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Dynamic evaluation method on dissemination capability of microblog users based on topic segmentation

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  • Li, Kai
  • Zhu, Hengmin
  • Zhang, Yihan
  • Wei, Jing

Abstract

Different from the evaluation of the influence of nodes in social networks, user dissemination capability refers to the ability of users facilitating information spreading in the network. It is a hot issue in the field of information diffusion to evaluate users’ dissemination capability. Previous studies have rarely considered the impact of neighbor nodes or time decay on user dissemination capability. By constructing the user interaction network of Sina Microblog, we achieved a dynamic evaluation of user dissemination capability. Firstly, based on the thought of topic segmentation, the relationship strength between users under different topics was calculated respectively according to their historical interaction records. Secondly, we analyzed the effect of the subsequent forwarding behaviors of neighbor nodes and time decay on user dissemination capability, and proposed a dynamic evaluation method of user dissemination capability. Then, based on the modified PageRank algorithm, the ranking of user dissemination capability under different topics was calculated. Finally, experiments were conducted to verify the dynamic evaluation method on user dissemination capability. The results show that it can identify users with high dissemination capability under different topics, which might be helpful to intervene the information spreading.

Suggested Citation

  • Li, Kai & Zhu, Hengmin & Zhang, Yihan & Wei, Jing, 2022. "Dynamic evaluation method on dissemination capability of microblog users based on topic segmentation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    • Handle: RePEc:eee:phsmap:v:608:y:2022:i:p1:s0378437122008226
    DOI: 10.1016/j.physa.2022.128264
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    References listed on IDEAS

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    1. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    3. Zhu, Hengmin & Yin, Xicheng & Ma, Jing & Hu, Wei, 2016. "Identifying the main paths of information diffusion in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 320-328.
    4. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
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    Cited by:

    1. Amirhosein Bodaghi & Jonathan J. H. Zhu, 2024. "A big data analysis of the adoption of quoting encouragement policy on Twitter during the 2020 U.S. presidential election," Journal of Computational Social Science, Springer, vol. 7(2), pages 1861-1893, October.

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