Rumor Detection on Social Networks Based on the Degree Distribution Analysis in Step-by-Step Propagation Subgraphs

Document Type : Original Article

Authors

1 PhD student, Electrical and Computer University Complex, Malik Ashtar University of Technology, Tehran, Iran

2 Professor, Electrical and Computer University Complex, Malik Ashtar University of Technology, Tehran, Iran

3 Assistant Professor, Electrical and Computer University Complex, Malik Ashtar University of Technology, Tehran, Iran

4 Assistant Professor, Faculty of Computer and Information Technology, Amirkabir University of Technology, Tehran, Iran

Abstract

With the expansion of social networks and the increase in their users, these networks have become an effective medium for publishing news and various content. Therefore, new challenges have been created in this space, one of the most important of which is spreading rumors and false information. Rumors are moving at an incredible rate in society due to their appeal and attraction. Their spread can have many destructive effects on human societies and sometimes have irreparable consequences. For this reason, many researchers today deal with rumors in these networks. The purpose of this article is to provide a new method that can detect rumors without user information and post content analysis, and only according to the post propagation subgraph. Therefore, the degree distribution of the propagation graphs in the rumored and non-rumored models is examined. Then different classifiers were used to distinguish between these two modes. The Random Forest classifier gives better results than others. Since this method can finally detect rumors within four steps after propagation, this method has a good performance in terms of time.

