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Journal of Library and Information Science in Agriculture ›› 2024, Vol. 36 ›› Issue (1): 83-96.doi: 10.13998/j.cnki.issn1002-1248.23-0808

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Network Analysis of Emergency Information Dissemination Considering the Strength Relationship Between Nodes

LI Sijia1, ZHENG Deming1, LIU Bo2   

  1. 1. Research Center for Network Public Opinion Governance, China People's Police University, Langfang 065000;
    2. Dunhuang Entry Exit Border Inspection Station, Dunhuang 736200
  • Received:2023-10-27 Online:2024-01-05 Published:2024-04-18

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Abstract: [Purpose/Significance] With the rapid development of new media technology, social media platform has become the main carrier of information dissemination. Social network analysis (SNA) is used to study the information dissemination structure and mode of emergencies in microblog, which provides theoretical support for the government to effectively deal with emergencies and crises. [Methods/Process] Taking "Tangshan barbecue restaurant beating incident" as an example, Weibo data were collected to build an information dissemination network with strength relationship between nodes. Social network analysis has been used to analyze the user attributes, node attributes, network attributes and dissemination attributes of the information dissemination network, in order to explore the role of the strength relationship between nodes in emergency information transmission. [Results/Conclusions] 1) The factors of user gender, activity, and region affect their dissemination power. In particular, female users, users with high activity or influence and those in developed provinces have a stronger power of information transmission. 2) The core nodes that plays the role of "bridge" in the dissemination chain is particularly critical. Nodes with strong relationships generally occupy central positions in the information dissemination network and may mainly consist of opinion leaders and mainstream media with greater influence. The pathways of weak and authority relationships are mainly concentrated around a few core nodes, while the pathways of strong relationships are dispersed. 3) Emergency information transmission network has high efficiency and sparse characteristics. 4) The whole process of information transmission is still dominated by weak relationships. Authority relations play an important role in all stages of information transmission, while the role of strong relationships is mainly concentrated in the initial stage. The results of this paper help to deepen the understanding of the patterns and rules of emergency information dissemination, and provide some insights for more effective management and guidance of emergency information dissemination in a particular field such as agriculture. However, our research still has shortcomings, such as insufficient crawling of user attributes and insufficient research methods. In the future study, we will obtain more abundant characteristics of users involved in the dissemination such as age, occupation and education level through interviews or questionnaires, and introduce new methods such as machine learning and graph neural network to predict and analyze the transmission path and node relationship of emergency information, so as to cover these shortcomings, improve the comprehensiveness and effectiveness of the study and deeply explore the information dissemination rules of emergencies.

Key words: emergencies, information dissemination, social network analysis, weibo, strength relationship between nodes

CLC Number: 

