Scientific Information Research
Keywords
government WeChat; index system; PSO; BP Neural Network
Abstract
[Purpose/significance]As a new government public service platform based on social media,the government WeChat plays an increasingly important role in the efforts of promoting the processes of govern-ment information-opening and building service-oriented government and digital government.This article starts from analyzing the evaluation standards of the impact of government WeChat to improve the service quality and the communication effect.[Method/process]In order to improve the level of credibility and intelligence of the evaluation, this article proposes a PSO-BP Neural Network model to evaluate the impact of government WeChat.Firstly,the model builds an index system with four first level indicators:information services,usage behavior,platform character,and influencing power based on mass communication theory;Then the model adopts AHP to determine the weight vectors of index and uses grey method to get clustering coefficients and results;Finally,an experiment is conducted using PSO-BP Neural Network model to evaluate 50 government WeChat platforms.[Result/conclusion]The experiment validates that the model is effective and intelligent.Adopting the model will improve the performance of government WeChat platforms.
First Page
60
Recommended Citation
HE, Xiaoyu
(2022)
"Research on an Evaluation Model of the Impact of Government WeChat Based on PSO-BP Neural Network,"
Scientific Information Research: Vol. 4:
Iss.
3, Article 7.
Available at:
https://eng.kjqbyj.com/journal/vol4/iss3/7
Reference
[1] 李宗富,张向先.政务微信信息生态链的构成要素、形成机理、结构与类型[J].情报理论与实践,2016,39(08):32-39.
[2] 李增辉.网民在哪,政务新媒体就在哪[N].人民日报,2015-02-12(020).
[3] BONSÓN E,ROYO S,RATKAI M.Citizens' Engagement on Local Governments' Facebook Sites.An Empirical Analysis:The Impact of Different Media and Content Types in Western Europe[J].Government Information Quarterly,2015,32(01):52-62.
[4] DRISS O,MELLOULI S,TRABELSI Z.From citizens to government policy-makers:Social media data analysis[J].Government Information Quarterly,2019,36(03):560-570.
[5] GRUZD A,LANNIGAN J,QUIGLEY K.Eamining government cross-platform engagement in social media:Instagram vs Twitter and the big lift project[J].Government Information Quarterly,2018,35(04):579-587.
[6] MEDAGLIA R,ZHU D.Public deliberation on government-managed social media:A study on Weibo users in China[J].Government Information Quarterly,2017,34(03):533-544.
[7] ANTONIADIS K,ZAFIROPOULOS K,VRANA V.A Method for Assessing the Performance of e-Government Twitter Accounts[J].Future Internet,2016,8(02):1-18.
[8] PÉREZ-MOROTE R,PONTONES-ROSA C,NÚÑEZ-CHICHARRO M.The effects of e-government evaluation,trust and the digital divide in the levels of e-government use in European countries[J].Technological Forecasting and Social Change,2020,154:119973.
[9] 贾哲敏,顾晓宇.政务微信传播的框架建构与影响[J].北京航空航天大学学报(社会科学版),2018,31(01):32-38.
[10] 张放,杨颖,吴林蔚.政务微信“软文”化传播效果的实验研究[J].新闻界,2020(01):59-73.
[11] 谢丽娜.政务社交媒体中用户信息获取影响因素研究述评[J].图书情报工作,2015,59(19):113-121.
[12] 宋之杰,巫翠玉,石蕊.政务微信公众号用户采纳研究[J].电子政务,2015(03):18-25.
[13] 王萍,张韫麒,朱力香,等.政务微信公众号知识服务质量影响因素研究[J].图书情报工作,2018,62(23):43-50.
[14] 任昱广,陈旸.政务微信公众平台信息服务质量提升策略研究[J].企业技术开发,2018,37(11):34-36.
[15] 马莱茨克.大众传播心理学[M].汉堡:汉斯-布雷多学院出版社,1963.
[16] 管新建,张文鸽,吴泽宁.水利工程评价中层次分析法标度分析[J].河南水利,2002(04):20-21.
[17] 庞博,李玉霞,童玲.基于灰色聚类法和模糊综合法的水质评价[J].环境科学与技术,2011,34(11):185-188.
[18] 王玉冬,朱红.高新技术企业资金控制效果评价:基于PSO-BP神经网络[J].财会通讯,2017(08):37-39.