Library and Information Service

Library and Information Service >

2022 , Vol. 66 >Issue 16: 24 - 35

DOI: https://doi.org/10.13266/j.issn.0252-3116.2022.16.003

SPECIAL TOPIC：Network Public Opinion Dissemination and Emergency Management of Major Emergencies Driven by Digital Intelligence

A Model and Empirical Study of Opinion Leader Node Influence Index in Social Networks——Taking the Natural Disaster "7·20" Henan Rainstorm as an Example

  • Wang Xiwei ,
  • Bi Yingying ,
  • Li Yueqi
Expand
  • 1. School of Business and Management, Jilin University, Changchun 130012;
    2. Research Center for Big Data Management, Jilin University, Changchun 130012;
    3. Research Center for Cyberspace Governance, Jilin University, Changchun 130012

Received date: 2022-02-25

  Revised date: 2022-06-21

  Online published: 2022-08-19

Fold

Abstract

[Purpose/Significance] In social networks, opinion leader node influence plays an important role in the development of public opinion in social networks. Building a comprehensive scientific opinion leader node influence index and computational analysis model in social networks can better identify key opinion leaders and can better monitor and guide the direction of public opinion. [Method/Process] Based on the relevant theories of information theory and p-index, an OLEI index algorithm was constructed based on three dimensions of opinion leaders' recognition, emotional connection and network communication in social networks, and a model for calculating and analyzing the node influence index of opinion leaders in social networks was proposed. The OLEI index algorithm was validated and analyzed by combining the typical public opinion topics in the natural disaster "7·20" Henan rainstorm. [Result/Conclusion] The results of the empirical study show that the recognition degree in the OLEI index can reflect the degree of opinion influence and trust support of social network users on other users; the emotional connection degree can reflect that the opinion leader nodes have triggered greater user emotional fluctuations in the social network; the greater the network communication degree of the opinion leader, the more important the position it occupies in the social media network.

Key words： social networks; opinion leaders; node influence; index

Cite this article

Wang Xiwei , Bi Yingying , Li Yueqi . A Model and Empirical Study of Opinion Leader Node Influence Index in Social Networks——Taking the Natural Disaster "7·20" Henan Rainstorm as an Example[J]. Library and Information Service, 2022 , 66(16) : 24 -35 . DOI: 10.13266/j.issn.0252-3116.2022.16.003

