Abstract
Online social networks (OSNs) connect people around the globe under one virtual society. It helps people gather, communate and share their common interests. But many times, OSNs are also exploited and eventually become a major platform for rumor or false information propagation. Controlling such rumors in OSNs has been the most challenging research interest in recent days. Since OSNs are a platform of collective behavior, we focus on a collective rumor containment approach to control or eradicate rumors. In this paper, an anti-rumor information spreading approach is proposed to contain rumors collectively by following a bio-inspired immunization method called social immunity. First, A competitive information propagation model called competitive cascade (CC) model that spreads rumor and true information simultaneously is defined. This model continuously updates the trustworthiness of individuals in the network on every communication among the participants of OSNs. Then, the initial spreaders of anti-rumors are identified with the help of the intensity of the rumor in the network as well as the individual’s trustworthiness. Finally, a collective rumor containment approach is applied by considering the cost of rumor containment and a rumor intensity threshold. The proposed approach is compared with recent and well-known rumor control approaches and the results show that the proposed approach is effective in eradicating rumors.
Similar content being viewed by others
References
Guille A, Hacid H, Favre C, Zighed DA (2013) Information diffusion in online social networks: a survey. ACM Sigmod Record 42(2):17–28
Muller E, Peres R (2019) The effect of social networks structure on innovation performance: a review and directions for research. Int J Res Mark 36(1):3–19
Bakshy E, Rosenn I, Marlow C, Adamic L (2012) The role of social networks in information diffusion. In: Proceedings of the 21st international conference on World Wide Web, pp 519–528
Wen S, Haghighi MS, Chen C, Xiang Y, Zhou W, Jia W (2015) A sword with two edges: propagation studies on both positive and negative information in online social networks. IEEE Trans Comput 64(3):640–653
Doerr B, Fouz M, Friedrich T (2011) Social networks spread rumors in sublogarithmic time. In: Proceedings of the forty-third annual ACM symposium on Theory of computing, pp 21–30
Friggeri A, Adamic L, Eckles D, Cheng J (2014) Rumor cascades. In: Eighth International AAAI Conference on Weblogs and Social Media
Allport GW, Postman L (1947 The psychology of rumor. Henry Holt
Chen W, Zhang Y, Yeo CK, Lau CT, Lee BS (2018) Unsupervised rumor detection based on users’ behaviors using neural networks. Pattern Recogn Lett 105:226–233
Luo W, Tay WP, Leng M (2013) Identifying infection sources and regions in large networks. IEEE Trans Signal Process 61(11):2850–2865
Thakur HK, Gupta A, Bhardwaj A, Verma D (2018) Rumor detection on twitter using a supervised machine learning framework. Int J Inf Retrieval Res (IJIRR) 8(3):1–13
Wu K, Yang S, Zhu KQ (2015) False rumors detection on sina weibo by propagation structures. In: 2015 IEEE 31st international conference on data engineering, pp 651–662
Kostka J, Oswald YA, Wattenhofer R (2008) Word of mouth: rumor dissemination in social networks. In: International colloquium on structural information and communication complexity, pp 185–196
Fan L, Lu Z, Wu W, Thuraisingham B, Ma H, Bi Y (2013) Least cost rumor blocking in social networks. In: Distributed Computing Systems (ICDCS), 2013 IEEE 33rd International Conference on, pp 540–549
Hu Y, Pan Q, Hou W, He M (2018) Rumor spreading model considering the proportion of wisemen in the crowd. Phys A 505:1084–1094
Kotnis BA (2014) Cost effective rumor containment in social networks. arXiv preprint 1403.6315.