  • TP391
[1] 格岩. 加强重大决策社会风险评估与治理[J]. 中国管理信息化, 2023, 26(17): 193-196.
GE Y.Strengthen the social risk assessment and governance of major decision-making[J]. China management informationization, 2023, 26(17): 193-196.
[2] 侯利君, 张峰. 微博突发事件负面舆情信息传播机理及应对策略[J]. 数学的实践与认识, 2018, 48(23): 167-174.
HOU L J, ZHANG F.Information dissemination mechanism and countermeasures of micro-blog negative public opinion[J]. Mathematics in practice and theory, 2018, 48(23): 167-174.
[3] 王辉. 微博舆论场的特点及引导策略[J]. 青年记者, 2023(19): 60-62.
WANG H.The characteristics and guiding strategies of microblog public opinion field[J]. Youth journalist, 2023(19): 60-62.
[4] 陈然. 媒介变迁视域下媒介用户传播权力演变及其影响研究[D]. 昆明: 云南师范大学, 2023.
CHEN R.Research on the evolution and influence of media users' communication power from the perspective of media change[D]. Kunming: Yunnan Normal University, 2023.
[5] 方星, 霍良安, 黄培清. 突发事件后的官方信息与不实信息传播的交互模型[J]. 系统管理学报, 2018, 27(4): 722-728.
FANG X, HUO L A, HUANG P Q.An interaction model for official information and unconfirmed information after emergencies[J]. Journal of systems & management, 2018, 27(4): 722-728.
[6] 李晚莲, 高光涵. 突发事件网络舆情研究进展与趋势分析[J]. 重庆邮电大学学报(社会科学版), 2019, 31(5): 60-68.
LI W L, GAO G H.An analysis of the research progress and trends of emergency network public opinion[J]. Journal of Chongqing university of posts and telecommunications(social science edition), 2019, 31(5): 60-68.
[7] 杨卓伟, 刘德林. 我国突发事件网络舆情研究进展[J]. 河南科学, 2023, 41(1): 108-116.
YANG Z W, LIU D L.Research progress of emergency network public opinion in China[J]. Henan science, 2023, 41(1): 108-116.
[8] 王林, 张梦溪, 吴江. 信息生态视角下新冠肺炎疫情的网络舆情传播与演化分析研究[J]. 情报科学, 2022, 40(1): 31-37, 50.
WANG L, ZHANG M X, WU J.Propagation and evolution of public opinion in the outbreak of COVID-19 from the perspective of information ecology[J]. Information science, 2022, 40(1): 31-37, 50.
[9] 陈迎欣, 苏泽伟, 周蕾. 灾害救助信息网络传播的关键因素及有效路径[J]. 情报杂志, 2022, 41(5): 106-111.
CHEN Y X, SU Z W, ZHOU L.Key factors and effective path of disaster relief information network dissemination[J]. Journal of intelligence, 2022, 41(5): 106-111.
[10] 王晰巍, 邢云菲, 王楠阿雪, 等. 新媒体环境下突发事件网络舆情信息传播及实证研究——以新浪微博“南海仲裁案”话题为例[J]. 情报理论与实践, 2017, 40(9): 1-7.
WANG X W, XING Y F, WANG N A X, et al. An empirical study on information dissemination of network public opinion on emergencies under the new media environment[J]. Information studies: Theory & application, 2017, 40(9): 1-7.
[11] 王治莹, 李勇建, 王伟康. 基于随机Petri网的突发事件信息演化模型[J]. 中国管理科学, 2020, 28(3): 113-121.
WANG Z Y, LI Y J, WANG W K.Evolving model of information towards emergencies based on stochastic petri net[J]. Chinese journal of management science, 2020, 28(3): 113-121.
[12] 邓建高, 张璇, 傅柱, 等. 基于系统动力学的突发事件网络舆情传播研究:以“江苏响水爆炸事故” 为例[J]. 数据分析与知识发现, 2020, 4(S1): 110-121.
DENG J G, ZHANG X, FU Z, et al.Research on network public opinion propagation of emergencies based on system dynamics: Taking "Xiangshui explosion accident in Jiangsu" as an example[J]. Data analysis and knowledge discovery, 2020, 4(S1): 110-121.
[13] 沈家豪, 汪传雷, 潘加玲, 等. 基于SNA的“双微”应急信息关键词拓扑网络比较——以河南“7·20”特大暴雨为例[J]. 情报探索, 2022(8): 69-78.
SHEN J H, WANG C L, PAN J L, et al.Comparison of keyword topology networks of microblog and WeChat emergency information based on SNA: Case study of “7·20” extraordinary rainstorm in Henan[J]. Information research, 2022(8): 69-78.
[14] 汪婧, 陈发培. 基于SNA的突发事件舆情传播网络的结构特征分析——以江苏响水 “3·21” 爆炸事故为例[J]. 电子科技大学学报(社会科学版), 2021, 23(1): 10-18.
WANG J, CHEN F P.Analysis of structural features of public opinion dissemination network for emergency events based on SNA - Take Jiangsu Xiangshui "3.21 explosive accident" as an example[J]. Journal of university of electronic science and technology of China (social sciences edition), 2021, 23(1): 10-18.
[15] 万钰珏, 李世银, 房子豪, 等. 基于SNA的突发事件网络舆情意见领袖传播影响力[J]. 西安科技大学学报, 2022, 42(2): 290-298.
WAN Y J, LI S Y, FANG Z H, et al.Dissemination influence of Internet public opinion leaders in emergencies based on SNA[J]. Journal of Xi'an university of science and technology, 2022, 42(2): 290-298.
[16] 王雪秋. 突发金融舆情事件信息传播规律与对策研究[J]. 情报科学, 2021, 39(4): 54-61.
WANG X Q.Research on the characteristics and laws of financial public opinion events[J]. Information science, 2021, 39(4): 54-61.
[17] 徐嘉硕, 祁凯. 突发公共卫生事件微博网络社群挖掘及特征研究——基于2022年上海疫情事件的分析[J]. 情报探索, 2023(11): 74-80.
XU J S, QI K. Research on microblog network community mining and characteristics of public health emergencies: Based on the analysis of the2022 Shanghai epidemic event[J]. Information research, 2023(11): 74-80.
[18] 向紫娟. 强弱关系对突发事件信息传播的影响[J]. 青年记者, 2020(35): 18-19.
XIANG Z J.The influence of strong and weak relationship on the information dissemination of emergencies[J]. Youth journalist, 2020(35): 18-19.
[19] 许小可, 胡海波, 张伦, 等. 社交网络上的计算传播学[M]. 北京: 高等教育出版社, 2015.
XU X K, HU H B, ZHANG L, et al.Computational communication on social networks[M]. Beijing: Higher Education Press, 2015.
[20] 李丹丹, 马静. 社会网络上的舆论传播及控制策略[J]. 系统管理学报, 2017, 26(5): 835-841.
LI D D, MA J.Rumor spreading and controlling strategies research in social networks[J]. Journal of systems & management, 2017, 26(5): 835-841.
[21] 孙玺菁, 司守奎. 复杂网络算法与应用[M]. 北京: 国防工业出版社, 2015.
SUN X J, SI S K.Complex network algorithms and applications[M]. Beijing: National Defense Industry Press, 2015.
[22] 李锋, 魏莹. 小世界网络下病毒式信息传播的仿真分析[J]. 系统仿真学报, 2019, 31(9): 1790-1801.
LI F, WEI Y.Simulation analysis of viral-style information diffusion in small world networks[J]. Journal of system simulation, 2019, 31(9): 1790-1801.
[23] 乔玉为, 钟智锦, 许小可, 等. 突发事件舆论中的情绪极性与传染模式:社会网络分析视角[J]. 吉首大学学报(社会科学版), 2021, 42(6): 131-142.
QIAO Y W, ZHONG Z J, XU X K, et al.Emotional polarity and contagion pattern in unexpected public opinion: From the perspective of network analysis[J]. Journal of Jishou university (social sciences), 2021, 42(6): 131-142.
[24] 夏一雪. 网络话题传播规律建模与预测问题研究[J]. 现代情报, 2019, 39(4): 3-12.
XIA Y X.Research on modeling and prediction of network topic dissemination law[J]. Journal of modern information, 2019, 39(4): 3-12.
[25] 兰月新, 夏一雪, 刘冰月, 等. 网络舆情传播阶段精细化建模与仿真研究[J]. 现代情报, 2018, 38(1): 76-86.
LAN Y X, XIA Y X, LIU B Y, et al.The research on propagation phase accurate model and simulation of network public opinion[J]. Journal of modern information, 2018, 38(1): 76-86.
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