網路謠言的危害有目共睹，對社會、經濟與政治造成巨大負面影響，有關當局與社群平台莫不投注大量心力，期能遏止其擴散。有鑑於此，本論文將發展有效的網路謠言預測方法，以抑制網路謠言的散播，維護網路社群的正常運作。首先，我們分析網路謠言的散布模式，發現謠言討論串的長度很低，但數量龐大，明顯以廣度優於深度方式來傳遞。其次，分析推特(twitter)中發文者的個人資訊與文章特徵，發展出15種偵測屬性。接下來，為配合不同學習方法，我們將來源資料集進行轉換，產生一維與二維資料集。最後，配合發文文字分析，以多種深度學習與非深度學習方法進行實驗。為驗證提出方法之有效性，本論文使用實際的謠言資料集進行實驗，結果顯示: 只考慮來源發文，以傳統文字剖析方式建立資料集，不論配合深度或非深度學習方法，可獲得較佳的偵測結果。當考慮來源發文及其回應串，偵測結果便明顯降低，且非深度學習方法明顯優於深度學習方法，顯示回應串可能干擾謠言的判讀，無助於偵測結果的提升。使用本研究提出之屬性集配合文字性特徵，並以Multi-Tasking Learning方法塑模，可獲得穩定之最佳結果。根據本論文的研究成果，為網路謠言偵測提供更深入的了解，除可提供相關單位有效的決策依據，亦有助於未來相關方法的研發。

The damage of Internet rumors is obvious to everyone. It has have large adverse effect on society, economy, and politics. The government departments and social media platforms have been dedicated to stopping its spreading for a long time. In light of this, this paper developed effective internet rumor detection method for stopping the spread of rumors and maintaining the good order on social media. First, we analyze the spreading pattern of rumors on social media and find the length of rumor-threads are not long but the quantity are large, so we consider the rumors spread is in related with “breadth” instead of “depth”. Second, according to the information of tweets, we design 15 attributes for detecting rumor. Moreover, to apply on our machine learning and deep learning, we process our dataset and develop 1-D and 2-D datasets. Finally, we make experiment by using the things mentioned above and text. The results show that if we just use the source tweets to make experiment with text, we can get better results. But if we affiliate the reaction tweets, the result become worse. It means that reaction tweet would hinder the detection of rumor. It’s not helpful for our outcome. With our attributes and text in paper, and build the model with the Multi-task Learning, we can get the best outcome. According our achievement on research, we can provide a better understanding of internet rumor detection. Not only will we provide the concerned department the basis of making the effective decision, but help them devise the new approach of internet rumor detection in the future.

目錄
第一章	緒論	1
第二章	相關技術與文獻探討	5
2.1	網路謠言(Internet Rumors)	5
2.2	謠言偵測(Rumor Detection)	6
2.3	塑模方法: 非深度學習方法	8
2.4	謠言偵測之塑模方法: 深度學習方法	11
2.4.1	Multilayer Perceptron (MLP)	13
2.4.2	Recurrent Neural Network (RNN)	14
2.4.3	Long Short Term Memory(LSTM)	15
2.4.4	Multi-tasking learning	17
第三章	網路謠言偵測方法	18
3.1	謠言傳播模式分析	18
3.2	偵測屬性設計	24
3.3	謠言偵測方法	27
3.3.1   資料集的建立	27
3.3.2   謠言偵測流程	33
第四章	實驗結果	36
4.1	資料來源	36
4.2	評量指標	37
4.3	謠言偵測之實驗結果	38
4.3.1	非文字特徵資料集的實驗結果	38
4.3.2	文字特徵資料集之實驗結果	40
4.4	混合型資料集	43
第五章	結論與未來工作	45
參考文獻	47

圖目錄
圖2-1: 巢狀交叉驗證法	9
圖2-2: 決策樹範例	10
圖2-3: ReLU啟動函數	11
圖2-4: Sigmoid啟動函數	12
圖2-5: 多層感知機常見架構	14
圖2-6: RNN架構圖	14
圖2-7:  LSTM結構圖	15
圖2-8:  Multi-Tasking Learning架構圖	17
圖3-1: 討論串在推特上的示意圖	19
圖3-2: PHEME資料集中各事件在Twitter中對應之發文(tweets)總數	20
圖3-3: PHEME資料庫中所有謠言(rumor)討論串之長度統計	21
圖3-4: PHEME資料庫中所有非謠言(non-rumor)討論串之長度統計	22
圖3-5: Ferguson事件中所有謠言(rumor)討論串之長度統計	22
圖3-6: Ferguson事件中所有非謠言(non-rumor)討論串之長度統計	23
圖3-7: Ferguson事件中之一個長度超過10之討論串	24
圖3-8: 手寫數字辨識之二維表示格式	28
圖3-9: 多則討論串在推特上的示意圖	30
圖3-10: 1-D文字特徵資料集	32
圖3-11: 2-D文字特徵資料集	32
圖3-12: 本研究之謠言偵測流程	33
圖3-13: 分割資料集	33


表目錄
表2-1 謠言偵測之相關研究	7
表3-1: 討論串(Discussion Threads)之說明範例	19
表3-2: 在各事件中，謠言與非謠言討論串所佔的比例	21
表3-3: 本研究提出之謠言偵測屬性集	26
表3-4: 針對來源推特(s1...sn)產生的1D資料表範例	27
表3-5:  針對討論串(以Sm為來源推特)產生之2D資料集	29
表4-1: PHEME謠言資料集之統計資料	36
表4-2: 混淆矩陣(Confusion Matrix)	37
表4-3: 1-D非文字資料集(僅使用15個偵測屬性)之偵測結果	39
表4-4: 2-D非文字資料集(僅使用15個偵測屬性)之偵測結果	40
表4-5: 1-D文字資料集之偵測結果	41
表4-6: 二維(2D)文字特徵資料集之偵測結果	43
表4-7: 使用Multi-task Learning 對1-D與2-D資料集進行實驗	44

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