Abstract
Since the emergence of the Online Social Networks, people have been increasingly involved in the online mode of communications. They have been more or less influenced by these online communications and the idea of having or making an influence has been a key specialization for Online Social Network users. To control or spread something that is good or bad, we always have to point out the key players on Online Social Networks, so that we can take out remedial steps in order to deal with good or bad diffusions around the Globe in the modern era of Online Social Networks. These key players are called as influential users. To model the extraction of these influential users and to model information influence in the Online Social Networks the researchers have modeled as information diffusion model. In this paper, we are going to explore some of the models and the approaches that have an advantage to tackle the problems relating to the information diffusion in Online Social Networks.
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References
Silva, A., Guimaraes, S., Meira Jr., W., Zaki, M.: Profile rank: finding relevant content and influential users based on information diffusion. In: ACM. 978-1-4503-2330-7-7/06/13
Wang, F., Wang, H., Xu, K.: Dynamic mathematical modelling of information diffusion in online social networks
Taxidou, I., Fischer, P.M.: Online analysis of ınformation diffusion in Twitter. In: ACM. 978-1-4503-2745-9/14/04
Cole, W.D.: An information diffusion approach for detecting emotional contagion in online social networks. A thesis for MS degree
Zhang, H., Mishra, S., Thai, M.T.: Recent advances in information diffusion and influence maximization of complex social networks. Opportunistic Mobile Soc. Netw. 37, 37 (2014)
Mottin, D., Lazaridou, K.: Graph mining: social network analysis and information diffusion. Hasso Plattner Institute
Goldenberg, J., Libai, B., Muller, E.: Talk of the network: a complex systems look at the underlying process of word-of-mouth. Mark. Lett. 3, 211–223 (2001)
Goldenberg, J., Libai, B., Muller, E.: Using complex systems analysis to advance marketing theory development. Acad. Mark. Sci. Rev. 9, 1–18 (2001)
Granovetter, M.S.: Threshold models of collective behaviour. Am. J. Sociol. 83(6), 1420–1443 (1978)
Singh, A.K., Gambhir, S.: A comprehensive review of overlapping community detection algorithms for social networks. Int. J. Eng. Res. Appl. (IJERA) ISSN: 2248-9622
Li, M., Wang, X., Gao, K., Zhang, S.: A survey on ınformation diffusion in online social networks: models and methods. Information 8, 118 (2017)
Choudhury, D., Paul, A.: Community detection in social networks: an overview. IJRET: Int. J. Res. Eng. Technol. eISSN: 2319-1163
Tang, J.: Social influence and information diffusion. Department of Computer Science and Technology Tsinghua University
Weng, L.: Information diffusion in online social networks, April 2014
Wani, M., Ahmad, M.: Survey of information diffusion over interaction networks of Twitter. Int. J. Comput. Appl. 3, 310–313 (2014)
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Shiekh, M.A., Sharma, K., Ganai, A.H. (2020). Information Diffusion: Survey to Models and Approaches, a Way to Capture Online Social Networks. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_3
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DOI: https://doi.org/10.1007/978-3-030-34080-3_3
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