A Study on the Presentation and Communication Influence of Shaanxi Regional Culture in Public Service Advertisements from the Perspective of Social Network Analysis

Ying Ma
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Abstract

Abstract This paper describes the dynamic propagation process of the influence of public service advertisements (PSAs) using the information dissemination model and proposes a click probability function by modeling the click rate of advertisements, which is solved by a heuristic method. The EPR advertisement recommendation algorithm is selected to simulate the push process of the advertisement, and the experimental performance is analyzed. Analyze the presentation of Shaanxi regional culture and the network mobilization mechanism model of enterprise public service advertisement, predict and analyze the click rate of public service advertisement broadcasting, and emphasize the trust analysis of the audience. Variables such as the number of effective viewing of advertisements, the number of link clicks, the number of likes, and the amount of placement are selected to carry out descriptive analysis and correlation analysis of the effect of PSAs on Shaanxi regional culture. From the correlation coefficient matrix, it can be seen that the correlation coefficients between the explanatory variables take smaller values and are at the level below 0.11, and the number of likes has significant positive correlation coefficients with the indicators of advertisement effect at each stage. Thus it can be verified that the social attributes of advertisements play a positive role in the effect of advertisements.
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社会网络分析视角下陕西地域文化在公益广告中的呈现与传播影响研究
摘要本文利用信息传播模型描述了公益广告影响力的动态传播过程,并通过对广告点击率建模,提出了一个点击概率函数,并用启发式方法求解。选择EPR广告推荐算法模拟广告的推送过程,并对实验性能进行了分析。分析陕西地域文化的呈现和企业公益广告的网络动员机制模型,预测分析公益广告播出点击率,强调受众的信任分析。选取广告有效浏览量、链接点击量、点赞量、投放量等变量,对公益广告对陕西地域文化的影响进行描述性分析和相关性分析。从相关系数矩阵可以看出,各解释变量之间的相关系数均较小,均在0.11以下的水平上,点赞数与各阶段广告效果指标之间存在显著的正相关系数。由此可以验证广告的社会属性对广告的效果起到了积极的作用。
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来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
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