Construction and empirical analysis of government micro-blog influence evaluation system

doi:10.14088/j.cnki.issn0439-8114.2023.06.034

Abstract

Abstract: Based on the two roles of the information communication media and the social network platform of the government micro-blog,an evaluation system covering 14 measurement indicators was constructed from four dimensions of breadth, creativity, initiative and participation. Through the empirical analysis of 15 types data of Sina government micro-blog, the operability of the model was verified. The results showed that the “four-dimensional” evaluation model was operable. Except for the breadth, the trend of changes in the other three dimensions was generally consistent with the trend of comprehensive influence changes.

Key words: government micro-blog, “four-dimensional” system, influence evaluation

CLC Number:

G206

LIANG Yong-kang, HE Xiu-ping, SONG Kuo, ZHANG Wei-feng. Construction and empirical analysis of government micro-blog influence evaluation system[J]. HUBEI AGRICULTURAL SCIENCES, 2023, 62(6): 187-191.

References

[1] CNNIC.第44次中国互联网络发展状况统计报告[R].北京:中国互联网络信息中心,2019.
[2] PAGE L,BRIN S,MOTWANI R,et al.The PageRank citation ranking: Bringing order to the web[A]. Proceedings of the 7th international conference companion on world wide[C].Brisbane, Australia,1998.161-172.
[3] CHEN X,WANG P F,QIN Z,et al.HLBPR:A Hybrid Local Bayesian Personal Ranking method[A].Proceedings of the 25th international conference companion on world wide web[C]. Montreal, Canada, 2016.21-22.
[4] 孙红,左腾. 基于PageRank的微博用户影响力算法研究[J].计算机应用研究,2018,35(4) :1028-1032.
[5] 黄贤英,阳安志,刘小洋,等.一种改进的微博用户影响力评估算法[J].计算机工程,2019, 45(12): 294-299.
[6] 罗芳,徐阳,蒲秋梅,等.基于PageRank的多维度微博用户影响力度量[J].计算机应用研究,2020,37(5):1354-1358,1367.
[7] CHA M, HADDADI H, BENEVENUTO F, et al.Measuring user influence in Twitter: The million follower fallacy[A]. Proceedings of the 4th international AAAI conference on weblogs and social media[C]. Washington, DC, USA,2010.10-17.
[8] ANTONIADIS K, ZAFIROPOULOS K,VRANA V.A method for assessing the performance of e-government Twitter accounts[J].Future internet, 2016, 8(2):12.
[9] 赵阿敏,曹桂全.政务微博影响力评价与比较实证研究——基于因子分析和聚类分析[J].情报杂志,2014,33(3):107-112.
[10] 杨长春,张睿.基于H指数的政务微博影响力研究[J].现代情报,2018,38(3):110-115,123.
[11] 王林,潘陈益,朱文静.基于h指数、g指数和p指数的微博影响力评价比较研究[J].现代情报,2018,38(6):11-18,61.
[12] 陈明亮,邱婷婷,谢莹.微博主影响力评价指标体系的科学构建[J].浙江大学学报(人文社会科学版),2014,44(2):53-63.
[13] 柯赟. 新浪微博信息传播的影响因素分析与效果预测[J].现代情报,2016,36(3):22-26.
[14] 冯小东,马捷,蒋国银.社会信任、理性行为与政务微博传播:基于文本挖掘的实证研究[J].情报学报,2019,38(9):954-965.
[15] 刘晓娟,王昊贤,肖雪,等.基于微博特征的政务微博影响因素研究[J].情报杂志,2013,32(12): 35-41.
[16] 张绍武,尹杰,林鸿飞,等.基于用户分析的微博用户影响力度量模型[J].中文信息学报,2015,29(4):59-66.
[17] 赵晓罡,王瑶,常亮.基于改进熵值法的景区微博平台影响力评价研究——以全国5A级景区为例[J].贵州社会科学,2019(2):153-160.
[18] 崔金栋,孙遥遥,郑鹊,等.我国政务微博社会网络特征对比分析实证研究[J].情报科学,2016,34(12):120-126,132.
[19] 李磊,汪萌,吴信东.基于社交网络的社交行为分析[J].电子与信息学报,2017,39(9): 2108-2118.
[20] 张继东,杨杨.基于交互行为和情感倾向的移动社交网络用户影响力度量模型研究[J].情报理论与实践,2019,42(1):112-117,93.
[21] 刘威,张明新,安德智.面向微博话题的用户影响力分析算法[J].计算机应用,2019, 39(1):213-219.
[22] 罗雨宁,胡广伟,卢明欣.政务微博影响力与粉丝特征关系研究[J].电子政务,2017(12): 82-89.