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題名 社群媒體異常帳號探勘系統的設計與實作
Design and Implementation of the Intelligent Discovery System to Explore Malicious Accounts over Social Media
作者 蕭君弘
Hsiao, Chun-Hung
貢獻者 沈錳坤
Shan, Man-Kwan
蕭君弘
Hsiao, Chun-Hung
關鍵詞 異常帳號
智慧型探勘系統
以例探索
Malicious Account
Intelligent Discovery System
Explore By Examples
日期 2020
上傳時間 2-Sep-2020 13:15:45 (UTC+8)
摘要 近年來社群媒體越來越興盛，改變了大眾接收資訊的習慣。社群媒體上的話語權也成了不同陣營攻防的重點。因此社群媒體上開始出現越來越多行為可疑的異常帳號，試圖操弄輿論引導風向，藉此獲得自身的利益。
本論文研究開發一個社群媒體上的異常帳號探勘分析，透過發文及留言互動行為探索分析異常帳號。本系統的核心精神為由已知的異常帳號找出其他未知異常帳號。系統提供兩大主要功能Explore By Examples及Analysis By Examples。前者著重在探索異常帳號、後者著重在分析異常行為。兩大功能都由作息、來源IP、回應行為、網絡四個面向來探索分析，以協助使用者探索出異常帳號。本論文以台灣最熱門的本土社群媒體平台PTT BBS資料進行實證，並根據PTT官方公告異常帳號運用本系統進行案例分析。
In recent years, with the growth of social media, public`s habits of receiving information have been changed. More and more malicious accounts appear on social media. These malicious accounts try to manipulate public opinion for spin control to gain their own interests.
This thesis aims at the design and implementation of an intelligent discovery system to explore malicious accounts over social media. The core spirit of the developed system lies in the exploration of unknown malicious accounts by known malicious accounts over social media. Two main functions provided by the developed system are exploration by examples and analysis by examples. While the former focuses on exploration of unknown malicious accounts, the latter focuses on the analysis of known ones. To explore and analyze malicious account, four aspects of malicious behaviors are considered, namely activity, IP address, like/dislike behaviors, and network. The developed system is verified on the data collected from PTT bulletin board system, which is the most popular domestic social media service in Taiwan. Four case studies are performed to demonstrate the superiority of the developed intelligent discovery system.
參考文獻 [1] K. S. Adewole, N. B. Anuar, A. Kamsin, K. D. Varathan and S. A. Razak, Malicious Accounts: Dark of the Social Networks, Journal of Network and Computer Applications, 79, pp. 41-67, 2016.
[2] A. Badawy, E. Ferrara and K. Lerman, Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign, The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 258-265, 2018.
[3] N. Chavoshi, H. Hamooni and A. Mueen, Temporal Patterns in Bot Activities, International Conference on World Wide Web Companion, pp. 1601-1606, 2017.
[4] N. Chavoshi, H. Hamooni, and A. Mueen, DeBot: Twitter Bot Detection via Warped Correlation, IEEE International Conference on Data Mining, pp. 817-822, 2016.
[5] N. Chavoshi, H. Hamooni, and A. Mueen, Identifying Correlated Bots in Twitter, International Conference on Social Informatics, pp. 14-21, 2016.
[6] L. A. Cornelissen, R. J. Barnett, P. Schoonwinkel, B. D. Eichstadt and H. B. Magodla, A Network Topology Approach to Bot Classification, Annual Conference of the South African Institute of Computer Scientists and Information Technologists, pp. 79-88, 2018.
[7] C. A. Davis, O. Varol, E. Ferrara, A. Flammini and F. Menczer, BotOrNot: A System to Evaluate Social Bots, International Conference Companion on World Wide Web, pp. 273-274, 2016.
[8] A. Duh, M. S. Rupnik, and D. Korošak, Collective Behavior of Social Bots Is Encoded in Their Temporal Twitter Activity, Big Data 6, 2, pp. 113-123, 2018.
[9] S. Gokalp, M. Temkit, H. Davulcu, and I.H. Toroslu, Partitioning and Scaling Signed Bipartite Graphs for Polarized Political Blogosphere, IEEE International Conference on Social Computing, pp. 168-173, 2013.
[10] S. Gupta, P. Kumaraguru, and T. Chakraborty, MalReG: Detecting and Analyzing Malicious Retweeter Groups, ACM India Joint International Conference on Data Science and Management of Data, pp. 61-69, 2019.
[11] M. Jiang, P. Cui and C. Faloutsos, Suspicious Behavior Detection: Current Trends and Future Directions, IEEE Intelligent Systems, 31, 1, pp. 31-39, 2016.
[12] A. Karataş and S. Şahin, A Review on Social Bot Detection Techniques and Research Directions, International Security and Cryptology Conference, Turkey, pp. 156-161, 2017.
[13] T. Khaund, K. K. Bandeli, M. N. Hussain, A. Obadimu, S. Al-Khateeb and N. Agarwal, Analyzing Social and Communication Network Structures of Social Bots and Humans, The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 794-797, 2018.
