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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 2-Mar-2020 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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social media: A data mining perspective," ACM SIGKDD Explorations
Newsletter, vol. 19, no. 1, pp. 22-36, 2017.
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satirical cues to detect potentially misleading news," in Proceedings of the
second workshop on computational approaches to deception detection, 2016,
pp. 7-17.
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news sources and political attitudes of inefficacy, alienation, and cynicism,"
Communication Research, vol. 41, no. 3, pp. 430-454, 2014.
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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
dc.contributor.advisor 胡毓忠zh_TW
dc.contributor.advisor Hu, Yuh-Jongen_US
dc.contributor.author (Authors) 林佑駿zh_TW
dc.contributor.author (Authors) Lin, Yu-Chunen_US
dc.creator (作者) 林佑駿zh_TW
dc.creator (作者) Lin, Yu-Chunen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-Mar-2020 11:38:27 (UTC+8)-
dc.date.available 2-Mar-2020 11:38:27 (UTC+8)-
dc.date.issued (上傳時間) 2-Mar-2020 11:38:27 (UTC+8)-
dc.identifier (Other Identifiers) G0106971004en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/128993-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系碩士在職專班zh_TW
dc.description (描述) 106971004zh_TW
dc.description.abstract (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。		zh_TW
dc.description.abstract (摘要) 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.		en_US
dc.description.tableofcontents 目錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
表目錄 vii
圖目錄 viii
第一章 導論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 研究貢獻 4
第二章 研究背景 6
2.1 假新聞定義 6
2.1.1 社群媒體中的假新聞 7
2.2 假新聞特徵 8
2.2.1 社群媒體假新聞特徵 10
2.3 假新聞分類方法 12
2.3.1 假新聞偵測 14
2.3.2 推文立場 14
2.3.3 假新聞驗證 15
2.4 卷積神經網路 16
2.4.1 卷積神經網路應用於文本分類 17
2.4.2 動態卷積神經網路 18
2.5 自然語言處理 20
2.6 多重任務學習 22
第三章 相關研究 27
3.1 假新聞分類相關研究 27
第四章 研究方法與架構 28
4.1 假新聞資料集 29
4.1.1 PHEME 資料集 30
4.1.2 RumourEval 資料集 32
4.1.3 資料前處理 34
4.2 模型驗證 34
4.3 動態卷積網路建模 36
4.4 多重任務學習 36
第五章 研究實作與結果 38
5.1 實驗環境 38
5.2 實做過程 39
5.3 實驗結果 40
5.3.1 多任務深度動態卷積網路學習 40
5.3.2 各類模型比較 46
5.4 分析研討 48
5.4.1 資料集規模 48
5.4.2 動態池化參數 49
5.4.3 多重任務學習 49
5.4.4 深度動態卷積神經網路模型特色 50
第六章 結論與未來展望 52
6.1 研究結論 52
6.2 未來展望 53
參考文獻 55		zh_TW
dc.format.extent 1858156 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106971004en_US
dc.subject (關鍵詞) 假新聞zh_TW
dc.subject (關鍵詞) 深度學習zh_TW
dc.subject (關鍵詞) 社群媒體zh_TW
dc.subject (關鍵詞) 動態卷積神經網路zh_TW
dc.subject (關鍵詞) 多重任務學習zh_TW
dc.subject (關鍵詞) Fake Newsen_US
dc.subject (關鍵詞) Detectionen_US
dc.subject (關鍵詞) Deep Learningen_US
dc.subject (關鍵詞) Social Mediaen_US
dc.subject (關鍵詞) Dynamic CNNen_US
dc.subject (關鍵詞) Multi-Task Learningen_US
dc.title (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zh_TW
dc.title (題名) Deep Dynamic Convolutional Neural Network with Multi-Task Learning for Fake News Detectionen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A convolutional neural
network for modelling sentences," arXiv preprint arXiv:1404.2188, 2014.
[2] Z. Tufekci, "It’s the (democracy-poisoning) Golden Age of free speech,"
WIRED. Accessed May, vol. 20, p. 2018, 2018.
[3] E. Hunt, "What is fake news? How to spot it and what you can do to stop it,"
The Guardian, vol. 17, 2016.
[4] K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, "Fake news detection on
social media: A data mining perspective," ACM SIGKDD Explorations
Newsletter, vol. 19, no. 1, pp. 22-36, 2017.
[5] V. Rubin, N. Conroy, Y. Chen, and S. Cornwell, "Fake news or truth? using
satirical cues to detect potentially misleading news," in Proceedings of the
second workshop on computational approaches to deception detection, 2016,
pp. 7-17.
[6] M. Balmas, "When fake news becomes real: Combined exposure to multiple
news sources and political attitudes of inefficacy, alienation, and cynicism,"
Communication Research, vol. 41, no. 3, pp. 430-454, 2014.
[7] E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini, "The rise of
social bots," Communications of the ACM, vol. 59, no. 7, pp. 96-104, 2016.
[8] J. Cheng, M. Bernstein, C. Danescu-Niculescu-Mizil, and J. Leskovec,
"Anyone can become a troll: Causes of trolling behavior in online
discussions," in Proceedings of the 2017 ACM conference on computer
supported cooperative work and social computing, 2017: ACM, pp. 1217-
1230.
[9] Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia, "Detecting automation of
twitter accounts: Are you a human, bot, or cyborg?," IEEE Transactions on
Dependable and Secure Computing, vol. 9, no. 6, pp. 811-824, 2012.
[10] M. Del Vicario et al., "Echo chambers: Emotional contagion and group
polarization on facebook," Scientific reports, vol. 6, p. 37825, 2016.
[11] W. Quattrociocchi, A. Scala, and C. R. Sunstein, "Echo chambers on
Facebook," Available at SSRN 2795110, 2016.
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dc.identifier.doi (DOI) 10.6814/NCCU202000234en_US