Li L, Scaglione A, Swami A, Zhao Q (2013) Consensus, polarization and clustering of opinions in social networks. IEEE J Sel Areas Commun 31(6):1072–1083
Wang J, Zhao L, Huang R (2014) SIRaRu rumor spreading model in complex networks. Phys A 398:43–55
Zhao L, Cui H, Qiu X, Wang X, Wang J (2013) SIR rumor spreading model in the new media age. Phys A Stat Mech Appl 392(4):995–1003
Starks PT, Blackie CA, Seeley TD (2000) Fever in honeybee colonies. Naturwissenschaften 87(5):229–231
Morente-Molinera JA, Kou G, Peng Y, Torres-Albero C, Herrera-Viedma E (2018) Analysing discussions in social networks using group decision making methods and sentiment analysis. Inf Sci 447:157–168
Morente-Molinera JA, Kou G, Samuylov K, Ureña R, Herrera-Viedma E (2019) Carrying out consensual Group Decision Making processes under social networks using sentiment analysis over comparative expressions. Knowl Based Syst 165:335–345
Li L, Scaglione A, Swami A, Zhao Q (2012) Phase transition in opinion diffusion in social networks. In: Acoustics, speech and signal processing (ICASSP), 2012 IEEE international conference on, pp 3073–3076
Oh O, Agrawal M, Rao HR (2013) Community intelligence and social media services: a rumor theoretic analysis of tweets during social crises. MIS Q 37(2):407–426
Nekovee M, Moreno Y, Bianconi G, Marsili M (2007) Theory of rumour spreading in complex social networks. Phys A 374(1):457–470
Li S, Zhu Y, Li D, Kim D, Huang H (2013) Rumor restriction in online social networks. In: 2013 IEEE 32nd International Performance Computing and Communications Conference (IPCCC), pp 1–10
Fan L, Wu W, Zhai X, Xing K, Lee W, Du D-Z (2014) Maximizing rumor containment in social networks with constrained time. Social Netw Anal Mining 4(1):214
Tong GA, Wu W, Guo L, Li D, Liu C, Liu B, Du DZ (2017) An efficient randomized algorithm for rumor blocking in online social networks. In: IEEE INFOCOM 2017-IEEE conference on computer communications. IEEE, pp 1–9
Tripathy RM, Bagchi A, Mehta S (2010) A study of rumor control strategies on social networks. In: Proceedings of the 19th ACM international conference on Information and knowledge management, pp 1817–1820
Huo L, Song N (2016) Dynamical interplay between the dissemination of scientific knowledge and rumor spreading in emergency. Phys A 461:73–84
Liu W, Yue K, Wu H, Li J, Liu D, Tang D (2016) Containment of competitive influence spread in social networks. Knowl-Based Syst 109:266–275
He X, Song G, Chen W, Jiang Q (2012) Influence blocking maximization in social networks under the competitive linear threshold model. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp 463–474
Li K, Zhang L, Huang H (2018) Social influence analysis: models, methods, and evaluation. Engineering 4(1):40–46
Kimura M, Saito K, Motoda H (2009) Blocking links to minimize contamination spread in a social network. ACM Trans Knowl Discov Data (TKDD) 3(2):9
Afassinou K (2014) Analysis of the impact of education rate on the rumor spreading mechanism. Phys A 414:43–52
Daley DJ, Kendall DG (1964) Epidemics and rumours. Nature 204(4963):1118
Zhao L, Wang Q, Cheng J, Chen Y, Wang J, Huang W (2011) Rumor spreading model with consideration of forgetting mechanism: a case of online blogging LiveJournal. Phys A Stat Mech Appl 390(13):2619–2625
Zhao L, Wang J, Chen Y, Wang Q, Cheng J, Cui H (2012) SIHR rumor spreading model in social networks. Phys A Stat Mech Appl 391(7):2444–2453
Chen G (2019) ILSCR rumor spreading model to discuss the control of rumor spreading in emergency. Phys A 522:88–97
Pan C, Yang L-X, Yang X, Wu Y, Tang YY (2018) An effective rumor-containing strategy. Phys A 500:80–91
Weisbuch G, Deffuant G, Amblard F, Nadal JP (2003) Interacting agents and continuous opinions dynamics. In: Heterogenous agents, interactions and economic performance. Springer, Berlin, Heidelberg, pp 225–242
Xia W, Cao M, Johansson KH (2015) Structural balance and opinion separation in trust–mistrust social networks. IEEE Trans Control Netw Syst 3(1):46–56
Cremer S, Armitage SA, Schmid-Hempel P (2007) Social immunity. Curr Biol 17(16):R693–R702
Bailey L (1968) Honey bee pathology. Annu Rev Entomol 13(1):191–212
Traniello JF, Rosengaus RB, Savoie K (2002) The development of immunity in a social insect: evidence for the group facilitation of disease resistance. Proc Natl Acad Sci 99(10):6838–6842
SNAP (2019) Standford datasets. http://snap.stanford.edu/
Zachary WW (1977) An information flow model for conflict and fission in small groups. J Anthropol Res 33:452–473
Hagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using networkx. In: Proceedings of the 7th Python in science conference. Varoquaux and Travis Vaught and Jarrod Millman, pp 11–15
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Srinivasan, S., L D, D. A social immunity based approach to suppress rumors in online social networks. Int. J. Mach. Learn. & Cyber. 12, 1281–1296 (2021). https://doi.org/10.1007/s13042-020-01233-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-020-01233-0