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Empower rumor events detection from Chinese microblogs with multi-type individual information

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Abstract

Online social media has become an ideal place in spreading rumor events with its convenience in communication and information dissemination, which raises the difficulty in debunking rumor events automatically. To deal with such a challenge, traditional classification approaches relying on manually labeled features have to face a daunting number of human efforts. With the consideration of the realness of a rumor event, it will be verified and authenticated with multi-type individual information, especially with individuals’ emotional expressions to events and their own credibility. This paper presents a novel two-layer GRU model for rumor events detection based on multi-type individual information (MII) and a dynamic time-series (DTS) algorithm, named as MII–DTS-GRU. Specifically, MII refers to adopt the sentiment dictionary to identify fine-grained human emotional expressions to events and fuse with the individual credibility. Besides, the DTS algorithm retains the time distribution of social events. Experimental results on Sina Weibo datasets show that our model achieves a high accuracy of 96.3% and demonstrate that our proposed MII–DTS-GRU model outperforms the state-of-the-art models on rumor events detection.

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Notes

  1. https://www.snopes.com/.

  2. http://service.account.weibo.com/?type=5&status=0.

  3. https://en.wikipedia.org/wiki/Book_of_Rites.

  4. https://code.google.com/archive/p/word2vec/.

  5. https://drive.google.com/open?id=1_LNC9D1baGSpaKFE6KrhsKn21x19-J8m.

  6. http://service.account.weibo.com/?type=5&status=0.

  7. ESWD: https://drive.google.com/file/d/1rG3ulAbzXuQdx09GUD2kJNAO6vZXL9fQ.

  8. SED: https://drive.google.com/file/d/1VKNX92by5gQfP2Dmab51gl-49eOk8O6y.

References

  1. Li Y, Chen YH, Liu T (2016) Survey on predicting information propagation in microblogs. J Softw 27(2):247–263

    MathSciNet  Google Scholar 

  2. Zhang L, Guo XT (2012) Rumor, credibility and mass incident: based on the research about salt rumors and salt panic. Chin J Journal Commun 7:12–18

    Google Scholar 

  3. Yang F, Liu Y, Yu X, Yang M (2012) Automatic detection of rumor on Sina Weibo. In: Proceedings of the ACM SIGKDD workshop on mining data semantics. ACM

  4. Liu X, Nourbakhsh A, Li QZ, Fang R, Shah S (2015) Real-time rumor debunking on twitter. In: Proceedings of the 24th ACM international on conference on information and knowledge management (CIKM), pp 1867–1870

  5. Ma J, Gao W, Wei Z Y, Lu YM, Wong K F (2015) 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 (CIKM), pp 1751–1754

  6. Kwon S, Cha M, Jung K (2017) Rumor detection over varying time windows. PLoS ONE 12(1):e0168344

    Article  Google Scholar 

  7. Liang G, He WB, Xu C, Chen LY, Zeng JQ (2015) Rumor identification in microblogging systems based on users’ behavior. IEEE Trans Comput Soc Syst 2(3):99–108

    Article  Google Scholar 

  8. Kwon S, Cha M, Jung K, Chen W, Wang YJ (2014) Prominent features of rumor propagation in online social media. In: IEEE 13th international conference on data mining (ICDM), USA, pp 1103–1108

  9. Wu K, Yang S, Zhu KQ (2015) False rumors detection on Sina Weibo by propagation structures. In: IEEE 31st international conference on data engineering (ICDE), South Korea, pp 651–662

  10. Ma J, Gao W, Wong KF (2017) Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th annual meeting of the association for computational linguistics (ACL) vol 1, pp 708–717

  11. Wang ZH, Guo Y (2018) Automatic rumor events detection on Chinese microblogs. J Chin Inf Process

  12. Mao ES, Chen G, Liu X, Wang B (2016) Research on detecting micro-blog rumors based on deep features and ensemble classifier. Appl Res Comput 33(11):3369–3373

    Google Scholar 

  13. Yang WT, Liang G, Xie K, Yang J, Xu C (2017) Rumor detection method based on burst topic detection and domain expert discovery. J Comput Appl 37(10):2799–2805

