English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 90756/120810 (75%)
Visitors : 25066079      Online Users : 282
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
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/128993


    Title: 以深度動態卷積神經網路實施多重任務學習偵測假新聞
    Deep Dynamic Convolutional Neural Network with Multi-Task Learning for Fake News Detection
    Authors: 林佑駿
    Lin, Yu-Chun
    Contributors: 胡毓忠
    Hu, Yuh-Jong
    林佑駿
    Lin, Yu-Chun
    Keywords: 假新聞
    深度學習
    社群媒體
    動態卷積神經網路
    多重任務學習
    Fake News
    Detection
    Deep Learning
    Social Media
    Dynamic CNN
    Multi-Task Learning
    Date: 2020
    Issue Date: 2020-03-02 11:38:27 (UTC+8)
    Abstract: 傳統的假新聞偵測主要區分為知識庫比對、專家人工辨識與特徵機器學習等3大方式,但是隨著資料數據的日益龐大、新聞來源的多樣化以及惡意變造新聞的手法推層出新,傳統假新聞偵測方法已出現瓶頸,逐漸不敷現況使用,為了突破此一困境,於是出現以深度學習找尋未知特徵的偵測方式。
    以往深度學習受限於硬體效能,不易針對模型的調整與優化進行全方位驗測,所幸隨著科技進步與莫爾定律推演,硬體效能持續以指數性程度成長,進而使深度學習的研究邁向了全新領域。
    本論文除了研究如何以深度學習中的深度動態卷積神經網路進行假新聞偵
    測外,同時也探討超參數在深度學習中對於優化的影響及資料集特徵在模型中所扮演的角色。運用多重任務學習框架,將推文立場、假新聞偵測與假新聞驗證等3個任務相互搭配,分析各任務彼此之間的關連影響。另針對深度動態卷積神經網路在處理假新聞偵測應用問題上的特性進行分析。
    Traditional fake news detection is mainly divided into three major methods, knowledge based comparison, expert manual identification, and feature machine learning. With the increasing data, the diversification of news sources, and the malicious method of altering news, the traditional methods of detecting fake news has become a bottleneck, and it is gradually inadequate to use it. To break through this dilemma, there is a detection method that uses deep learning to find unknown features.
    In the past, deep learning was limited by hardware performance, and it was not easy to conduct comprehensive testing for model adjustment and optimization.
    Fortunately, with the advancement of science and technology and Moore's Law, hardware performance continued to grow exponentially, deep learning towards a whole new field.
    In addition to studying how to detect fake news with deep dynamic convolutional neural networks in deep learning, this paper also explores the impact of
    hyperparameters on deep learning optimization and the role of data set features in the model. Besides, a multi-task learning framework is used to match three tasks, such as tweet position, fake news detection, and fake news verification, to analyze the impact of each task on each other. It also analyzes the characteristics of the deep dynamic convolutional neural network in dealing with the application of fake news detection.
    Reference: [1] N. Kalchbrenner, E. Grefenstette, and P. Blunsom, "A convolutional neural
    network for modelling sentences," arXiv preprint arXiv:1404.2188, 2014.
    [2] Z. Tufekci, "It’s the (democracy-poisoning) Golden Age of free speech,"
    WIRED. Accessed May, vol. 20, p. 2018, 2018.
    [3] E. Hunt, "What is fake news? How to spot it and what you can do to stop it,"
    The Guardian, vol. 17, 2016.
    [4] 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. 19, no. 1, pp. 22-36, 2017.
    [5] V. Rubin, N. Conroy, Y. Chen, and S. Cornwell, "Fake news or truth? using
    satirical cues to detect potentially misleading news," in Proceedings of the
    second workshop on computational approaches to deception detection, 2016,
    pp. 7-17.
    [6] M. Balmas, "When fake news becomes real: Combined exposure to multiple
    news sources and political attitudes of inefficacy, alienation, and cynicism,"
    Communication Research, vol. 41, no. 3, pp. 430-454, 2014.
