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題名 利用行為脈絡探索社群媒體上的異常使用者
Exploring Anomalous Users in Social Media by Contextual Relationship作者 黃懷萱
Huang, Huai-Syuan貢獻者 沈錳坤
Shan, Man-Kwan
黃懷萱
Huang, Huai-Syuan關鍵詞 異常使用者
不實資訊
社群搜尋
立場判斷
Malicious Users
Disinformation
Community Search
Polarity
Identification日期 2020 上傳時間 2-九月-2020 12:15:24 (UTC+8) 摘要 隨著社群媒體使用的普及，許多公關公司或是政治人物為了宣傳以及建立正面形象，會利用大量帳號互相哄抬、帶風向來操作輿論，借此影響其他使用者的看法。不實資訊已成為社群媒體的關鍵議題。本論文旨在研究社群媒體上異常使用者的探勘方法。我們針對有互相哄抬行為的帳號，提出考慮使用者之間回文的行為脈絡的探勘方法，利用使用者之間回文的互動行為及其對於特定主題的回文類型，探索使用者之間的行為脈絡，建立使用者Coordination Graph，並結合Community Search演算法，給定已知的異常使用者，找出跟異常使用者有密切互動的潛在其他異常使用者。本論文以PTT八卦板為例，實驗驗證我們所提出的方法，實驗結果顯示我們的方法在最差的情況時，找Community的TOP-50之Precision在0.8以上。
In recent years, social media has become a major tools used for people to socialize and share information. In order to manipulate public opinion on the social media, some operates a large number of abnormal accounts to collude with each other over social media. Disinformation has become one of the key issues of social mediaThis study aims to explore abnormal users on social media by constructing user coordination graph based on the interactions between users and the polarities of users on specific topics. We consider the problem of exploring malicious users as a community search problem to explore malicious users by examples. Given a set of abnormal users, the proposed algorithm identifies potential unknown accomplices who have close interaction with the abnormal users. The experiments are performed on the data collected from the largest domestic social media service in Taiwan, PTT bulletin-board service system. The experimental results show that the top-50 precision of our method is above 0.8 in the worst case.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, Vol. 79, 2017.[2] A. Atanasov, G. D. F. Morales, and P. Nakov, Predicting the Role of Political Trolls in Social Media. Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019.[3] A. E. Azab, A. M. Idrees, M. A. Mahmoud, and H. Hefny, Fake Accounts Detection in Twitter Based on Minimum Weighted Feature. International Scholarly and Scientific Research and Innovation, Vol. 10, No. 1, 2016.[4] N. Barbieri, F. Bonchi, E. Galimberti, and F. Gullo, Efficient and Effective Community Search. Data Mining and Knowledge Discovery, Vol. 29, No. 5, 2015.[5] S. Y. Bhat, and M. Abulaish, Community-based Features for Identifying Spammers in Online Social Networks. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013.[6] P. Biyani, K. Tsioutsiouliklis, and J. Blackmer, “8 Amazing Secrets for Getting More Clicks”: Detecting Clickbaits in News Streams Using Article Informality. Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016.[7] Q. Cao, M. Sirivianos, X. Yang, and T. Pregueiro, Aiding the Detection of Fake Accounts in Large Scale Social Online Services. Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, 2012.[8] T. Chakraborty, A. Dalmia, A. Mukherjee, and N. Ganguly, Metrics for Community Analysis: A Survey. ACM Computing Survey, Vol. 50, No. 4, 2017.[9] N. Chavoshi, H. Hamooni, and A. Mueen, Identifying Correlated Bots in Twitter. Proceedings of the 8th International Conference on Social Informatics, 2016.[10] A. Clauset, Finding Local Community Structure in Networks. Physical Review E, Vol. 72, Iss. 2, 2005.[11] W. Cui, Y. Xiao, H. Wang, and W. Wang, Local Search of Communities in Large Graphs. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, 2014.[12] Y. Fang, X. Huang, L. Qin, Y. Zhang, W. Zhang, R. Cheng, and X. Lin, A Survey of Community Search over Big Graphs. The VLDB Journal, Vol. 29, 2019.