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Modeling and minimizing information distortion in information diffusion through a social network

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Abstract

It is very common in real life that information distorts during the process of transmission in a social network, which may lead to people’s incorrect comprehension of the information and further poor decision making. In this paper, we study how to model and minimize the distortion of information when it diffuses through a social network. We propose the concept of information authenticity to measure distortion as well as a mathematical model to characterize how information distorts during its diffusion through a social network, and study the optimization problem of maximizing the information authenticity of a social network. In order to solve the problem, we employ a framework of greedy algorithms that was proposed by Ni et al. (Inf Sci 180(13):2514–2527, 2010), which can trade off between optimality and complexity. Finally, we perform experiments to show the greedy algorithms can effectively solve the problem we propose.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (Nos. 71471038, 71101027, 71001080, 71171191) and Beijing Higher Education Young Elite Teacher Project (No. YETP0909).

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Authors and Affiliations

  1. School of Information Technology and Management, University of International Business and Economics, Beijing, 100029, China

    Yaodong Ni & Liu Ning

  2. School of Economics and Management, Tongji University, Shanghai, 200092, China

    Hua Ke

  3. School of Business, Renmin University of China, Beijing, 100872, China

    Xiaoyu Ji

Authors
  1. Yaodong Ni
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  2. Liu Ning
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  3. Hua Ke
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  4. Xiaoyu Ji
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Correspondence to Yaodong Ni.

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Yaodong Ni, Liu Ning, Hua Ke, Xiaoyu Ji declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by A. Di Nola.

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Ni, Y., Ning, L., Ke, H. et al. Modeling and minimizing information distortion in information diffusion through a social network. Soft Comput 21, 5281–5293 (2017). https://doi.org/10.1007/s00500-016-2277-9

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