    [7] E. Ferrara, O. Varol, C. Davis, F. Menczer, and A. Flammini, "The rise of
    social bots," Communications of the ACM, vol. 59, no. 7, pp. 96-104, 2016.
    [8] J. Cheng, M. Bernstein, C. Danescu-Niculescu-Mizil, and J. Leskovec,
    "Anyone can become a troll: Causes of trolling behavior in online
    discussions," in Proceedings of the 2017 ACM conference on computer
    supported cooperative work and social computing, 2017: ACM, pp. 1217-
    1230.
    [9] Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia, "Detecting automation of
    twitter accounts: Are you a human, bot, or cyborg?," IEEE Transactions on
    Dependable and Secure Computing, vol. 9, no. 6, pp. 811-824, 2012.
    [10] M. Del Vicario et al., "Echo chambers: Emotional contagion and group
    polarization on facebook," Scientific reports, vol. 6, p. 37825, 2016.
    [11] W. Quattrociocchi, A. Scala, and C. R. Sunstein, "Echo chambers on
    Facebook," Available at SSRN 2795110, 2016.
    [12] C. Paul and M. Matthews, "The Russian “firehose of falsehood” propaganda
    model," Rand Corporation, pp. 2-7, 2016.
    [13] Y. Chen, N. J. Conroy, and V. L. Rubin, "Misleading online content:
    Recognizing clickbait as false news," in Proceedings of the 2015 ACM on
    Workshop on Multimodal Deception Detection, 2015: ACM, pp. 15-19.
    [14] J. Fürnkranz, "A study using n-gram features for text categorization," Austrian
    Research Institute for Artifical Intelligence, vol. 3, no. 1998, pp. 1-10, 1998.
    [15] M. Potthast, J. Kiesel, K. Reinartz, J. Bevendorff, and B. Stein, "A stylometric
    inquiry into hyperpartisan and fake news," arXiv preprint arXiv:1702.05638,
    2017.
    [16] S. Afroz, M. Brennan, and R. Greenstadt, "Detecting hoaxes, frauds, and
    deception in writing style online," in 2012 IEEE Symposium on Security and
    Privacy, 2012: IEEE, pp. 461-475.
    [17] C. Castillo, M. Mendoza, and B. Poblete, "Information credibility on twitter,"
    in Proceedings of the 20th international conference on World wide web, 2011:
    ACM, pp. 675-684.
    [18] F. Yang, Y. Liu, X. Yu, and M. Yang, "Automatic detection of rumor on
    Sina Weibo," in Proceedings of the ACM SIGKDD Workshop on Mining Data
    Semantics, 2012: ACM, p. 13.
    [19] J. Ma, W. Gao, Z. Wei, Y. Lu, and K.-F. Wong, "Detect rumors using time
    series of social context information on microblogging websites," in
    Proceedings of the 24th ACM International on Conference on Information and
    Knowledge Management, 2015: ACM, pp. 1751-1754.
    [20] S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang, "Prominent features of
    rumor propagation in online social media," in 2013 IEEE 13th International
    Conference on Data Mining, 2013: IEEE, pp. 1103-1108.
    [21] N. Ruchansky, S. Seo, and Y. Liu, "Csi: A hybrid deep model for fake news
    detection," in Proceedings of the 2017 ACM on Conference on Information
    and Knowledge Management, 2017: ACM, pp. 797-806.
    [22] Z. Jin, J. Cao, Y. Zhang, and J. Luo, "News verification by exploiting
    conflicting social viewpoints in microblogs," in Thirtieth AAAI Conference on
    Artificial Intelligence, 2016.
    [23] J. Ma et al., "Detecting rumors from microblogs with recurrent neural
    networks," in Ijcai, 2016, pp. 3818-3824.
    [24] E. Tacchini, G. Ballarin, M. L. Della Vedova, S. Moret, and L. de Alfaro,
    "Some like it hoax: Automated fake news detection in social networks," arXiv
    preprint arXiv:1704.07506, 2017.