References

[1] 2021年全国十大自然灾害[EB/OL].[2022-01-23]. https://www.mem.gov.cn/xw/yjglbgzdt/202201/t20220123_407199.shtml.
[2] MACEACHERN L, CRANLEY L, CURRAN J, et al. The role of motivation in the diffusion of innovations in Canada's long-term care sector: a qualitative study[J]. Implementation science communications, 2020, 1(1): 1-11.
[3] RIQUELME F, GONZÁLEZ-CANTERGIANI P. Measuring user influence on Twitter: a survey[J]. Information processing & management, 2016, 52(5): 949-975.
[4] KI C W C, CUEVAS L M, CHONG S M, et al. Influencer marketing: social media influencers as human brands attaching to followers and yielding positive marketing results by fulfilling needs[J]. Journal of retailing and consumer services, 2020, 55(C): 102133.
[5] TOBON S, GARCÍA-MADARIAGA J. The influence of opinion leaders' ewom on online consumer decisions: a study on social influence[J]. Journal of theoretical and applied electronic commerce research, 2021, 16(4): 748-767.
[6] CUOMO M T, TORTORA D, FOROUDI P, et al. Digital transformation and tourist experience co-design: big social data for planning cultural tourism[J]. Technological forecasting and social change, 2021, 162(C): 120345.
[7] DUBOIS E, MINAEIAN S, PAQUET-LABELLE A, et al. Who to trust on social media: how opinion leaders and seekers avoid disinformation and echo chambers[J].Social media+ society,2020,6(2): 1-13.
[8] 王晰巍,贾若男,韦雅楠,等.社交网络舆情事件主题图谱构建及可视化研究——以校园突发事件话题为例[J].情报理论与实践,2020,43(3):17-23.
[9] 刘玉文,王凯,刘月华.基于影响力遗传的意见领袖在线识别[J].情报理论与实践,2019,42(7):126-131,164.
[10] 陈一新,陈馨悦,吕妍,等.基于改进Hegselmann-Krause模型的微博舆论反转研究[J].情报理论与实践,2020,43(1):82-89.
[11] 安璐,胡俊阳,李纲.突发事件情境下社交媒体高影响力用户画像研究[J].情报资料工作,2020,41(6):6-16.
[12] LAZARSFELD P, BERELSON B, GAUDET H. The people's choice: how the voter makes up his mind in a presidential campaign[M]. Columbia: Columbia University Press, 1968.
[13] WEISSMAN A, NGUYEN T T, NGUYEN H T, et al. The role of the opinion leader research process in informing policy making for improved nutrition: experience and lessons learned in Southeast Asia[J]. Current developments in nutrition, 2020,4(6): nzaa093.
[14] RASHOTTE L, RITZER G, RYAN J M. Blackwell encyclopedia of sociology[J]. Choice reviews online, 2007, 44(11): 4434-4437.
[15] 刘佳程,马廷灿,岳名亮.HHa中心性算法:一种基于h指数和Ha指数的复杂网络节点排序算法[J].图书情报工作,2021,65(20):92-100.
[16] 李霄,曲阳,李辉,等.基于用户关系的在线问答平台用户重要性评估方法[J].计算机科学,2020,47(S2):430-436,448.
[17] 刘嘉琪,齐佳音,陈曼仪.基于社会网络分析的意见领袖与在线群体影响力关系研究[J].情报科学,2018,36(11):138-145.
[18] SHANNON C E. A mathematical theory of communication[J]. The Bell system technical journal, 1948, 27(3): 379-423.
[19] 钱晨,黄卫东.基于信息量的微博事件态势感知模型研究[J].情报科学,2019,37(2):46-51.
[20] HIRSCH J E.An index to quantify an individual＇s scientific research output[J].Proceedings of the National Academy of Sciences of the USA,2005,102(46): 16569-16572.
[21] PRATHAP G.The 100 most prolific economists using the p-index [J].Scientometrics,2010,84(1): 167-172.
[22] 郭博,许昊迪,雷水旺.知乎平台用户影响力分析与关键意见领袖挖掘[J].图书情报工作,2018,62(20):122-132.
[23] BRIN S. The PageRank citation ranking: bringing order to the web[J]. Proceedings of ASIS, 1998, 98: 161-172.
[24] 钟磊,宋香荣,孙瑞娜.基于LeaderRank的意见领袖发现模型及其应用[J].情报杂志,2021,40(4):194-199.
[25] 徐雅斌,孙秋天.特定舆情的意见领袖挖掘和关键传播路径预测[J].数据分析与知识发现,2021,5(2):32-42.
[26] 陈芬,高小欢,彭玥,等.融合文本倾向性分析的微博意见领袖识别[J].数据分析与知识发现,2019,3(11):120-128.
[27] 王晰巍,贾玺智,刘婷艳,等.区块链环境下社交网络用户意见领袖识别与影响力研究[J].情报理论与实践,2021,44(5):84-91.
[28] 姜婷婷,贺虹虹,张正楠.搜索任务复杂度对用户情感的影响研究[J].图书情报知识,2016(4):74-82.
[29] 吴江,赵颖慧,高嘉慧.医疗舆情事件的微博意见领袖识别与分析研究[J].数据分析与知识发现,2019,3(4):53-62.
[30] 王林,朱文静,潘陈益,等.基于p指数的微博传播力评价方法及效果探究——以我国34省、直辖市旅游政务官方微博为例[J].情报科学,2018,36(4):38-44.
[31] 王晰巍,李玥琪,刘婷艳,等.新冠肺炎疫情微博用户情感与主题挖掘的协同模型研究[J].情报学报,2021,40(3):223-233.
[32] AIELLO L M, BARRAT A, SCHIFANELLA R, et al. Friendship prediction and homophily in social media[J]. ACM Transactions on the Web, 2012, 6(2), 1-33.
[33] 安璐,陈思菁.基于H指数的校园微博影响力评价研究[J].信息资源管理学报,2017,7(1):79-88.
[34] 陈云伟,张瑞红.用于情报挖掘的典型网络社团划分算法比较研究[J].数据分析与知识发现,2018,2(10):84-94.
[35] 新浪微博数据中心. 2018 微博用户发展报告[EB/OL].[2020-02-27]. https://data.weibo.com/report/reportDetail?id=433.
[36] 河南遭遇特大暴雨 [EB/OL].[2021-01-24].https://ef.zhiweidata.com/event/8265e34c64b90e6510054700/trend.
[37] 廖琳,黄涛.信源、信息内容、情绪特征对微博转发的影响探究[J].现代情报,2020,40(9):42-52.
[38] YIGITCANLAR T, REGONA M, KANKANAMGE N, et al. Detecting natural hazard-related disaster impacts with social media analytics: the case of Australian states and territories[J]. Sustainability, 2022, 14(2): 810.
[39] 袁红,李佳.行动者网络理论视域下社会热点事件网络舆情治理策略研究[J].情报资料工作,2021,42(6):31-44.
[40] 王佳敏,吴鹏,沈思.突发事件中意见领袖对网民的情感影响建模研究[J].情报杂志,2018,37(9):120-126.
[41] YOUNIS S, AHSAN A. Know your stars before they fall apart: a social network analysis of telecom industry to foster employee retention using data mining technique[J].IEEE access, 2021,9: 16467-16487.
[42] VERA-BURGOS C M, PADGETT D R G. Using Twitter for crisis communications in a natural disaster: Hurricane Harvey[J]. Heliyon, 2020, 6(9): e04804.
Options
Outlines

/

〈 〉

    Copyrights © LIS Press Co., Ltd. Reproduction is prohibited without permission.