[14] S. Kudugunta and E. Ferrara, Deep Neural Networks for Bot Detection, Information Sciences, 467, pp. 312-322, 2018.
[15] T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, International Conference on Learning Representations, abs/1301.3781, 2013.
[16] S. Sadiq, Y. Yan, A. Taylor, M. L. Shyu, S. C. Chen and D. Feaster, AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter, 2017 IEEE International Conference on Information Reuse and Integration, pp. 356-365, 2017.
[17] H. Sakoe and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans Acoustics Speech Signal Process, 26, pp. 43-49, 1978.
[18] R. Schuchard, A. Crooks, A. Stefanidis and A. Croitoru, Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks, L.M. Aiello, C. Cherifi, H. Cherifi et al.(eds) Complex networks and their applications VII. Springer International Publishing, pp. 424-436, 2019.
[19] Y. Wang, C. Wu, K. Zheng, and X. Wang, Social Bot Detection Using Tweets Similarity, International Conference on Security and Privacy in Communication Systems, pp. 63-78, 2018.
[20] 黃懷萱，利用行為脈絡探索社群媒體上的異常使用者，國立政治大學資訊科學系碩士論文，2020。
描述 碩士
國立政治大學
資訊科學系碩士在職專班
107971020
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107971020
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan, Man-Kwanen_US
dc.contributor.author (Authors) 蕭君弘zh_TW
dc.contributor.author (Authors) Hsiao, Chun-Hungen_US
dc.creator (作者) 蕭君弘zh_TW
dc.creator (作者) Hsiao, Chun-Hungen_US
dc.date (日期) 2020en_US
dc.date.accessioned 2-Sep-2020 13:15:45 (UTC+8)-
dc.date.available 2-Sep-2020 13:15:45 (UTC+8)-
dc.date.issued (上傳時間) 2-Sep-2020 13:15:45 (UTC+8)-
dc.identifier (Other Identifiers) G0107971020en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131939-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系碩士在職專班zh_TW
dc.description (描述) 107971020zh_TW
dc.description.abstract (摘要) 近年來社群媒體越來越興盛，改變了大眾接收資訊的習慣。社群媒體上的話語權也成了不同陣營攻防的重點。因此社群媒體上開始出現越來越多行為可疑的異常帳號，試圖操弄輿論引導風向，藉此獲得自身的利益。
本論文研究開發一個社群媒體上的異常帳號探勘分析，透過發文及留言互動行為探索分析異常帳號。本系統的核心精神為由已知的異常帳號找出其他未知異常帳號。系統提供兩大主要功能Explore By Examples及Analysis By Examples。前者著重在探索異常帳號、後者著重在分析異常行為。兩大功能都由作息、來源IP、回應行為、網絡四個面向來探索分析，以協助使用者探索出異常帳號。本論文以台灣最熱門的本土社群媒體平台PTT BBS資料進行實證，並根據PTT官方公告異常帳號運用本系統進行案例分析。		zh_TW
dc.description.abstract (摘要) In recent years, with the growth of social media, public`s habits of receiving information have been changed. More and more malicious accounts appear on social media. These malicious accounts try to manipulate public opinion for spin control to gain their own interests.
This thesis aims at the design and implementation of an intelligent discovery system to explore malicious accounts over social media. The core spirit of the developed system lies in the exploration of unknown malicious accounts by known malicious accounts over social media. Two main functions provided by the developed system are exploration by examples and analysis by examples. While the former focuses on exploration of unknown malicious accounts, the latter focuses on the analysis of known ones. To explore and analyze malicious account, four aspects of malicious behaviors are considered, namely activity, IP address, like/dislike behaviors, and network. The developed system is verified on the data collected from PTT bulletin board system, which is the most popular domestic social media service in Taiwan. Four case studies are performed to demonstrate the superiority of the developed intelligent discovery system.		en_US
dc.description.tableofcontents 第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的與方法 3
1.3論文貢獻 3
1.4論文架構 4
第二章 相關研究 5
2.1異常帳號分析 5
2.2現有社群媒體分析系統 5
2.2現有社群媒體分析系統共通處 12
第三章 研究方法 13
3.1 社群媒體共通功能 13
3.2 異常帳號在社群媒體上的行為特徵 16
3.3功能設計 18
3.3.1系統功能 18
3.3.2 Analysis By Examples 19
3.3.3 Explore By Examples 25
3.4系統特色 28
第四章 系統實作 29
4.1資料來源 29
4.2系統架構 29
4.3系統頁面 32
4.3.1首頁 32
4.3.2 Analysis By Examples-Activity 33
4.3.3 Analysis By Examples-IP 36
4.3.4 Analysis By Examples-Like/Dislike 39
4.3.5 Analysis By Examples-Network 41
4.3.6 Explore By Examples-Activity 43
4.3.7 Explore By Examples-IP 45
4.3.8 Explore By Examples-Like/Dislike 46
4.3.9 Explore By Examples-Network 48
4.3.10 Network Visualization 48
第五章 案例分析 50
5.1案例一 50
5.2案例二 72
5.3案例三 76
5.4案例四 87
第六章 結論 91
6.1結論 91
6.2未來研究方向 91
參考文獻 92		zh_TW
dc.format.extent 14383666 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107971020en_US
dc.subject (關鍵詞) 異常帳號zh_TW
dc.subject (關鍵詞) 智慧型探勘系統zh_TW
dc.subject (關鍵詞) 以例探索zh_TW
dc.subject (關鍵詞) Malicious Accounten_US
dc.subject (關鍵詞) Intelligent Discovery Systemen_US
dc.subject (關鍵詞) Explore By Examplesen_US
dc.title (題名) 社群媒體異常帳號探勘系統的設計與實作zh_TW
dc.title (題名) Design and Implementation of the Intelligent Discovery System to Explore Malicious Accounts over Social Mediaen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] K. S. Adewole, N. B. Anuar, A. Kamsin, K. D. Varathan and S. A. Razak, Malicious Accounts: Dark of the Social Networks, Journal of Network and Computer Applications, 79, pp. 41-67, 2016.
