使用圖神經網路偵測 PTT 的低活躍異常帳號

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姓名

王瑞緣(JUI-YUAN WANG) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用圖神經網路偵測 PTT 的低活躍異常帳號
(Using Graph Neural Networks to Detect Inactive Spammers on PTT)

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摘要(中)

隨著社群媒體的興起，雇用「公關公司」在網路上散播不實消息，成
為左右時事輿論的新興手法，公關公司的大量帳號，常被各大論壇視為
異常帳號。國內外皆有學者以深度學習偵測異常帳號，但我們發現，現
階段偵測異常帳號的論文中，並沒有針對帳號的活躍程度作探討。
本篇論文中，我們依據帳號在限定時間內的活動次數定義出「活
躍值」的概念，我們觀察到，用簡單的卷積類神經網路 (Convolutional
Neural Network) 模型，即可在偵測高活躍異常帳號的任務中達到 0.9169 的 ROC 曲線下的面積 (AUROC)，但是偵測低活躍的異常帳號卻只有 0.7830，顯示出偵測低活躍異常帳號是非常棘手的任務。我們利用使用者與使用者之間的關係建立社群網路，以提供額外的特徵作為訓練的資料，
並引入圖神經網路，成功改善偵測低活躍異常帳號的任務。

摘要(英)

With the rise of social media, hiring public relations companies to spread fake news on the Internet has become an emerging method to manipulate public opinions. These large number of accounts owned by public relations companies are regarded as spammers by most online forums. Researchers have used deep learning techniques to detect abnormal accounts.
However, we found that these studies likely conducted experiments mainly on the active users.
In this thesis, we define the concept of ”Active Value” based on the number of activities of an account within a unit period. For active users, even a simple Convolutional Neural Network model can distinguish a spammer from a regular user: the area under the ROC curve (AUROC) achieves
0.9169. However, for the inactive users, the score drops to 0.7830. The result indicates that detecting inactivity spammers is much more challenging. We use user-to-user relationships to build a social network. We apply graph neural networks to the social network and extract additional social features as training clues. Experimental results show that these strategies better distinguish the spammers from regular users, especially when these users have limited activities.

關鍵字(中)

★ 網軍
★ 異常帳號
★ 低活躍異常帳號
★ 低活躍帳號
★ 圖神經網路
★ 批踢踢
★ 批踢踢實業坊

關鍵字(英)

★ spammer
★ social networks
★ inactive user
★ inactive spammer
★ graph convolutional networks
★ PTT

論文目次

目錄
頁次
摘要 iv
Abstract v
致謝 vii
目錄 viii
圖目錄 xi
表目錄 xiii
一、緒論 1
二、相關研究 3
2.1 不同網路平台上的異常帳號 .......................................... 3
2.2 偵測異常帳號的研究方法 ............................................. 4
2.3 偵測台灣 PTT 異常帳號 .............................................. 5
三、研究模型及方法 7
3.1 活躍值 (Active Value).................................................. 7
3.2 資料集 ..................................................................... 7
3.2.1 PTT 的介紹及統計數字 ...................................... 7
3.2.2 PTT 官方認定的異常帳號 ................................... 8
3.2.3 帳號的篩選機制 ................................................ 8
viii
目錄目錄
3.3 訓練特徵 .................................................................. 10
3.3.1 帳號參與的文章的總留言數 ................................. 10
3.3.2 帳號參與的文章的推噓總分 ................................. 12
3.3.3 帳號的活動時間 ................................................ 13
3.4 GNN 模型介紹........................................................... 14
3.4.1 Graph Convolutional Networks ............................. 14
3.4.2 Topology Adaptive Graph Convolutional Networks.... 16
3.4.3 Graph Attention Network .................................... 17
四、實驗結果 21
4.1 實驗設置 .................................................................. 21
4.1.1 參數設置 ......................................................... 21
4.1.2 比較模型 ......................................................... 22
4.1.3 評估指標 ......................................................... 23
4.2 實驗結果與討論 ......................................................... 24
4.2.1 「偵測高活躍異常帳號」與「偵測低活躍異常帳號」
是否難度相同? ........................................................... 25
4.2.2 GNN-Method 是否有成功改善「偵測低活躍異常帳
號」的任務 ............................................................... 33
4.2.3 在 Baseline 加入 Social Network 的特徵，是否也能
夠改善「偵測低活躍異常帳號」的任務? .......................... 34
4.2.4 GNN-Method 模型加入 Social Network 的特徵，是
否在「偵測低活躍異常帳號」的任務中表現更為出色? ........ 38
4.2.5 模型認為前 K 個最有可能為異常的帳號，用 F1-
Score, Recall 與 Precision 評估效能。 ............................. 43
4.2.6 為什麼 F1-Score 並不會隨著活躍值上升?................ 55
五、總結與未來展望 56
ix
目錄目錄
參考文獻 57
附錄 59

參考文獻

參考文獻
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指導教授

陳弘軒

審核日期

2022-7-19

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