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    题名: YouTube政治頻道異常使用者行為探勘
    Detecting Abnormal User Behavior over YouTube Political Channels
    作者: 張恩慈
    Chang, En-Cih
    贡献者: 沈錳坤
    Shan, Man-Kwan
    張恩慈
    Chang, En-Cih
    关键词: 異常帳號
    立場分析
    時序探勘
    共謀關係
    日期: 2023
    上传时间: 2024-02-01 11:40:13 (UTC+8)
    摘要: 隨著社群媒體的日漸興盛，Facebook、Instagram、Twitter、Tiktok等社群軟體儼然成為人們日常生活中不可或缺的社交工具，其中YouTube更是全世界每日流量最高的影片串流平台。
    社群平台進而成為行銷業者維持正面形象、提升曝光度的手段。政治勢力也開始在社群平台上宣傳政見和理念，尋求更廣泛的支持者。意想不到的後果是，社群媒體上不正常的活動漸漸萌芽，公關公司開始透過網軍，以人為的方式增加影片觀看次數、點讚、給予正面的評論及衝高頻道訂閱數，也運用經營的非真人帳號在文章或影片下留言，激起熱衷粉絲情緒性留言，也試圖帶動群眾意見風向。
    近年來已陸續有社群平台異常帳號之研究。但YouTube與一般社群平台不同，影片內容不易分析，影片下方的留言討論不似一般社群平台即時且熱烈，且YouTube 站方沒有公開影片及留言按讚或按噓的帳號，提高了異常帳號分析的挑戰性。也因此現有YouTube 異常帳號的研究非常少，多數集中在色情暴力或盜版影片的偵測。
    本研究旨在透過YouTube平台上公開顯示的欄位資訊，結合時間特徵值及社群帳號活動關聯性，提出異常帳號探勘的方法。本論文在大量政治相關頻道的評論樣本獲取使用者當中的隱含知識，並透過評論時間及獲得其他使用者之回饋，分析YouTube政治相關頻道立場及挖掘潛在的異常使用者同夥行為，作為政治頻道的生態探討。
    參考文獻: [1] Aiyar, S., & Shetty, N. P. (2018). N-Gram Assisted Youtube Spam Comment Detection. International Conference on Computational Intelligence and Data Science (ICCIDS). Gurugram, India.
    [2] Alassad, M., Agarwal, N., & Hussain, M. N. (2019). Examining Intensive Groups in YouTube Commenter Networks. In Social, Cultural, and Behavioral Modeling. Washington, DC, USA: Springer.
    [3] Alberto, T. C., Lochter, J. V., & Almeida, T. A. (2015). TubeSpam: Comment Spam Filtering on YouTube. 14th International Conference on Machine Learning and Applications (ICMLA). Miami, FL, USA.
    [4] Alharbi, A., Dong, H., Yi, X., Tari, Z., & Khalil, I. (2021). Social Media Identity Deception Detection: A Survey. ACM Computing Surveys, Vol. 54, No. 3.
    [5] Bond, R. & Messing, S. (2015). Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook. American Political Science Review, Vol. 109, No. 1.
    [6] Chowdury, R., Adnan, M. N., Mahmud, G. A., & Rahman, R. M. (2013). A Data Mining Based Spam Detection System for YouTube. Eighth International Conference on Digital Information Management (ICDIM). Islamabad, Pakistan.
    [7] Dutta, H. S., Jobanputra, M., Negi, H., & Chakraborty, T. (2021). Detecting and Analyzing Collusive Entities on YouTube. ACM Transactions on Intelligent Systems and Technology, Vol. 12, No. 5.
    [8] Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The Rise of Social Bots. Communications of the ACM, Vol. 59, No. 7.
    [9] Hussain, M. N., Tokdemir, S., Agarwal , N., & Al-khateeb, S. (2018). Analyzing Disinformation and Crowd Manipulation Tactics on YouTube. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). Barcelona, Spain.
    [10] Kaushal, R., Saha, S., Bajaj, P., & Kumaraguru, P. (2016). KidsTube: Detection, Characterization and Analysis of Child Unsafe Content & Promoters on YouTube. 14th Annual Conference on Privacy, Security and Trust (PST). Auckland, New Zealand.
    [11] Korn, F., Labrinidis, A., Kotidis, Y., & Faloutsos C. (2000). Quantifiable Data Mining Using Ratio Rules. The VLDB Journal, Vol. 8.
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    [12] Singh, S., Kaushal, R., Buduru, A. B., & Kumaraguru, P. (2019). KidsGUARD: Fine Grained Approach for Child Unsafe Video Representation and Detection. Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing (SAC). Limassol, Cyprus.
    [13] Varol, O., Ferrara , E., Davis, C. A., Menczer, F., & Flammini, A. (2017). Online Human-Bot Interactions: Detection, Estimation, and Characterization. Proceedings of the International AAAI Conference on Web and Social Media. Proceedings of the International AAAI Conference on Web and Social Media.
    [14] Yusof, Y., & Sadoon, O. H. (2017). Detecting Video Spammers in YouTube Social Media. Proceedings of the 6 th International Conference on Computing and Informatics (ICOCI). Kuala Iumpur, Malaysia.
    描述: 碩士
    國立政治大學
    資訊科學系
    109753109
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0109753109
    数据类型: thesis
    显示于类别:[資訊科學系] 學位論文

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