Abstract
This paper proposes a method to generate an optimal community on Twitter by grouping users using a solution to the knapsack problem. Most past studies have proposed methods to recommend one user or some candidates, rather than recommending multiple users as candidates. It is quite difficult to recommend candidates as a single group that considers the balance of user characteristics, because grouping in terms of relationships among users is a combinatorial problem, especially on Twitter, as a huge number of users must be handled, so combinatorial explosion occurs easily. Although the combination optimization problem is difficult, with the knapsack problem, it is possible to obtain a solution of good quality within a practical calculation time. In this paper, we calculate the combination of users whose total amount of knowledge is maximized by using “amount of knowledge acquired” as a community evaluation item. In addition, we conduct a subject experiment to evaluate the information obtained from the generated community and the performance of the proposed method.
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Acknowledgements
This research is supported in part by JSPS KAKENHI 19H04154, 18K11383, and 19K12090. We would like to thank Dr. Yuji Nakagawa for discussing the optimization method using non-linear knapsack problem. We would also like to thank the participants in the experiments.
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Okada, Y., Ito, N., Yonezawa, T. (2021). Optimal Community-Generation Methods for Acquiring Extensive Knowledge on Twitter. In: Meiselwitz, G. (eds) Social Computing and Social Media: Experience Design and Social Network Analysis . HCII 2021. Lecture Notes in Computer Science(), vol 12774. Springer, Cham. https://doi.org/10.1007/978-3-030-77626-8_7
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DOI: https://doi.org/10.1007/978-3-030-77626-8_7
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