[13] G. W. Flake, S. Lawrence, and C. L. Giles. Efficient, Identification of Web Communities. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.[14] C. Giatsidis, D. M. Thilikos, and M. Vazirgiannis, D-cores: Measuring Collaboration of Directed Graphs Based on Degeneracy. Proceedings of the IEEE International Conference on Data Mining, 2011.[15] S. Gokalp, M. Temkit, H. Davulcu, and I.H. Toroslu, Partitioning and Scaling Signed Bipartite Graphs for Polarized Political Blogosphere. Proceedings of the 2013 IEEE International Conference on Social Computing, 2013.[16] S. Gupta, P. Kumaraguru, and T. Chakraborty, MalReG: Detecting and Analyzing Malicious Retweeter Groups. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2019.[17] A. Kadian, V. Singh, and A. Bhattacherjee, Detecting Clickbait Using User Emotions and Behaviors on Social Media. Proceedings of the 39th International Conference on Information Systems, 2018.[18] S. Kumar, and N. Shah, False Information on Web and Social Media: A Survey. Social Media Analytics: Advances and Applications, CRC Press, 2018.[19] Y. Li, C. Sha, X. Huang, and Y. Zhang, Community Detection in Attributed Graphs: An Embedding Approach. Proceedings of the 32th AAAI Conference on Artificial Intelligence, 2018.[20] T. Liu, W. Wei, and X. Wan, Learning to Explain Ambiguous Headlines of Online News. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018.[21] F. Luo, J. Z. Wang, and E. Promislow, Exploring Local Community Structures in Large Networks. Web Intelligent and Agent Systems, Vol. 6, No. 4, 2008.[22] U. V. Luxburg, A Tutorial on Spectral Clustering. Statistics and Computing, Vol. 17, No. 4, 2007.[23] T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space. Proceedings of the International Conference on Learning Representations, 2013.[24] A. Minnich, N. Chavoshi, D. Koutra and A. Mueen, BotWalk: Efficient Adaptive Exploration of Twitter Bot Networks. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017.[25] M. E. Newman, Fast Algorithm for Detecting Community Structure in Networks. Physical Review E, Vol. 69, No. 6, 2004.[26] B. Perozzi, R. Al-Rfou, and S. Skiena, DeepWalk: Online Learning of Social Representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.[27] B. Saha, A. Hoch, S. Khuller, L. Raschid, and X.-N. Zhang, Dense Subgraphs with Restrictions and Applications to Gene Annotation Graphs. Proceedings of the 14th Annual International Conference on Research in Computational Molecular Biology, 2010.[28] K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, Vol. 109, No. 1, 2017.[29] M. Sozio, and A. Gionis, The Community-search Problem and How to Plan a Successful Cocktail Party. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010.[30] L. Sun, X. Huang, R.-H. Li, and J. Xu, Fast Algorithms for Intimate-Core Group Search in Weighted Graphs. Proceedings of the International Conference on Web Information Systems Engineering, 2019.[31] N. Vo, K. Lee, C. Cao, T. Tran, and H. Choi, Revealing and Detecting Malicious Retweeter Groups. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017.[32] M.-H. Wang, N.-L. Nguyen, S.-c. Dai, P.-W. Chi, and C.-R. Dow, Understanding Potential Cyber-Armies in Elections: A Study of Taiwan. Sustainability, Vol. 12, No. 6, 2020.[33] M.-H. Wang, N.-L. Nguyen, and C.-R. Dow, Detecting Potential Cyber Armies of Election Campaigns Based on Behavioral Analysis. Proceedings of the International Conference on Complex Networks and their Applications, 2018.[34] Y. Wang, C. Wu1, K. Zheng, and X. Wang, Social Bot Detection Using Tweets Similarity. Security and Privacy in Communication Networks, 2018.