Research on the Evaluation of Program Influence Based on PageRank Algorithm

2021 International Conference on Culture-oriented Science & Technology (ICCST) Pub Date : 2021-11-01 DOI:10.1109/ICCST53801.2021.00113

Feng Qing, Yan Wang, Yantong Zhang

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Abstract

The paper introduces the complex network theory into the program influence evaluation system. We collect relevant data of the variety shows on Tencent Video Platform from 2016-2021 and establish the program comprehensive influence algorithm program. The comprehensive influence consists of two direct and indirect parts, the direct influence is calculated from the total view counts, the comments in the latest issue, and the fans of the official program account. The indirect influence is calculated by PageRank, LeaderRank and TimedPageRank through the program page. The results obtained by the algorithm shows that the output of highly influential variety shows has increased in recent years. Even though COVID-19 in 2020 reduced the total production of variety shows, it still produces a relatively high proportion of influential programs. High influence programs are mainly “Develop” and “Game” programs, “Cultural” programs rank low. Inspire our need to strengthen the content production and value concept guidance of the variety show market, and deepen the content innovation and reform of the “Cultural” programs themselves.

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基于PageRank算法的节目影响力评价研究

将复杂网络理论引入到节目影响评价体系中。我们收集2016-2021年腾讯视频平台综艺节目相关数据，建立节目综合影响力算法方案。综合影响力由直接和间接两部分组成，直接影响力由总观看数、最新一期评论数、公众号粉丝数计算。间接影响由PageRank, LeaderRank和timmedpagerank通过程序页面计算。算法结果表明，近年来影响力较大的综艺节目产量有所增加。尽管2020年的新冠疫情导致综艺节目总量减少，但有影响力的节目仍占比较高的比例。影响力较大的节目主要是“发展”和“游戏”类节目，“文化”类节目排名较低。启示我们需要加强综艺节目市场的内容生产和价值观念引导，深化“文化”类节目本身的内容创新和改革。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2021 International Conference on Culture-oriented Science & Technology (ICCST)

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