    [25] A. Zubiaga, A. Aker, K. Bontcheva, M. Liakata, and R. Procter, "Detection
    and resolution of rumours in social media: A survey," ACM Computing
    Surveys (CSUR), vol. 51, no. 2, p. 32, 2018.
    [26] A. Vlachos and S. Riedel, "Fact checking: Task definition and dataset
    construction," in Proceedings of the ACL 2014 Workshop on Language
    Technologies and Computational Social Science, 2014, pp. 18-22.
    [27] N. Hassan, C. Li, and M. Tremayne, "Detecting check-worthy factual claims
    in presidential debates," in Proceedings of the 24th acm international on
    conference on information and knowledge management, 2015: ACM, pp.
    1835-1838.
    [28] M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni,
    "Open information extraction from the web," in Ijcai, 2007, vol. 7, pp. 2670-
    2676.
    [29] A. Magdy and N. Wanas, "Web-based statistical fact checking of textual
    documents," in Proceedings of the 2nd international workshop on Search and
    mining user-generated contents, 2010: ACM, pp. 103-110.
    [30] Y. Wu, P. K. Agarwal, C. Li, J. Yang, and C. Yu, "Toward computational
    fact-checking," Proceedings of the VLDB Endowment, vol. 7, no. 7, pp. 589-
    600, 2014.
    [31] G. L. Ciampaglia, P. Shiralkar, L. M. Rocha, J. Bollen, F. Menczer, and A.
    Flammini, "Computational fact checking from knowledge networks," PloS
    one, vol. 10, no. 6, p. e0128193, 2015.
    [32] B. Shi and T. Weninger, "Fact checking in heterogeneous information
    networks," in Proceedings of the 25th International Conference Companion
    on World Wide Web, 2016: International World Wide Web Conferences
    Steering Committee, pp. 101-102.
    [33] Z. Zhao, P. Resnick, and Q. Mei, "Enquiring minds: Early detection of
    rumors in social media from enquiry posts," in Proceedings of the 24th
    International Conference on World Wide Web, 2015: International World
    Wide Web Conferences Steering Committee, pp. 1395-1405.
    [34] A. Zubiaga, M. Liakata, and R. Procter, "Exploiting context for rumour
    detection in social media," in International Conference on Social Informatics,
    2017: Springer, pp. 109-123.
    [35] S. Kwon, M. Cha, and K. Jung, "Rumor detection over varying time
    windows," PloS one, vol. 12, no. 1, p. e0168344, 2017.
    [36] T. Chen, X. Li, H. Yin, and J. Zhang, "Call attention to rumors: Deep
    attention based recurrent neural networks for early rumor detection," in Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2018:
    Springer, pp. 40-52.
    [37] M. Mendoza, B. Poblete, and C. Castillo, "Twitter under crisis: Can we trust
    what we RT?," in Proceedings of the first workshop on social media analytics,
    2010: ACM, pp. 71-79.
    [38] R. Procter, F. Vis, and A. Voss, "Reading the riots on Twitter: methodological
    innovation for the analysis of big data," International journal of social
    research methodology, vol. 16, no. 3, pp. 197-214, 2013.
    [39] L. Derczynski et al., "PHEME: Computing Veracity—the Fourth Challenge
    of Big Social Data," in Proceedings of the Extended Semantic Web
    Conference EU Project Networking session (ESCW-PN), 2015.
    [40] M. Lukasik, P. Srijith, D. Vu, K. Bontcheva, A. Zubiaga, and T. Cohn,
    "Hawkes processes for continuous time sequence classification: an application
    to rumour stance classification in twitter," in Proceedings of the 54th Annual
    Meeting of the Association for Computational Linguistics (Volume 2: Short
    Papers), 2016, vol. 2, pp. 393-398.
    [41] A. Zubiaga et al., "Discourse-aware rumour stance classification in social
    media using sequential classifiers," Information Processing & Management,
    vol. 54, no. 2, pp. 273-290, 2018.
    [42] W. Y. Wang, "" liar, liar pants on fire": A new benchmark dataset for fake
    news detection," arXiv preprint arXiv:1705.00648, 2017.
