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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
| dc.contributor.author | Yu-Hao Lee | en |
| dc.contributor.author | 李羽浩 | zh_TW |
| dc.date.accessioned | 2021-06-15T16:49:01Z | - |
| dc.date.available | 2020-08-25 | |
| dc.date.copyright | 2020-08-25 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-05 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53175 | - |
| dc.description.abstract | 近年來社群媒體興起加速了假新聞的傳播，因此假新聞偵測逐漸被視為一個重要的任務。有別於過去的研究大多將機器學習模型應用在假新聞偵測並以文字、圖片作為訓練資料，我們認為社群參與資料(Social Interaction Data)也能提供重要資訊。本研究利用線上新聞分享的社群參與資料(Online News Sharing Data)建立一個異質圖(Heterogeneous Graph)，並以圖神經網路模型(Graph Neural Network Model)進行假新聞預測。我們使用了三個推特(Twitter)上的假新聞資料集進行實驗，其中的資料包含原推文、回應推文、推文關係的結構、推文作者與原推文正確性標記等資訊。不同於文獻中的方法，將推文的文字內容特徵與在異質圖上的網路特徵分開進行預測，我們的模型將兩資料同時作為圖神經網路模型的訓練資料。實驗結果顯示，在假新聞偵測這個任務上，比起分開利用各項特徵進行預測，運用圖神經網路與社群參與資料有更突出的表現。 | zh_TW |
| dc.description.abstract | The rise of social media accelerates fake news propagation and worsen the fake news problem. Even though there have been many machine learning approaches proposed on fake news detection, most of them focused on text and image data. To leverage online social interaction data for fake news detection, we proposed a graph neural network model using a heterogeneous graph that captures online news sharing activities. We evaluated our models with three fake news datasets constructed based on news-worthy events on twitter. The datasets contain variables such as source tweets, reply tweets, tweets relation structures, and manually labeled ground truth for fake news. Except from previous works predicting fake news with text content features or embedding features on heterogeneous graph separately, our models combine both features as the training data and predict with graph neural network model. Experimental results show that our models perform better comparing with other methods predicting with text features and graph embedding features separately. The results suggest that graph neural network model and online news sharing data are helpful for fake news detection tasks. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T16:49:01Z (GMT). No. of bitstreams: 1 U0001-0508202011432300.pdf: 5554120 bytes, checksum: 1763ec91d2537d6ae0dba8adc2acc41a (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 口試委員審定書 I 誌謝 II 中文摘要 III ABSTRACT IV CHAPTER 1 INTRODUCTION 1 CHAPTER 2 LITERATURE REVIEW 5 2.1 FAKE NEWS DETECTION 5 2.1.1 Hierarchical structure for fake news on social media 5 2.1.2 Existing approaches for fake news detection 8 2.2 NETWORK REPRESENTATION MODELS 10 2.2.1 Graphs for Network Representation Tasks 10 2.2.2 Homogeneous graph’s embedding and GNN methods 12 2.2.3 Heterogeneous graph’s embedding and GNN methods 16 2.3 NETWORK REPRESENTATION MODELS ON FAKE NEWS DETECTION TASKS 19 2.4 RESEARCH GAPS 20 CHAPTER 3 MODEL DESIGN 22 3.1 HETGNN 22 3.1.1 Data Preprocess Section in HetGNN 24 3.1.2 Model Section in HetGNN 25 3.1.3 Objective and Training Setting for HetGNN 26 3.2 HETGNNPRE AND HETGNNEMB 27 3.2.1 Data Preprocess Section in HetGNNpre and HetGNNemb 29 3.2.2 Model Section in HetGNNpre and HetGNNemb 31 3.2.3 Objective and Training Setting for HetGNNpre and HetGNNemb 32 CHAPTER 4 EXPERIMENT AND DISCUSSION 33 4.1 DATASET 33 4.1.1 Online News Sharing Interaction Graph 34 4.1.2 PHEME 35 4.1.3 Twitter15 and Twitter16 36 4.2 EXPERIMENTAL DESIGN AND BASELINE MODELS 38 4.2.1 Experimental Design 38 4.2.2 Baseline Models Selection 40 4.2.3 Experiment Results 41 4.2.4 Analysis 52 CHAPTER 5 CONCLUSION AND FUTURE WORK 56 REFERENCE 58 | |
| dc.language.iso | en | |
| dc.title | 假新聞偵測：圖神經網路模型 | zh_TW |
| dc.title | Fake News Detection: A Graph Neural Network Approach | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林怡伶,簡宇泰 | |
| dc.subject.keyword | 假新聞偵測,圖神經網路,異質圖, | zh_TW |
| dc.subject.keyword | Fake News Detection,Graph Neural Network,Heterogeneous Graph, | en |
| dc.relation.page | 63 | |
| dc.identifier.doi | 10.6342/NTU202002446 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-05 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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