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題名 應用可解釋的遞歸神經網路於社群媒體中的假新聞辨識
XFlag: explainable fake news detection model on social media作者 楊程鈞
Yang, Cheng-Jun貢獻者 簡士鎰<br>郁方
Chien, Shih-Yi<br>Fang, Yu
楊程鈞
Yang, Cheng-Jun關鍵詞 可解釋人工智慧
逐層相關性傳播演算法
SAT
透明度
假新聞偵測
長短期記憶
社群媒體
XAI
LRP
SAT
Transparency
Fake news detection
LSTM
Social media日期 2022 上傳時間 10-Feb-2022 12:53:38 (UTC+8) 摘要 社群媒體成為了現今快速散播新聞的管道，只需透過電腦、行動裝置上網，人人都可以便利地瀏覽當天的最新消息。不過，這同時也是一把雙刃劍，有別於傳統媒體，大眾可以輕易地在網路中傳播資訊，而不需要受到查核機構的管制，這使得網路中的新聞來源混雜且難以辨別其真偽，假新聞的氾濫嚴重影響了人們信任網絡資訊的意圖與行為。為了解決問題，近期的研究提出利用人工智慧技術來發展假新聞偵測模型，然而，他們大多著重於如何提升人工智慧模型的效能(如準確率)，而忽略了資訊透明度的議題。因此，本研究提出了創新的可解釋人工智慧(Explainable AI)框架XFlag。其可分為三個階段，首先訓練長短期記憶模型(Long short-term memory)來偵測社群媒體中的假新聞文章；接著以逐層相關性傳播演算法(Layer-wise relevance propagation)分析訓練好的偵測模型，產生對於預測結果的解釋向量；最後，由於未經處理的數學向量對於一般使用者是難以解讀的，我們以SAT模型(Situation awareness-based agent transparency)將解釋向量與預測結果設計為使用者容易理解的人機介面，提升人與人工智慧系統之間的資訊透明度。本研究透過線上的使用者研究驗證XFlag的有效性，其結果表明相較於黑盒子般的預測結果，此框架可以更好地提升系統透明度，讓使用者了解偵測模型背後的邏輯，進而解決社群媒體中的假新聞議題。更進一步來說，XFlag能夠幫助使用者以很小的認知工作量，來理解系統目標、判別系統決策和預測系統的不確定性。
Social media platforms provide an easy and rapid approach for news consumption. They allow any individual to disseminate information without third-party restrictions (such as fact-checking), making it difficult to verify the authenticity of a source. The proliferation of fake news has severely affected people’s intentions and behaviors in trusting online sources. Applying AI approaches for fake news detection on social media is the focus of much recent research, most of which, however, focuses on enhancing AI performance (such as accuracy). In contrast, in this study we propose XFlag, an innovative explainable AI (XAI) framework which uses long short-term memory (LSTM) to identify fake news articles, a layer-wise relevance propagation (LRP) algorithm to explain the fake news detection model based on LSTM, and a situation awareness-based agent transparency (SAT) model to increase transparency in human–AI interaction. The proposed framework has been empirically validated via online user studies, the results of which confirm that the XFlag framework is effective in resolving the fake news problems on social media by enhancing system transparency and enabling a user to understand the logic behind an AI model. The research findings suggest that the use of XFlag supports users in understanding system goals (i.e., perception), justifying system decisions (i.e., comprehension), and predicting system uncertainty (i.e., projection), with little cost of perceived cognitive workload.參考文獻 Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138-52160.Allcott, H., & Gentzkow, M. (2017). Social media and fake news in the 2016 election. Journal of economic perspectives, 31(2), 211-236.Arras, L., Arjona-Medina, J., Widrich, M., Montavon, G., Gillhofer, M., Müller, K.-R., Hochreiter, S., & Samek, W. (2019). 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國立政治大學
資訊管理學系
108356018資料來源 http://thesis.lib.nccu.edu.tw/record/#G0108356018 資料類型 thesis dc.contributor.advisor 簡士鎰<br>郁方 zh_TW dc.contributor.advisor Chien, Shih-Yi<br>Fang, Yu en_US dc.contributor.author (Authors) 楊程鈞 zh_TW dc.contributor.author (Authors) Yang, Cheng-Jun en_US dc.creator (作者) 楊程鈞 zh_TW dc.creator (作者) Yang, Cheng-Jun en_US dc.date (日期) 2022 en_US dc.date.accessioned 10-Feb-2022 12:53:38 (UTC+8) - dc.date.available 10-Feb-2022 12:53:38 (UTC+8) - dc.date.issued (上傳時間) 10-Feb-2022 12:53:38 (UTC+8) - dc.identifier (Other Identifiers) G0108356018 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/138885 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 108356018 zh_TW dc.description.abstract (摘要) 社群媒體成為了現今快速散播新聞的管道，只需透過電腦、行動裝置上網，人人都可以便利地瀏覽當天的最新消息。不過，這同時也是一把雙刃劍，有別於傳統媒體，大眾可以輕易地在網路中傳播資訊，而不需要受到查核機構的管制，這使得網路中的新聞來源混雜且難以辨別其真偽，假新聞的氾濫嚴重影響了人們信任網絡資訊的意圖與行為。為了解決問題，近期的研究提出利用人工智慧技術來發展假新聞偵測模型，然而，他們大多著重於如何提升人工智慧模型的效能(如準確率)，而忽略了資訊透明度的議題。因此，本研究提出了創新的可解釋人工智慧(Explainable AI)框架XFlag。