政大學術集成


Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/128991


Title: 以深度遞歸神經網路實施多重任務學習偵測假新聞
Deep Recurrent Neural Networks with Multi-Task Learning for Fake News Detection
Authors: 劉永鈞
Liou, Yung-Jiun
Contributors: 胡毓忠
Hu, Yuh-Jong
劉永鈞
Liou, Yung-Jiun
Keywords: 社交媒體
假新聞
錯誤資訊
多重任務學習
假新聞資料集
遞歸神經網路
傳統深度學習
Social Media
Fake News
Misinformation
Muti-Task Learning
PHEME
Fake News Dataset
Recurrent Neural Network
GRU
Traditional Deep Learning
Date: 2020
Issue Date: 2020-03-02 11:38:01 (UTC+8)
Abstract: 偵測假新聞是一項十分艱鉅的任務,包含偵測假新聞(Rumour Detection)、假新聞追蹤(Rumour Tracking)及立場分類(Stance Classification),從這些方法最終對假新聞作驗證(Rumour Verification)。欲做到辨識新聞的驗證以使讀者能閱讀到正確的新聞及資訊,本研究希望探索以多重任務學習(Multi-Task Learning, MTL)用於處理數量龐大的假新聞資料上,並比較與傳統深度學習的差異,達到自動辨識及判別假新聞的目的。
本研究使用RumourEval、PHEME兩種假新聞資料集來進行深度遞歸神經網路(Recurrent Neural Network, RNN)中的GRU(Gate Recurrent Unit)演算法實作,並進行多重任務學習的訓練,對假新聞進行分類,進而找出處理識別假新聞的最佳參數。最後透過各種模擬實驗來比較改良過後的深度學習演算法(即GRU)與傳統深度學習的差異,並依據實驗結果進行量化與質化的分析。
Detecting fake news is a very difficult task, including Rumour Detection,Rumour Tracking and Stance Classification, and finally leading to Rumour Verification. To identify the authenticity of news so that readers can read the correct news and information, this research hopes to explore the use of Multi-Task Learning technology for processing a large number of fake news datasets and compare it with traditional deep learning, to achieve the purpose of automatically identifying and distinguishing fake news.
This research uses two fake news datasets, RumourEval and PHEME,to implement the GRU (Gated Recurrent Unit) algorithm of the Recurrent Neural Network (RNN), and trains for multiple tasks to perform fake news classification to find the best parameters for handling fake news. Finally, through various simulation experiments, the differences between the improved and traditional deep learning algorithm will be compared, and quantitative and qualitative analysis is performed based on the experimental results
Reference: [1] P. N. Howard, G. Bolsover, B. Kollanyi, S. Bradshaw, and L.-M. Neudert,
\Junk news and bots during the us election: What were michigan voters
sharing over twitter," CompProp, OII, Data Memo, 2017.
[2] E. Kochkina, M. Liakata, and A. Zubiaga, \All-in-one: Multi-task learning
for rumour verification," arXiv preprint arXiv:1806.03713, 2018.
[3] 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.
[4] 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. International World Wide Web Conferences
Steering Committee, 2015, pp. 1395{1405.
[5] 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.
[6] 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.
ACM, 2010, pp. 71{79.
[7] 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.
[8] 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), vol. 2, 2016, pp. 393{398.
[9] 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.
[10] T. Chen, X. Li, H. Yin, and J. Zhang, \Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection," in PacificAsia Conference on Knowledge Discovery and Data Mining. Springer, 2018,
pp. 40{52.
[11] G. Giasemidis, C. Singleton, I. Agrafiotis, J. R. Nurse, A. Pilgrim, C. Willis,
and D. V. Greetham, \Determining the veracity of rumours on twitter," in
International Conference on Social Informatics. Springer, 2016, pp. 185{205.
[12] 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. ACM,
2014, pp. 743{748.
[13] J. Ma, W. Gao, P. Mitra, S. Kwon, B. J. Jansen, K.-F. Wong, and M. Cha,
\Detecting rumors from microblogs with recurrent neural networks." in Ijcai,
2016, pp. 3818{3824.
[14] S. Kwon, M. Cha, and K. Jung, \Rumor detection over varying time windows," PloS one, vol. 12, no. 1, p. e0168344, 2017.
[15] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, \Empirical evaluation
of gated recurrent neural networks on sequence modeling," arXiv preprint
arXiv:1412.3555, 2014.
[16] C. Wardle and H. Derakhshan, \Information disorder: Toward an interdisciplinary framework for research and policy making," Council of Europe Report,
vol. 27, 2017.
[17] 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.
[18] A. Zubiaga, M. Liakata, and R. Procter, \Exploiting context for rumour
detection in social media," in International Conference on Social Informatics.
Springer, 2017, pp. 109{123.
[19] 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. ACM, 2008, pp.
160{167.
[20] 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.
[21] R. Caruana, \Multitask learning: A knowledge-based source of inductive bias.
machine learning," 1997.
[22] 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.
[23] L. Duong, T. Cohn, S. Bird, and P. Cook, \Low resource dependency parsing:
Cross-lingual parameter sharing in a neural network parser," in Proceedings
of the 53rd Annual Meeting of the Association for Computational Linguistics
and the 7th International Joint Conference on Natural Language Processing
(Volume 2: Short Papers), 2015, pp. 845{850.
[24] Y. Yang and T. M. Hospedales, \Trace norm regularised deep multi-task
learning," arXiv preprint arXiv:1606.04038, 2016.
[25] A. Zubiaga, M. Liakata, and R. Procter, \Learning reporting dynamics during breaking news for rumour detection in social media," arXiv preprint
arXiv:1610.07363, 2016.
[26] R. McCreadie, C. Macdonald, and I. Ounis, \Crowdsourced rumour identification during emergencies," in Proceedings of the 24th International Conference
on World Wide Web. ACM, 2015, pp. 965{970.
[27] S. Ruder, \An overview of multi-task learning in deep neural networks," arXiv
preprint arXiv:1706.05098, 2017.
[28] Y. S. Abu-Mostafa, \Learning from hints in neural networks," Journal of
complexity, vol. 6, no. 2, pp. 192{198, 1990.
[29] J. Baxter, \A model of inductive bias learning," Journal of artificial intelligence research, vol. 12, pp. 149{198, 2000.
[30] K. Cho, B. Van Merri¨enboer, D. Bahdanau, and Y. Bengio, \On the properties
of neural machine translation: Encoder-decoder approaches," arXiv preprint
arXiv:1409.1259, 2014.
[31] D. Bahdanau, K. Cho, and Y. Bengio, \Neural machine translation by jointly
learning to align and translate," arXiv preprint arXiv:1409.0473, 2014.
[32] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \Efficient estimation of word
representations in vector space," arXiv preprint arXiv:1301.3781, 2013.
Description: 碩士
國立政治大學
資訊科學系碩士在職專班
105971005
Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105971005
Data Type: thesis
Appears in Collections:[Executive Master Program of Computer Science of NCCU] Theses

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