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Rumor Identification with Maximum Entropy in MicroNet

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  • Suisheng Yu
  • Mingcai Li
  • Fengming Liu

Abstract

The widely used applications of Microblog, WeChat, and other social networking platforms (that we call MicroNet) shorten the period of information dissemination and expand the range of information dissemination, which allows rumors to cause greater harm and have more influence. A hot topic in the information dissemination field is how to identify and block rumors. Based on the maximum entropy model, this paper constructs the recognition mechanism of rumor information in the micronetwork environment. First, based on the information entropy theory, we obtained the characteristics of rumor information using the maximum entropy model. Next, we optimized the original classifier training set and the feature function to divide the information into rumors and nonrumors. Finally, the experimental simulation results show that the rumor identification results using this method are better than the original classifier and other related classification methods.

Suggested Citation

  • Suisheng Yu & Mingcai Li & Fengming Liu, 2017. "Rumor Identification with Maximum Entropy in MicroNet," Complexity, Hindawi, vol. 2017, pages 1-8, September.
    • Handle: RePEc:hin:complx:1703870
    DOI: 10.1155/2017/1703870
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    References listed on IDEAS

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    1. Zhao Zhang & Wen Xu & Weili Wu & Ding-Zhu Du, 2017. "A novel approach for detecting multiple rumor sources in networks with partial observations," Journal of Combinatorial Optimization, Springer, vol. 33(1), pages 132-146, January.
    2. Devavrat Shah & Tauhid Zaman, 2016. "Finding Rumor Sources on Random Trees," Operations Research, INFORMS, vol. 64(3), pages 736-755, June.
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    Cited by:

    1. Hai, Audrey Hang & Franklin, Cynthia & Cole, Allan Hugh & Panisch, Lisa S. & Yan, Yueqi & Jones, Kristian, 2021. "Impact of MindUP on elementary school students’ classroom behaviors: A single-case design pilot study," Children and Youth Services Review, Elsevier, vol. 125(C).

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