A hot topic diffusion approach based on the independent cascade model and trending search lists in online social networks

Yuqi Chen; Xianyong Li; Weikai Zhou; Yajun Du; Yongquan Fan; Dong Huang; Xiaoliang Chen; Yuqi Chen; Xianyong Li; Weikai Zhou; Yajun Du; Yongquan Fan; Dong Huang; Xiaoliang Chen

doi:10.3934/mbe.2023499

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 11260-11280. doi: 10.3934/mbe.2023499

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Research article Special Issues

A hot topic diffusion approach based on the independent cascade model and trending search lists in online social networks

School of Computer and Software Engineering, Xihua University, Chengdu 610039, China

Academic Editor: Lu-Xing Yang

Received: 23 November 2022 Revised: 18 March 2023 Accepted: 03 April 2023 Published: 25 April 2023

In online social networks, users can quickly get hot topic information from trending search lists where publishers and participants may not have neighbor relationships. This paper aims to predict the diffusion trend of a hot topic in networks. For this purpose, this paper first proposes user diffusion willingness, doubt degree, topic contribution, topic popularity and the number of new users. Then, it proposes a hot topic diffusion approach based on the independent cascade (IC) model and trending search lists, named the ICTSL model. The experimental results on three hot topics show that the predictive results of the proposed ICTSL model are consistent with the actual topic data to a great extent. Compared with the IC, independent cascade with propagation background (ICPB), competitive complementary independent cascade diffusion (CCIC) and second-order IC models, the Mean Square Error of the proposed ICTSL model is decreased by approximately 0.78%–3.71% on three real topics.
- hot topic,
- trending search list,
- topic diffusion,
- independent cascade (IC) model,
- topic popularity,
- social networks
Citation: Yuqi Chen, Xianyong Li, Weikai Zhou, Yajun Du, Yongquan Fan, Dong Huang, Xiaoliang Chen. A hot topic diffusion approach based on the independent cascade model and trending search lists in online social networks[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 11260-11280. doi: 10.3934/mbe.2023499

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Abstract

In online social networks, users can quickly get hot topic information from trending search lists where publishers and participants may not have neighbor relationships. This paper aims to predict the diffusion trend of a hot topic in networks. For this purpose, this paper first proposes user diffusion willingness, doubt degree, topic contribution, topic popularity and the number of new users. Then, it proposes a hot topic diffusion approach based on the independent cascade (IC) model and trending search lists, named the ICTSL model. The experimental results on three hot topics show that the predictive results of the proposed ICTSL model are consistent with the actual topic data to a great extent. Compared with the IC, independent cascade with propagation background (ICPB), competitive complementary independent cascade diffusion (CCIC) and second-order IC models, the Mean Square Error of the proposed ICTSL model is decreased by approximately 0.78%–3.71% on three real topics.

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