[35] Y. Wu, R. Jin, J. Li, and X. Zhang, Robust Local Community Detection: On Free Rider Effect and Its Elimination. PVLDB, Vol. 8, No. 7, 2015.[36] S. Yoon, K. Park, J. Shin, H. Lim, S. Won, M. Cha, and K. Jung, Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019.[37] H. Zha, Generic Summarization and Keyphrase Extraction Using Mutual Reinforcement Principle and Sentence Clustering. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002.[38] W. Zhao, and F. Zhang, Node Embedding With a CN-Based Random Walk for Community Search. IEEE Access, Vol. 7, 2019.[39] D. Zheng, J. Liu, R.-H. Li, C. Aslay, Y.-C. Chen, and X. Huang, Querying Intimate-Core Groups in Weighted Graphs. Proceedings of the IEEE International Conference on Semantic Computing, 2017.[40] 林佳賢, 〈【獨家分析】跟著資料記者追網軍，「假外國人」如何在PTT鼓吹韓流〉.《天下雜誌》671期《假新聞黑洞！輿論戰爭@台灣》, 2019.[41] 陳郁雯, 以深度學習探勘社群網路異常使用者的協作行為. 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
107753002資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107753002 資料類型 thesis dc.contributor.advisor 沈錳坤 zh_TW dc.contributor.advisor Shan, Man-Kwan en_US dc.contributor.author (作者) 黃懷萱 zh_TW dc.contributor.author (作者) Huang, Huai-Syuan en_US dc.creator (作者) 黃懷萱 zh_TW dc.creator (作者) Huang, Huai-Syuan en_US dc.date (日期) 2020 en_US dc.date.accessioned 2-九月-2020 12:15:24 (UTC+8) - dc.date.available 2-九月-2020 12:15:24 (UTC+8) - dc.date.issued (上傳時間) 2-九月-2020 12:15:24 (UTC+8) - dc.identifier (其他 識別碼) G0107753002 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/131631 - 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 zh_TW dc.description (描述) 107753002 zh_TW dc.description.abstract (摘要) 隨著社群媒體使用的普及，許多公關公司或是政治人物為了宣傳以及建立正面形象，會利用大量帳號互相哄抬、帶風向來操作輿論，借此影響其他使用者的看法。不實資訊已成為社群媒體的關鍵議題。本論文旨在研究社群媒體上異常使用者的探勘方法。我們針對有互相哄抬行為的帳號，提出考慮使用者之間回文的行為脈絡的探勘方法，利用使用者之間回文的互動行為及其對於特定主題的回文類型，探索使用者之間的行為脈絡，建立使用者Coordination Graph，並結合Community Search演算法，給定已知的異常使用者，找出跟異常使用者有密切互動的潛在其他異常使用者。本論文以PTT八卦板為例，實驗驗證我們所提出的方法，實驗結果顯示我們的方法在最差的情況時，找Community的TOP-50之Precision在0.8以上。 zh_TW dc.description.abstract (摘要) In recent years, social media has become a major tools used for people to socialize and share information. In order to manipulate public opinion on the social media, some operates a large number of abnormal accounts to collude with each other over social media. Disinformation has become one of the key issues of social mediaThis study aims to explore abnormal users on social media by constructing user coordination graph based on the interactions between users and the polarities of users on specific topics. We consider the problem of exploring malicious users as a community search problem to explore malicious users by examples. Given a set of abnormal users, the proposed algorithm identifies potential unknown accomplices who have close interaction with the abnormal users. The experiments are performed on the data collected from the largest domestic social media service in Taiwan, PTT bulletin-board service system. The experimental results show that the top-50 precision of our method is above 0.8 in the worst case. en_US dc.description.tableofcontents 致謝 i摘要 iiAbstract iii目錄 iv圖目錄 vi表目錄 viii第一章 緒論 11.1 研究背景 11.2 研究動機與目的 3第二章 相關研究 42.1 研究類型 42.2 異常帳號類型 52.3 Malicious Accounts Analysis 5第三章 研究方法 73.1 研究架構 73.2 研究方法 73.2.1 資料類型 73.2.2 Contextual Relationship Discovery 93.2.3 Polarity Identification 133.2.4 Community Search with Coordination Graph 18第四章 實驗設計與結果分析 234.1 資料概況 234.1.1 PTT八卦板資料 234.1.2 PTT站方公告資料 234.2 實驗設計與評估方法 254.3 實驗結果 274.3.1 Polarity Identification and Case Study 274.3.2 Contextual Relationship Discovery 354.3.3 Community Search 40第五章 結論與未來研究 49參考資料 50 zh_TW dc.format.extent 5153017 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107753002 en_US dc.subject (關鍵詞) 異常使用者 zh_TW dc.subject (關鍵詞) 不實資訊 zh_TW dc.subject (關鍵詞) 社群搜尋 zh_TW dc.subject (關鍵詞) 立場判斷 zh_TW dc.subject (關鍵詞) Malicious Users en_US dc.subject (關鍵詞) Disinformation en_US dc.subject (關鍵詞) Community Search en_US dc.subject (關鍵詞) Polarity en_US dc.subject (關鍵詞) Identification en_US dc.title (題名) 利用行為脈絡探索社群媒體上的異常使用者 zh_TW dc.title (題名) Exploring Anomalous Users in Social Media by Contextual Relationship en_US dc.type (資料類型) thesis en_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, Vol. 79, 2017.[2] A. Atanasov, G. D. F. Morales, and P. Nakov, Predicting the Role of Political Trolls in Social Media. Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019.[3] A. E. Azab, A. M. Idrees, M. A. Mahmoud, and H. Hefny, Fake Accounts Detection in Twitter Based on Minimum Weighted Feature. International Scholarly and Scientific Research and Innovation, Vol. 10, No. 1, 2016.[4] N. Barbieri, F. Bonchi, E. Galimberti, and F. Gullo, Efficient and Effective Community Search. Data Mining and Knowledge Discovery, Vol. 29, No. 5, 2015.[5] S. Y. Bhat, and M. Abulaish, Community-based Features for Identifying Spammers in Online Social Networks. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013.[6] P. Biyani, K. Tsioutsiouliklis, and J. Blackmer, “8 Amazing Secrets for Getting More Clicks”: Detecting Clickbaits in News Streams Using Article Informality. Proceedings of the 30th AAAI Conference on Artificial Intelligence, 2016.[7] Q. Cao, M. Sirivianos, X. Yang, and T. Pregueiro, Aiding the Detection of Fake Accounts in Large Scale Social Online Services. Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, 2012.[8] T. Chakraborty, A. Dalmia, A. Mukherjee, and N. Ganguly, Metrics for Community Analysis: A Survey. ACM Computing Survey, Vol. 50, No. 4, 2017.[9] N. Chavoshi, H. Hamooni, and A. Mueen, Identifying Correlated Bots in Twitter. Proceedings of the 8th International Conference on Social Informatics, 2016.[10] A. Clauset, Finding Local Community Structure in Networks. Physical Review E, Vol. 72, Iss. 2, 2005.[11] W. Cui, Y. Xiao, H. Wang, and W. Wang, Local Search of Communities in Large Graphs. Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, 2014.[12] Y. Fang, X. Huang, L. Qin, Y. Zhang, W. Zhang, R. Cheng, and X. Lin, A Survey of Community Search over Big Graphs. The VLDB Journal, Vol. 29, 2019.[13] G. W. Flake, S. Lawrence, and C. L. Giles. Efficient, Identification of Web Communities. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.[14] C. Giatsidis, D. M. Thilikos, and M. Vazirgiannis, D-cores: Measuring Collaboration of Directed Graphs Based on Degeneracy. Proceedings of the IEEE International Conference on Data Mining, 2011.[15] S. Gokalp, M. Temkit, H. Davulcu, and I.H. Toroslu, Partitioning and Scaling Signed Bipartite Graphs for Polarized Political Blogosphere. Proceedings of the 2013 IEEE International Conference on Social Computing, 2013.[16] S. Gupta, P. Kumaraguru, and T. Chakraborty, MalReG: Detecting and Analyzing Malicious Retweeter Groups. Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, 2019.[17] A. Kadian, V. Singh, and A. Bhattacherjee, Detecting Clickbait Using User Emotions and Behaviors on Social Media. Proceedings of the 39th International Conference on Information Systems, 2018.[18] S. Kumar, and N. Shah, False Information on Web and Social Media: A Survey. Social Media Analytics: Advances and Applications, CRC Press, 2018.[19] Y. Li, C. Sha, X. Huang, and Y. Zhang, Community Detection in Attributed Graphs: An Embedding Approach. Proceedings of the 32th AAAI Conference on Artificial Intelligence, 2018.