其可分為三個階段，首先訓練長短期記憶模型(Long short-term memory)來偵測社群媒體中的假新聞文章；接著以逐層相關性傳播演算法(Layer-wise relevance propagation)分析訓練好的偵測模型，產生對於預測結果的解釋向量；最後，由於未經處理的數學向量對於一般使用者是難以解讀的，我們以SAT模型(Situation awareness-based agent transparency)將解釋向量與預測結果設計為使用者容易理解的人機介面，提升人與人工智慧系統之間的資訊透明度。本研究透過線上的使用者研究驗證XFlag的有效性，其結果表明相較於黑盒子般的預測結果，此框架可以更好地提升系統透明度，讓使用者了解偵測模型背後的邏輯，進而解決社群媒體中的假新聞議題。更進一步來說，XFlag能夠幫助使用者以很小的認知工作量，來理解系統目標、判別系統決策和預測系統的不確定性。 zh_TW dc.description.abstract (摘要) Social media platforms provide an easy and rapid approach for news consumption. They allow any individual to disseminate information without third-party restrictions (such as fact-checking), making it difficult to verify the authenticity of a source. The proliferation of fake news has severely affected people’s intentions and behaviors in trusting online sources. Applying AI approaches for fake news detection on social media is the focus of much recent research, most of which, however, focuses on enhancing AI performance (such as accuracy). In contrast, in this study we propose XFlag, an innovative explainable AI (XAI) framework which uses long short-term memory (LSTM) to identify fake news articles, a layer-wise relevance propagation (LRP) algorithm to explain the fake news detection model based on LSTM, and a situation awareness-based agent transparency (SAT) model to increase transparency in human–AI interaction. The proposed framework has been empirically validated via online user studies, the results of which confirm that the XFlag framework is effective in resolving the fake news problems on social media by enhancing system transparency and enabling a user to understand the logic behind an AI model. The research findings suggest that the use of XFlag supports users in understanding system goals (i.e., perception), justifying system decisions (i.e., comprehension), and predicting system uncertainty (i.e., projection), with little cost of perceived cognitive workload. en_US dc.description.tableofcontents CHAPTER 1 INTRODUCTION 7CHAPTER 2 RELATED WORK 132.1 Fake news detection on social media 132.2 Explainable artificial intelligence (XAI) 152.3 Fake news flagging mechanism 162.4 Situation awareness-based agent transparency (SAT) 18CHAPTER 3 XFLAG: EXPLAINABLE FAKE NEWS DETECION AND INTERFACE SYNTHESIS 203.1 Detection model 213.1.1 Feature selection 223.1.2 Long short-term memory (LSTM) construction 233.2 Explanation model 243.2.1 Computation of feature explanation 243.2.2 User explanation systhesis 27CHAPTER 4 MODEL VALIDATION 304.1 Dataset 304.2 Experiment setup 314.3 Fake news detection performance 314.4 LRP explanation 324.5 Relevance validation 354.6 Cross-validation with Twitter dataset 37CHAPTER 5 USER STUDY 395.1 First-round user study 395.2 Second-round user study 415.2.1 Pilot test—Source validation 415.2.2 Online survey user study—Experimental designs and conditions 42CHAPTER 6 RESULTS 456.1 Perceived news authenticity of different XFlag conditions 456.2 Calibration of trust beliefs 466.3 System understandability and explainability in SAT approaches 476.4 Trust and workload in SAT approaches 486.5 Feature importance and user preferences 49CHAPTER 7 DISCUSSION 517.1 Development of XFlag framework 517.2 SAT model in source authenticity 537.3 User perception in XFlag framework 55CHAPTER 8 CONCLUSION AND FUTURE WORK 57REFERENCES 59 zh_TW dc.format.extent 1978537 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0108356018 en_US dc.subject (關鍵詞) 可解釋人工智慧 zh_TW dc.subject (關鍵詞) 逐層相關性傳播演算法 zh_TW dc.subject (關鍵詞) SAT zh_TW dc.subject (關鍵詞) 透明度 zh_TW dc.subject (關鍵詞) 假新聞偵測 zh_TW dc.subject (關鍵詞) 長短期記憶 zh_TW dc.subject (關鍵詞) 社群媒體 zh_TW dc.subject (關鍵詞) XAI en_US dc.subject (關鍵詞) LRP en_US dc.subject (關鍵詞) SAT en_US dc.subject (關鍵詞) Transparency en_US dc.subject (關鍵詞) Fake news detection en_US dc.subject (關鍵詞) LSTM en_US dc.subject (關鍵詞) Social media en_US dc.title (題名) 應用可解釋的遞歸神經網路於社群媒體中的假新聞辨識 zh_TW dc.title (題名) XFlag: explainable fake news detection model on social media en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Adadi, A., & Berrada, M. 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