    [43] G. Giasemidis et al., "Determining the veracity of rumours on Twitter," in
    International Conference on Social Informatics, 2016: Springer, pp. 185-205.
    [44] C. Boididou, S. Papadopoulos, Y. Kompatsiaris, S. Schifferes, and N.
    Newman, "Challenges of computational verification in social multimedia," in
    Proceedings of the 23rd International Conference on World Wide Web, 2014:
    ACM, pp. 743-748.
    [45] C. Boididou, S. Papadopoulos, L. Apostolidis, and Y. Kompatsiaris,
    "Learning to detect misleading content on twitter," in Proceedings of the 2017
    ACM on International Conference on Multimedia Retrieval, 2017: ACM, pp.
    278-286.
    [46] O. Russakovsky et al., "Imagenet large scale visual recognition challenge,"
    International journal of computer vision, vol. 115, no. 3, pp. 211-252, 2015.
    [47] Y. Kim, "Convolutional neural networks for sentence classification," arXiv
    preprint arXiv:1408.5882, 2014.
    [48] I. Solaiman et al., "Release strategies and the social impacts of language
    models," arXiv preprint arXiv:1908.09203, 2019
    [49] J. Baxter, "A Bayesian/information theoretic model of learning to learn via
    multiple task sampling," Machine learning, vol. 28, no. 1, pp. 7-39, 1997.
    [50] R. Collobert and J. Weston, "A unified architecture for natural language
    processing: Deep neural networks with multitask learning," in Proceedings of
    the 25th international conference on Machine learning, 2008: ACM, pp. 160-
    167.
    [51] L. Derczynski, K. Bontcheva, M. Liakata, R. Procter, G. W. S. Hoi, and A.
    Zubiaga, "SemEval-2017 Task 8: RumourEval: Determining rumour veracity
    and support for rumours," arXiv preprint arXiv:1704.05972, 2017.
    [52] R. Caruana, "Multitask learning: A knowledge-based source of inductive bias.
    Machine Learning," 1997.
    [53] S. Ruder, "An overview of multi-task learning in deep neural networks," arXiv
    preprint arXiv:1706.05098, 2017.
    [54] E. Kochkina, M. Liakata, and A. Zubiaga, "All-in-one: Multi-task learning for
    rumour verification," arXiv preprint arXiv:1806.03713, 2018.
    [55] O. Enayet and S. R. El-Beltagy, "NileTMRG at SemEval-2017 Task 8:
    Determining Rumour and Veracity Support for Rumours on Twitter," in
    Proceedings of the 11th International Workshop on Semantic Evaluation
    (SemEval-2017), 2017, pp. 470-474.
    [56] A. Zubiaga, M. Liakata, R. Procter, G. W. S. Hoi, and P. Tolmie, "Analysing
    how people orient to and spread rumours in social media by looking at
    conversational threads," PloS one, vol. 11, no. 3, p. e0150989, 2016.
    [57] E. Kochkina, M. Liakata, and I. Augenstein, "Turing at semeval-2017 task 8:
    Sequential approach to rumour stance classification with branch-lstm," arXiv
    preprint arXiv:1704.07221, 2017.
    [58] T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient estimation of word
    representations in vector space," arXiv preprint arXiv:1301.3781, 2013.
    [59] D. Britz, "Understanding convolutional neural networks for NLP," URL:
    http://www. wildml. com/2015/11/understanding-convolutionalneuralnetworks-for-nlp/(visited on 11/07/2015), 2015.
    [60] T. Perry, "Convolutional methods for text," ed: Medium, 2017.
    Description: 碩士
    國立政治大學
    資訊科學系碩士在職專班
    106971004
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106971004
    Data Type: thesis
    DOI: 10.6814/NCCU202000234
    Appears in Collections:[資訊科學系碩士在職專班] 學位論文

    Files in This Item:

    File SizeFormat
    100401.pdf1814KbAdobe PDF0View/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
    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。
    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。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback