English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 112721/143689 (78%)
Visitors : 49634364      Online Users : 699
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 >  Item 140.119/131631
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/131631


    Title: 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
    Authors: 黃懷萱
    Huang, Huai-Syuan
    Contributors: 沈錳坤
    Shan, Man-Kwan
    黃懷萱
    Huang, Huai-Syuan
    Keywords: 異常使用者
    不實資訊
    社群搜尋
    立場判斷
    Malicious Users
    Disinformation
    Community Search
    Polarity
    Identification
    Date: 2020
    Issue Date: 2020-09-02 12:15:24 (UTC+8)
    Abstract: 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 media
    This 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.
    Reference: [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.
    Description: 碩士
    國立政治大學
    資訊科學系
    107753002
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107753002
    Data Type: thesis
    DOI: 10.6814/NCCU202001687
    Appears in Collections:[資訊科學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    300201.pdf5032KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback