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Query-oriented topical influential users detection for top-k trending topics in twitter

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Abstract

Online Social Networks (OSNs) have become inevitable for any new methodology both for viral promoting applications and instructing the creation of inciting information and data. As a result, finding influential users in OSNs is one of the most studied research problems. Existing research works paid less attention to the temporal factors associated with the activities performed by the social users. Our motivation is to find influential users who show their most powerful interests towards a given query on various subjects (topics) at different time intervals by featuring more on users’ most recent activities as well as their associations with different users. To address this problem, we propose a temporal activity-biased weight model that gives higher weight to users’ recent activities and develops an algorithm to list the most effective influential users. In addition, our proposed model also considers the impacts of topical similarities both from direct and indirect neighbors of the users. Experimental results on two real datasets demonstrate that our proposed framework yields better outcomes than the baseline method.

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Code Availability

The code implemented in this paper is described in Sections 3.2.3 and 3.2.3. Detailed code can be found in https://github.com/ron352/Trending-TIUD.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jahangirnagar University, Savar, Dhaka, Bangladesh

    Sarmistha Sarna Gomasta, Aditi Dhali & Md Musfique Anwar

  2. Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh

    Iqbal H. Sarker

Authors
  1. Sarmistha Sarna Gomasta
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  2. Aditi Dhali
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  3. Md Musfique Anwar
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  4. Iqbal H. Sarker
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Correspondence to Sarmistha Sarna Gomasta.

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Availability of data and material

The data that support the findings of this study are openly available in [27] and [40].

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This article belongs to the Topical Collection: Big Data-Driven Large-Scale Group Decision Making Under Uncertainty

Appendix

Appendix

In our proposed framework, we have used multiple mathematical parameters to calculate equations. All the parameters are listed in Table 11.

Table 11 Parameters Used in Proposed Method
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Gomasta, S.S., Dhali, A., Anwar, M.M. et al. Query-oriented topical influential users detection for top-k trending topics in twitter. Appl Intell 52, 13415–13434 (2022). https://doi.org/10.1007/s10489-022-03582-5

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