Review of the Application of Machine Learning in Rumor Detection
Author: 
Yuhang YuAuthors Info & Claims
Yuhang YuAuthors Info & ClaimsPages 46 - 52
Abstract
Because of the convenience of the social platform, the spread of the rumors has become more and more serious. Rumors will not only propagate misinformation but may also affect people's normal lives and even cause panic among the people. Many researchers have explored the machine learning techniques to detect the rumors automatically. In this survey, we will discuss these machine learning models from four perspectives: (1) the datasets used in training and verifying the models, (2) the features to detect the rumors, (3) the algorithms of rumor detection, (4) the metrics used to evaluate the results of the models. After reviewing the past work of rumor detection models, we highlight some rumor detection development directions at the end of the survey.
References
[1]
Castillo C, Mendoza M, Poblete B. Information credibility on twitter[C]//Proceedings of the 20th international conference on World wide web. 2011: 675--684.
[2]
Ma J, Gao W, Wong K F. Detect rumor and stance jointly by neural multi-task learning[C]//Companion Proceedings of the Web Conference 2018. 2018: 585--593.
[3]
Ma J, Gao W, Wong K F. Detect rumors in microblog posts using propagation structure via kernel learning[C]. Association for Computational Linguistics, 2017.
[4]
Ma J, Gao W, Mitra P, et al. Detecting rumors from microblogs with recurrent neural networks[J]. 2016.
[5]
Shu K, Mahudeswaran D, Wang S, et al. FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media[J]. arXiv preprint arXiv:1809.01286, 2018.
[6]
Popat K, Mukherjee S, Strötgen J, et al. Where the truth lies: Explaining the credibility of emerging claims on the web and social media[C]//Proceedings of the 26th International Conference on World Wide Web Companion. 2017: 1003--1012.
[7]
Zeng L, Starbird K, Spiro E S. # unconfirmed: Classifying rumor stance in crisis-related social media messages[C]//Tenth International AAAI Conference on Web and Social Media. 2016.
[8]
Kwon S, Cha M, Jung K, et al. Prominent features of rumor propagation in online social media[C]//2013 IEEE 13th International Conference on Data Mining. IEEE, 2013: 1103--1108.
[9]
Chen Y C, Liu Z Y, Kao H Y. IKM at SemEval-2017 Task 8: Convolutional neural networks for stance detection and rumor verification[C]//Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). 2017: 465--469.
[10]
Ruchansky N, Seo S, Liu Y. Csi: A hybrid deep model for fake news detection[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2017: 797--806.
[11]
Li Q, Liu X, Fang R, et al. User behaviors in newsworthy rumors: A case study of twitter[C]//Tenth International AAAI Conference on Web and Social Media. 2016.
[12]
Liu Y, Wu Y F B. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks[C]//Thirty-Second AAAI Conference on Artificial Intelligence. 2018.
[13]
Ma J, Gao W, Wong K F. Rumor detection on twitter with tree-structured recursive neural networks[C]. Association for Computational Linguistics, 2018.
[14]
Yang S, Shu K, Wang S, et al. Unsupervised fake news detection on social media: A generative approach[C]//Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 33: 5644--5651.
[15]
De Sarkar S, Yang F, Mukherjee A. Attending sentences to detect satirical fake news[C]//Proceedings of the 27th International Conference on Computational Linguistics. 2018: 3371--3380.
[16]
Qian F, Gong C, Sharma K, et al. Neural User Response Generator: Fake News Detection with Collective User Intelligence[C]//IJCAI. 2018: 3834--3840.
[17]
Rashkin H, Choi E, Jang J Y, et al. Truth of varying shades: Analyzing language in fake news and political fact-checking[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017: 2931--2937.
[18]
Popat K, Mukherjee S, Yates A, et al. DeClarE: Debunking fake news and false claims using evidence-aware deep learning[J]. arXiv preprint arXiv:1809.06416, 2018.
[19]
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Advances in neural information processing systems. 2017: 5998--6008.
[20]
Shu K, Wang S, Liu H. Beyond news contents: The role of social context for fake news detection[C]//Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining. 2019: 312--320.
[21]
Volkova S, Shaffer K, Jang J Y, et al. Separating facts from fiction: Linguistic models to classify suspicious and trusted news posts on twitter[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2017: 647--653.
[22]
Liu X Y, Wu J, Zhou Z H. Exploratory undersampling for class-imbalance learning[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2008, 39(2): 539--550.
[23]
Chawla N V, Bowyer K W, Hall L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of artificial intelligence research, 2002, 16: 321--357.
[24]
Pérez-Rosas V, Kleinberg B, Lefevre A, et al. Automatic detection of fake news[J]. arXiv preprint arXiv:1708.07104, 2017.
[25]
Chorpita B F, Daleiden E L, Weisz J R. Identifying and selecting the common elements of evidence based interventions: A distillation and matching model[J]. Mental health services research, 2005, 7(1): 5--20.
[26]
Paul S, Joy J I, Sarker S, et al. Fake News Detection in Social Media using Blockchain[C]//2019 7th International Conference on Smart Computing&Communications (ICSCC). IEEE, 2019: 1--5
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Published: 15 February 2021
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CCEAI 2021
CCEAI 2021: 5th International Conference on Control Engineering and Artificial Intelligence
January 14 - 16, 2021
Sanya, China
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