Keywords

Smiley face

References

[1]    K. Kumar & G. Geethakumari, "Analysis of Semantic Attacks in Online Social Networks," in Recent Trends in Computer Networks and Distributed Systems Security, vol. 420, pp. 45-56, 2014.
[2]    G. W.Allport & L.Postman, "The psychology of rumor," Journal of Clinical Psychology, vol. 3, pp. 402-402, 1947.
[3]    B. Guerin & Y. Miyazaki, "Analyzing rumors, gossip, and urban legends through their conversational properties," The Psychological Record, vol. 56, pp. 2-10, 2010.
[4]    J. Ma, W. Gao, & K.F. Wong, "Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning," in Association for Computational Linguistics, 2017.
[5]    N. DiFonzo & P. Bordia, "Rumor, Gossip and Urban Legends," Diogenes, vol. 54, pp. 19-35, 2007.
[6]    K. Kumar & G. Geethakumari, "Detecting misinformation in online social networks using cognitive psychology", Human-centric Computing and Information Sciences, vol. 4, pp. 1-22, 2014.
[7]    X. Dong, Y. Lian, Y. Chi, X. Tang, & Y. Liu, "A two‑step rumor detection model based on the supernetwork theory about Weibo," The Journal of Supercomputing, pp. 1-25, 2021.
[8]    B. Galitsky, “Detecting Rumor and Disinformation by Web Mining,” 2015 AAAI Spring Symposium, 2015.
[9]    S. Vosoughi, "Automatic Detection and Verification of Rumors on Twitter," PhD Thesis, the Program in Media Arts and Sciences In Massachusetts Institute of Technology, 2014.
[10]  E. L. Kochkina, M. Liakata and A. Zubiaga, "All-in-one: Multi-task Learning for Rumour Verificatio," in Proceedings of the 27th International Conference on Computational Linguistics, 2018.
[11]  A. Kumar, M. Bhatia, & S. R. Sangwan, "Rumour detection using deep learning and filter‑wrapper feature selection in benchmark twitter dataset," Multimedia Tools and Applications, pp. 1-18, 2021.
[12]  M. Z. Asghar, A. Habib, A. Habib, A. Khan, R. Ali, & A. Khattak, "Exploring deep neural networks for rumor detection," Journal of Ambient Intelligence and Humanized Computing, vol. 12, pp. 4315-4333, 2021.
[13]  Y. Peng & J. Wang, "Rumor Detection Based on Attention CNN and Time Series of Context Information," Future Internet, vol. 13, p. 267, 2021.
[14]  S. S. Natali Ruchansky & Yan Liu, "CSI: A Hybrid Deep Model for Fake News Detection," Proceedings of the 2017 {ACM} on Conference on Information and Knowledge Management, 2017.
[15]  Y. Zhou, C. Wu, Q. Zhu, Y. Xiang, & S. W. Loke, "Rumor Source Detection in Networks Based on the SEIR Model," IEEE access, vol. 7, pp. 45240-45258, 2019.
[16]  F.Zarifpour, M.Mojarad, & H. Arfaeinia, "Analysis of Rumor Spreading in Social Networks using Combination of SIR, SIHR and Autoencoder Models," IJSRNSC, vol. 8, pp. 1-6, 2020.
[17]  Y. Hu, Q. Pan, W. Hou, & M. He, "Rumor spreading model with the different attitudes towards rumors," Physica A: Statistical Mechanics and its Applications, vol. 502, pp. 331-344, 2018.
[18]  S. Han, F. Zhuang, Q. He, Z. Shi, & X. Ao, "Energy model for rumor propagation on social networks," Physica A: Statistical Mechanics and its Applications, vol. 394, pp. 99-109, 2014.
[19]  A. I. E. Hosni, K. Li, & S. Ahmad, "Minimizing rumor influence in multiplex online social networks based on human individual and social behaviors," Information Sciences, vol. 512, pp. 1458-1480, 2020.
[20]  L. Fan, Z. Lu, W. Wu, B. Thuraisingham, H. Ma, & Y. Bi, "Least cost rumor blocking in social networks," in 2013 IEEE 33rd International Conference on Distributed Computing Systems, 2013.
[21]  Y. Liu & S. Xu, "Detecting rumors through modeling information propagation networks in a social media environment," IEEE Transaction on computational social systems, vol. 3. pp. 46-62, 2016.
[22]  C. Budak, D. Agrawal, & A. El Abbad "Limiting the spread of misinformation in social networks," in Proceedings of the 20th international conference on World wide web, 2011.
[23]  M. Jalili & M. Perc, "Information cascades in complex networks," Journal of Complex Networks, vol. 5, pp. 665-693, 2017.
[24]  A. Ye, L. Wang, R. Wang, W. Wang, J. Ke, & D. Wang, "An End-to-End Rumor Detection Model Based on Feature Aggregation," Complexity, vol. 2, pp. 1-16, 2021.
[25]  D. T. Vu & J. J. Jung, "Rumor Detection by Propagation Embedding Based on Graph Convolutional Network," International Journal of Computational Intelligence Systems, vol. 14, pp. 1053-1065, 2021.
[26]  . Zhao, H. Cui, X. Qiu, X. Wang, & J. Wang, "SIR rumor spreading model in the new media age," Physica A: Statistical Mechanics and its Applications, vol. 3, pp. 995-1003, 2013.
[27]  A.-L. Barabási & R. Albert, "Emergence of scaling in random networks," science, vol. 286, pp. 509-512, 1999.
[28]  S. Aliakbary, J. Habibi, & A. Movaghar, "Feature extraction from degree distribution for comparison and analysis of complex networks," The Computer Journal, vol. 58, pp. 2079-2091, 2015.
[29]  S. Mughdha, M. Kuddus, L. Salsabil, A. Anika & S. shatabda, "A Gaussian Naive Bayesian Classifier for Fake News Detection in Bengali," in Emerging Technologies in Data Mining and Information Security, pp. 283-291, 2021.
[30]  C. Adele, D.C. Richard, & J. R. Stevens, "Random forests," in Ensemble machine learning, pp. 157-175, 2012.
[31]  V. Yadegari, & A. R. Matinfar, “Detect Web Denial of Service Attacks Using Entropy and Support Vector Machine Algorithm,” Journal of Electronical & Cyber Defence, vol. 6, pp. 79-89,  2019. (In Persian)
[32]   R. A. Fisher, "The use of multiple measurements in taxonomic problems," Annals of eugenics, vol. 7, pp. 179-188, 1936.
[33]  D. J. Watts & S. H. Strogatz, "Collective dynamics of ‘small-world’networks," nature, vol. 393, pp. 440-442, 1998.
[34]  A. W. S. H. Zubiaga, Geraldine; Liakata, Maria; Procter, Rob, "PHEME dataset of Rumours and nonrumours. fgshare. Dataset”, figshare, 2016.
[35]  C.Castillo, M. Mendoza, & B. Poblete, "Information Credibility on Twitter," in Proceedings of the 20th international conference on World wide web, 2011.
[36]  Z. Zhao, P. Resnick, & 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.
[37]  Dang-Thinh Vu & J. J. Jung, "Detecting Emerging Rumors by Embedding Propagation Graphs," in Information Retrieval Technology, pp. 173-184, 2019.
[38]  J. Ma, W. Gao, & K.-F. Wong, "Rumor detection on Twitter with tree-structured recursive neural networks," in 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), pp. 1980-1989,
2018.

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History

  • Receive Date: 08 December 2021
  • Revise Date: 21 February 2022
  • Accept Date: 09 August 2022
  • Publish Date: 22 December 2022

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