    Google Scholar 

  14. Wu QY, He LN (2017) Analysis of short text rumors via multi-label bi-term topic model. J Intell 36(3):92–97

    Google Scholar 

  15. Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong KF, Cha MY (2016) Detecting rumors from microblogs with recurrent neural networks. In: The international joint conference on artificial intelligence (IJCAI), pp 3818–3824

  16. Chen T, Li X, Yin H, Wang Y (2018) Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Pacific-Asia conference on knowledge discovery and data mining (PAKDD). Springer, Cham, pp 40–52

  17. Shou HR, Deng SQ, Xu J (2017) Detecting online rumors with sentiment analysis. Data Anal Knowl Discov 1(7):44–51

    Google Scholar 

  18. Zu KL, Zhao MW, Guo K, Lin HF (2017) Research on the detection of rumor on Sina Weibo. J Chin Inf Process 31(3):198–204

    Google Scholar 

  19. Chen W, Yan Z, Chai KY, Chiew TL, Bu SL (2018) Unsupervised rumor detection based on users’ behaviors using neural networks. Pattern Recognit Lett 2018:105

    Google Scholar 

  20. Liu Z, Wei ZH, Zhang RX (2017) Rumor detection based on convolutional neural network. J Comput Appl 37(11):3053–3056

    Google Scholar 

  21. Ye QY, Lan YX, Wang Q (2017) Netizen emotional evolution for emergency dynamics model research. J Intell 36(9):153–159

    Google Scholar 

  22. Xu LH, Lin HF, Pan Y, Ren H, Chen JM (2008) Constructing the affective lexicon ontology. J China Soc Sci Tech Inf 27(2):180–185

    Google Scholar 

  23. Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151

    Article  Google Scholar 

  24. Ferguson TS (1996) A course in large sample theory. Chapman & Hall, London, p 1996

    Book  Google Scholar 

  25. Baidu Baike (2018) Qualitative research. https://baike.baidu.com/item/%E5%AE%9A%E6-%80%A7%E7%A0%94%E7%A9%B6/8137250. 11 Aug 2018

  26. Wei C, Lan YX, Zeng RX (2016) Research on the psychological evaluation of internet users in the event of network rumor. Libr Inf 2016(4):16–23

    Google Scholar 

  27. Wikipedia (2018) Scott Fahlman. https://en.wikipedia.org/wiki/Scott_Fahlman. 19 Sept 2018

  28. Yan Z, Chen W, Chai KY (2017) Detecting rumors on online social networks using multi-layer autoencoder. IEEE Technol Eng Manag Conf 2017:437–441

    Google Scholar 

  29. Bezdek JC (1981) Objective function clustering. In: Pattern recognition with fuzzy objective function algorithms. Springer, Boston, pp 43–93

  30. Box GE, Meyer RD (1986) An analysis for unreplicated fractional factorials. Technometrics 28(1):11–18

    Article  MathSciNet  MATH  Google Scholar 

  31. Zhao Z, Resnick P, Mei Q (2015) Enquiring minds: early detection of rumors in social media from enquiry posts. In: International conference on world wide web (WWW). International World Wide Web Conferences Steering Committee, pp 1395–1405

  32. Sampson J, Morstatter F, Wu L (2016) Leveraging the implicit structure within social media for emergent rumor detection. In: ACM international on conference on information and knowledge management (CIKM). ACM, pp 2377–2382

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Acknowledgements

This research is financially supported by National Natural Science Foundation of China (Grant Number 61462073) and Science and Technology Committee of Shanghai Municipality (STCSM) (Grant Numbers 17DZ1101003, 18511106602 and 18DZ2252300).

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Authors and Affiliations

  1. East China University of Science and Technology, Shanghai, China

    Zhihong Wang & Yi Guo

  2. National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai, China

    Yi Guo

  3. Shanghai Engineering Research Center of Big Data and Internet Audience, Shanghai, China

    Yi Guo

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  1. Zhihong Wang
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  2. Yi Guo
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Correspondence to Yi Guo.

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Wang, Z., Guo, Y. Empower rumor events detection from Chinese microblogs with multi-type individual information. Knowl Inf Syst 62, 3585–3614 (2020). https://doi.org/10.1007/s10115-020-01463-2

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