Open Access

ARTICLE

Improved Short-video User Impact Assessment Method Based on PageRank Algorithm

Lei Hong^1,*, Jie Yin¹, Ling-Ling Xia¹, Chao-Fan Gong¹, Qi Huang²

1 Jiangsu Police Institute, Nanjing, 210000, China
2 Purdue University, West Lafayette, IN, 47907, USA

* Corresponding Author: Lei Hong. Email:

Intelligent Automation & Soft Computing 2021, 29(2), 437-449. https://doi.org/10.32604/iasc.2021.016259

Received 28 December 2020; Accepted 02 March 2021; Issue published 16 June 2021

Abstract

The short-video platform is a social network where users’ content accelerates the speed of information dissemination. Hence, it is necessary to identify important users to effectively obtain information. Four algorithms (Followers Rank, Average Forwarding, K Coverage, and Expert Survey and Evaluation) have been proposed to calculate users’ influence and determine their importance. These methods simply take the number of a user’s fans or posts as the standard of influence evaluation, ignoring factors such as the paid posters, which makes such evaluations inaccurate. To solve these problems, we propose the short-video user influence rank (SVUIR) algorithm, which combines direct and indirect influence to comprehensively measure the influence of short-video users, using reference factors such as the number of fans, likes, number of users’ works, users’ work quality, focus on behavior, comments, and forwarding behavior. An experiment verifies the algorithm on Douyin (i.e., TikTok), which is a typical short-video platform, and confirms that SVUIR is more comprehensive and objective than the above four algorithms.

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