[20] T. Liu, W. Wei, and X. Wan, Learning to Explain Ambiguous Headlines of Online News. Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018.[21] F. Luo, J. Z. Wang, and E. Promislow, Exploring Local Community Structures in Large Networks. Web Intelligent and Agent Systems, Vol. 6, No. 4, 2008.[22] U. V. Luxburg, A Tutorial on Spectral Clustering. Statistics and Computing, Vol. 17, No. 4, 2007.[23] T. Mikolov, K. Chen, G. Corrado, and J. Dean, Efficient Estimation of Word Representations in Vector Space. Proceedings of the International Conference on Learning Representations, 2013.[24] A. Minnich, N. Chavoshi, D. Koutra and A. Mueen, BotWalk: Efficient Adaptive Exploration of Twitter Bot Networks. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017.[25] M. E. Newman, Fast Algorithm for Detecting Community Structure in Networks. Physical Review E, Vol. 69, No. 6, 2004.[26] B. Perozzi, R. Al-Rfou, and S. Skiena, DeepWalk: Online Learning of Social Representations. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.[27] B. Saha, A. Hoch, S. Khuller, L. Raschid, and X.-N. Zhang, Dense Subgraphs with Restrictions and Applications to Gene Annotation Graphs. Proceedings of the 14th Annual International Conference on Research in Computational Molecular Biology, 2010.[28] K. Shu, A. Sliva, S. Wang, J. Tang, and H. Liu, Fake News Detection on Social Media: A Data Mining Perspective. ACM SIGKDD Explorations Newsletter, Vol. 109, No. 1, 2017.[29] M. Sozio, and A. Gionis, The Community-search Problem and How to Plan a Successful Cocktail Party. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010.[30] L. Sun, X. Huang, R.-H. Li, and J. Xu, Fast Algorithms for Intimate-Core Group Search in Weighted Graphs. Proceedings of the International Conference on Web Information Systems Engineering, 2019.[31] N. Vo, K. Lee, C. Cao, T. Tran, and H. Choi, Revealing and Detecting Malicious Retweeter Groups. Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017.[32] M.-H. Wang, N.-L. Nguyen, S.-c. Dai, P.-W. Chi, and C.-R. Dow, Understanding Potential Cyber-Armies in Elections: A Study of Taiwan. Sustainability, Vol. 12, No. 6, 2020.[33] M.-H. Wang, N.-L. Nguyen, and C.-R. Dow, Detecting Potential Cyber Armies of Election Campaigns Based on Behavioral Analysis. Proceedings of the International Conference on Complex Networks and their Applications, 2018.[34] Y. Wang, C. Wu1, K. Zheng, and X. Wang, Social Bot Detection Using Tweets Similarity. Security and Privacy in Communication Networks, 2018.[35] Y. Wu, R. Jin, J. Li, and X. Zhang, Robust Local Community Detection: On Free Rider Effect and Its Elimination. PVLDB, Vol. 8, No. 7, 2015.[36] S. Yoon, K. Park, J. Shin, H. Lim, S. Won, M. Cha, and K. Jung, Detecting Incongruity Between News Headline and Body Text via a Deep Hierarchical Encoder. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, 2019.[37] H. Zha, Generic Summarization and Keyphrase Extraction Using Mutual Reinforcement Principle and Sentence Clustering. Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002.[38] W. Zhao, and F. Zhang, Node Embedding With a CN-Based Random Walk for Community Search. IEEE Access, Vol. 7, 2019.[39] D. Zheng, J. Liu, R.-H. Li, C. Aslay, Y.-C. Chen, and X. Huang, Querying Intimate-Core Groups in Weighted Graphs. Proceedings of the IEEE International Conference on Semantic Computing, 2017.[40] 林佳賢, 〈【獨家分析】跟著資料記者追網軍，「假外國人」如何在PTT鼓吹韓流〉.《天下雜誌》671期《假新聞黑洞！輿論戰爭@台灣》, 2019.[41] 陳郁雯, 以深度學習探勘社群網路異常使用者的協作行為. 國立政治大學資訊科學系, 碩士論文, 2020. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202001687 en_US