[2] A. Badawy, E. Ferrara and K. Lerman, Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign, The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 258-265, 2018.
[3] N. Chavoshi, H. Hamooni and A. Mueen, Temporal Patterns in Bot Activities, International Conference on World Wide Web Companion, pp. 1601-1606, 2017.
[4] N. Chavoshi, H. Hamooni, and A. Mueen, DeBot: Twitter Bot Detection via Warped Correlation, IEEE International Conference on Data Mining, pp. 817-822, 2016.
[5] N. Chavoshi, H. Hamooni, and A. Mueen, Identifying Correlated Bots in Twitter, International Conference on Social Informatics, pp. 14-21, 2016.
[6] L. A. Cornelissen, R. J. Barnett, P. Schoonwinkel, B. D. Eichstadt and H. B. Magodla, A Network Topology Approach to Bot Classification, Annual Conference of the South African Institute of Computer Scientists and Information Technologists, pp. 79-88, 2018.
[7] C. A. Davis, O. Varol, E. Ferrara, A. Flammini and F. Menczer, BotOrNot: A System to Evaluate Social Bots, International Conference Companion on World Wide Web, pp. 273-274, 2016.
[8] A. Duh, M. S. Rupnik, and D. Korošak, Collective Behavior of Social Bots Is Encoded in Their Temporal Twitter Activity, Big Data 6, 2, pp. 113-123, 2018.
[9] S. Gokalp, M. Temkit, H. Davulcu, and I.H. Toroslu, Partitioning and Scaling Signed Bipartite Graphs for Polarized Political Blogosphere, IEEE International Conference on Social Computing, pp. 168-173, 2013.
[10] S. Gupta, P. Kumaraguru, and T. Chakraborty, MalReG: Detecting and Analyzing Malicious Retweeter Groups, ACM India Joint International Conference on Data Science and Management of Data, pp. 61-69, 2019.
[11] M. Jiang, P. Cui and C. Faloutsos, Suspicious Behavior Detection: Current Trends and Future Directions, IEEE Intelligent Systems, 31, 1, pp. 31-39, 2016.
[12] A. Karataş and S. Şahin, A Review on Social Bot Detection Techniques and Research Directions, International Security and Cryptology Conference, Turkey, pp. 156-161, 2017.
[13] T. Khaund, K. K. Bandeli, M. N. Hussain, A. Obadimu, S. Al-Khateeb and N. Agarwal, Analyzing Social and Communication Network Structures of Social Bots and Humans, The 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 794-797, 2018.
[14] S. Kudugunta and E. Ferrara, Deep Neural Networks for Bot Detection, Information Sciences, 467, pp. 312-322, 2018.
[15] T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient estimation of word representations in vector space, International Conference on Learning Representations, abs/1301.3781, 2013.
[16] S. Sadiq, Y. Yan, A. Taylor, M. L. Shyu, S. C. Chen and D. Feaster, AAFA: Associative Affinity Factor Analysis for Bot Detection and Stance Classification in Twitter, 2017 IEEE International Conference on Information Reuse and Integration, pp. 356-365, 2017.
[17] H. Sakoe and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition, IEEE Trans Acoustics Speech Signal Process, 26, pp. 43-49, 1978.
[18] R. Schuchard, A. Crooks, A. Stefanidis and A. Croitoru, Bots in Nets: Empirical Comparative Analysis of Bot Evidence in Social Networks, L.M. Aiello, C. Cherifi, H. Cherifi et al.(eds) Complex networks and their applications VII. Springer International Publishing, pp. 424-436, 2019.
[19] Y. Wang, C. Wu, K. Zheng, and X. Wang, Social Bot Detection Using Tweets Similarity, International Conference on Security and Privacy in Communication Systems, pp. 63-78, 2018.
[20] 黃懷萱，利用行為脈絡探索社群媒體上的異常使用者，國立政治大學資訊科學系碩士論文，2020。		zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202001683en_US