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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 王志宇(Chih-Yu Wang) | |
| dc.contributor.author | Pei-Wen Sun | en |
| dc.contributor.author | 孫珮文 | zh_TW |
| dc.date.accessioned | 2022-11-25T03:07:00Z | - |
| dc.date.available | 2024-02-08 | |
| dc.date.copyright | 2022-02-21 | |
| dc.date.issued | 2022 | |
| dc.date.submitted | 2022-02-10 | |
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[19] Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. Modeling relational data with graph convolutional networks. In European Semantic Web Conference, pages 593–607, 2018. [20] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, pages 5998–6008, 2017. [21] Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. Graph attention networks. In International Conference on Learning Representations, 2018. [22] Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315, 2019. 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In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38–45, 2020. [27] Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. Big bird: Transformers for longer sequences. Advances in Neural Information Processing Systems, 33, 2020. [28] Xinyi Zhou, Jindi Wu, and Reza Zafarani. Safe: Similarity-aware multi-modal fake news detection. In 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020, pages 354–367, 2020. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81937 | - |
| dc.description.abstract | 隨著社群平台的興起，大量錯誤的醫療健康新聞流傳於網際網路上，當人們採取健康假訊息建議的偏方後，他們的生命可能會受到威脅。為了避免假新聞造成的負面影響，許多偵測的方法已被提出，例如，自然語言處理技術（NLP）能夠根據新聞的文字來判斷其真實性，然而由於當今人們時常從社群媒體接收新聞資訊，用戶的背景以及其對新聞的參與模式或許有助於假新聞的偵測，因此，研究學者引入圖神經網路（GNN）到此任務上。通常在一個社群網路中，每個節點對他相鄰的節點有不同的影響力，每種關係也有獨特的意義，有鑑於此，我們提出一個新穎、以階層式注意力機制為基礎的圖學習框架，以捕抓重要的節點和交互作用。另外，因為圖神經網路在多層堆疊時表現不佳，我們設計了兩階段的訓練策略，以縮短傳遞用戶交友圈之訊息到新聞節點的路徑。在辨別健康假新聞的任務上，實驗結果顯示我們的模型優於現有的方法，並且基於注意力機制的圖神經網路能受益於兩階段的訓練。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T03:07:00Z (GMT). No. of bitstreams: 1 U0001-0702202216280100.pdf: 5917098 bytes, checksum: d1059d1cf71a9a135b7e67cd13c1c24d (MD5) Previous issue date: 2022 | en |
| dc.description.tableofcontents | 口試委員會審定書 i 誌謝 ii 中文摘要 iii Abstract iv Contents vi List of Figures ix List of Tables x Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Contribution 3 1.3 Organization of Thesis 3 Chapter 2 Preliminary and Related Work 4 2.1 Preliminary 4 2.1.1 Heterogeneous Graph 4 2.1.2 Graph Neural Network 5 2.2 Related Work 5 2.2.1 Graph Neural Networks 5 2.2.2 Heterogeneous GNNs 6 2.2.3 Fake News Detection Methods 6 2.2.3.1 Text-based Approach 6 2.2.3.2 Graph-based Approach 7 2.2.3.3 Summary 7 Chapter 3 Dataset and Feature Engineering 8 3.1 Dataset 8 3.1.1 Overview of Dataset 8 3.1.2 Dataset Refinement 9 3.1.2.1 News 9 3.1.2.2 Tweets 10 3.2 Feature Engineering 10 3.2.1 Feature Processing 11 3.2.1.1 Text 11 3.2.1.2 Date and time 11 3.2.1.3 Category 12 3.2.1.4 Number 12 3.2.2 Feature Selection 13 3.2.2.1 Date and time 13 3.2.2.2 Category 13 3.2.2.3 Number 14 3.2.2.4 Summary 17 Chapter 4 Methodology 18 4.1 Graph Construction 18 4.2 Problem Definition 19 4.3 Model Architecture 19 4.3.1 Node Feature Transformation 20 4.3.2 Node-level Attention 21 4.3.3 Relation-level Attention 23 4.4 Two-stage Training 24 4.4.1 Motivation 24 4.4.2 Process of Two-stage Training 25 4.5 Loss Function 26 Chapter 5 Evaluation 27 5.1 Experimental Setup 27 5.1.1 Baselines 27 5.1.2 Dataset Splits and Evaluation Metrics 28 5.1.3 Implementation Details 29 5.2 Results 29 5.2.1 Fake Health News Detection 29 5.2.2 Two-stage Training 30 5.2.3 Ablation Study 31 Chapter 6 Conclusion and Future Work 33 6.1 Conclusion 33 6.2 Future Work 34 References 35 | |
| dc.language.iso | en | |
| dc.subject | 圖形神經網路 | zh_TW |
| dc.subject | 社群網路 | zh_TW |
| dc.subject | 健康新聞 | zh_TW |
| dc.subject | 注意力機制 | zh_TW |
| dc.subject | 假新聞偵測 | zh_TW |
| dc.subject | Attention Mechanism | en |
| dc.subject | Health News | en |
| dc.subject | Fake News Detection | en |
| dc.subject | Social Networks | en |
| dc.subject | Graph Neural Networks | en |
| dc.title | 偵測錯誤健康新聞的階層式圖注意力網路 | zh_TW |
| dc.title | Hierarchical Graph Attention Network for Fake Health News Detection | en |
| dc.date.schoolyear | 110-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 謝宏昀(Hung-Yun Hsieh) | |
| dc.contributor.oralexamcommittee | 王釧茹(Ching-Yu Chen),蘇黎(Hsiu-Jung Chen),(Yihru Cheng) | |
| dc.subject.keyword | 健康新聞,假新聞偵測,社群網路,圖形神經網路,注意力機制, | zh_TW |
| dc.subject.keyword | Health News,Fake News Detection,Social Networks,Graph Neural Networks,Attention Mechanism, | en |
| dc.relation.page | 39 | |
| dc.identifier.doi | 10.6342/NTU202200334 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2022-02-11 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資料科學學位學程 | zh_TW |
| dc.date.embargo-lift | 2024-02-08 | - |
| 顯示於系所單位： | 資料科學學位學程 | |
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| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-0702202216280100.pdf | 5.78 MB | Adobe PDF | 檢視/開啟 |
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