Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (4): 89-98.doi: 10.6040/j.issn.1672-3961.0.2021.296

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Early detection of fake news based on hybrid deep model

HUANG Hao1, ZHOU Lihua1*, HUANG Yaqun1, JIANG Yiting2   

  1. 1. School of Information, Yunnan University, Kunming 650000, Yunnan, China;
    2. School of Information, Yunnan Normal University, Kunming 650000, Yunnan, China
  • Published:2022-08-24

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  • TP181
[1] CASTILLO C, MENDOZA M,POBLETE B. Information credibility on twitter[C] //Proceedings of the 20th International Conference on World Wide Web. Hyderabad, India: ACM, 2011: 675-684.
[2] QAZVINIAN V, ROSENGREN E, RADEV D R, et al. Rumor has it: identifying misinformation in microblogs[C] //Proceedings of the Conference on Empirical Methods in Natural Language Processing. Edinburgh, UK: EMNLP, 2011: 1589-1599.
[3] GUPTA A, KUMARAGURU P, CASTILLO C, et al. TweetCred: real-time credibility assessment of content on twitter[C] //Proceedings of the International Conference on Social Informatics. Barcelona, Spain: SocInfo, 2014: 228-243.
[4] POPAT K. Assessing the credibility of claims on the Web[C] //Proceedings of the 26th International Conference on World Wide Web. Perth, Australia: International World Wide Web Conferences Steering Committee, 2017: 735-739.
[5] YANG F, YU X, LIU Y, et al. Automatic detection of rumor on Sina Weibo[C] //Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. Sydney, Australia: ACM, 2015: 1-7.
[6] ZHAO Z, RESNICK P, MEI Q. Enquiring minds: early detection of rumors in social media from enquiry posts[C] //Proceedings of the 24th International Conference on World Wide Web. Geneva,Switzerland:International World Wide Web Conferences Steering Committee, 2015: 1395-1405.
[7] MA J, GAO W, MITRA P, et al. Detecting rumors from microblogs with recurrent neural networks[C] //Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. New York, USA: AAAI Press, 2016: 3818-3824.
[8] MA J, GAO W, WONG K F. Detect rumors in microblog posts using propagation structure via kernel learning[C] //Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada: IJCAI, 2017: 708-717.
[9] JIN F, DOUGHERTY E R, SARAF P, et al. Epidemiological modeling of news and rumors on Twitter[C] //Proceedings of the 7th Workshop on Social Network Mining and Analysis. Chicago, USA: ACM, 2013: 1-9.
[10] WU K, YANG S, ZHU K Q. False rumors detection on Sina Weibo by propagation structures[C] //Proceedings of the 31st IEEE International Conference on Data Engineering. Seoul, Korea: ICDEW, 2015: 651-662.
[11] CHEN T, LI X, YIN H, et al. Call attention to rumors: deep attention based recurrent neural networks for early rumor detection[C] // Proceedings of Trends and Applications in Knowledge Discovery and Data Mining. Melbourne, Australia: LNCS, 2018: 40-52.
[12] LI Q, HU Q, LU Y, et al. Multi-level word features based on CNN for fake news detection in cultural communication[J]. Personal and Ubiquitous Computing, 2020, 24(2): 259-272.
[13] CHOWDHURY R, SRINIVASAN S, GETOOR L. Joint estimation of user and publisher credibility for fake news detection[C] //Proceedings of the 29th ACM Intern-ational Conference on Information & Knowledge Management. New York, America: CIKM, 2020: 1993-1996.
[14] BALESTRUCCI A, NICOLA R D. Credulous users and fake news: a real case study on the propagation in Twitter[C] //Proceedings of 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems(EAIS). Bari, Italy: IEEE, 2020: 1-8.
[15] HAMDI T, SLIMI H, BOUNHAS I, et al. A hybrid approach for fake news detection in Twitter based on user features and graphembedding[C] //Proceedings of Distributed Computing and Internet Technology. Bhubaneswar, India: ICDCIT, 2020: 266-280
[16] JIANG S, CHEN X, ZHANG L, et al. User-characteristic enhanced model for fake news detection in social media[C] //Proceedings of Natural Language Processing and Chinese Computing. Dunhuang, China: NLPCC, 2019: 634-646.
[17] SAMPSON J, MORSTATTER F, WU L, et al. Leveraging the implicit structure within social media for emergent rumor detection[C] //Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Turin, Italy: CIKM, 2016: 2377-2382.
[18] LIU Y, WU Y F. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks[C] //Proceedings of the AAAI Conference on Artificial Intelligence. Louisiana, USA: AAAI, 2018: 354-361.
[19] QIAN F, GONG C, SHARMA K, et al. Neuraluser response generator: fake news detection with collective user intelligence[C] //Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Washington, USA: IJCAI, 2018: 3834-3840.
[20] CASTILLO C, EL-HADDAD M, PFEFFER J, et al. Characterizing the life cycle of online news stories using social media reactions[C] //Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing. Baltimore, Maryland: CSCW, 2014: 211-223.
[21] FRIGGERI A, ADAMIC L A, ECKLES D, et al. Rumor cascades[C] //Proceedings of International AAAI Conference on Web and Social Media. Ann Arbor, USA: ICWSM, 2014: 1-13.
[22] KUMAR S, WEST R, LESKOVEC J. Disinformation on the web: impact, characteristics, and detection of wikipediahoaxes[C] //Proceedings of the 25th International Conference on World Wide Web. Quebec, Canada: International World Wide Web Conferences Steering Committee, 2016: 591-602.
[23] STARBIRD K, MADDOCK J, ORAND M, et al. Rumors, false flags, and digital vigilantes: misinformation on Twitter after the 2013 Boston Marathon Bombing[C] //Proceedings of the IConference 2014. Illinois, America: ISchool, 2014: 654-662.
[24] KWON S, CHA M, JUNG K. Rumor detection over varying time windows[J]. PLOS ONE, 2017,12(1):1-19.
[25] RUCHANSKY N, SEO S, LIU Y. CSI: ahybrid deep model for fake news detection[C] //Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore: CIKM, 2017: 797-806.
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