin, Yu-Chun
Contributors: 胡毓忠
Hu, Yuh-Jong
Lin, Yu-Chun
Keywords: 假新聞
Fake News
Deep Learning
Social Media
Dynamic CNN
Multi-Task Learning
Date: 2020
Issue Date: 2020-03-02 11:38:27 (UTC+8)
Abstract: 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。
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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