[Purpose/Significance] Using the event evolution graph to analyze the UGC of network public opinions under major emergencies, it can better present the causal evolution process and evolution path between public opinion events, thereby providing certain guidance for public opinion channeling and management and control of major emergencies. [Method/Process] This study constructed the UGC event evolution graph model of network public opinion of major emergencies, generated rule templates and syntactic patterns, and extracted event pairs with clear causality and event pairs with ambiguous causality, combined with the Weibo public opinion topic under the heavy rain rescue event in Henan, a major sudden natural disaster, to realize the construction of event evolution graph. The public opinion events with high similarity were generalized by K-means clustering algorithm, and the abstract event evolution graph was constructed. [Result/Conclusion] The study results show that using the UGC event evolution graph model of network public opinions of major emergencies constructed in this paper, the causal event correlation of network public opinions can be analyzed, so as to reveal the key events in the development process of network public opinions for major emergencies. Evolution graph model can help to better analyze the evolution process of network public opinions in major emergencies, abstract event evolution graph can help to further analyze the evolution path of network public opinion UGC.
Wang Xiwei
,
Wang Xiaotian
,
Li Yueqi
. Research on the Construction of the Event Evolution Graph of UGC of Network Public Opinions For Major Emergencies——Taking the Natural Disaster 7·20 Torrential Rain in Henan as an Example[J]. Library and Information Service, 2022
, 66(16)
: 13
-23
.
DOI: 10.13266/j.issn.0252-3116.2022.16.002
[1] 马海群,邹纯龙,王今.总体国家安全观下国家情报评估制度的基本逻辑研究[J/OL].图书情报工作, 2022, 66(2):1-9[2022-03-18].DOI:10.13266/j.issn.0252-3116.2022.02.001.
[2] 王海鹏. 重大自然灾害网络舆情政府应对研究[D].济南:山东大学,2021.
[3] SAOKOSAL O, DONG W H. An empirical study of the determinants of the intention to participate in user-created contents (UCC)services[J]. Expert systems with applications, 2011, 38 (12) : 15110-15121.
[4] JIN Y, PANG A, CAMERON G T. Toward a publics-driven, emotion-based conceptualization in crisis communication: un-earthing dominant emotions in multi-staged testing of the integrated crisis mapping (ICM) model [J]. Journal of public relations research, 2012, 24 (3): 266-298.
[5] QU Q,WANG L,ZHANG L, et al. Modeling and simulation for the impact of EGC strategies on the negative UGC diffusion[EB/OL].[2022-05-07]. https://www.sciencedirect.com/science/article/abs/pii/S0736585319307695.
[6] REYES-MENENDEZ A, SAURA J R, THOMAS S B. Exploring key indicators of social identity in the #MeToo era: using discourse analysis in UGC[J]. International journal of information management, 2020, 54: 102129.
[7] 王微,王晰巍,娄正卿,等.信息生态视角下移动短视频UGC网络舆情传播行为影响因素研究[J].情报理论与实践,2020,43(3):24-30.
[8] 娄正卿. 移动短视频UGC网络舆情主题图谱构建与传播特征研究[D].长春:吉林大学,2020.
[9] 张雷,谭慧雯,张璇,等.基于LDA模型的高校师德舆情演化及路径传导研究[J].情报科学,2022,40(3):144-151.
[10] HUANG X, JIN H D, ZHANG Y. Risk assessment of earthquake network public opinion based on global search BP neural network. [J] Plos One, 2019, 14(3) :e0212839.
[11] LI S Y, LIUZ X,LI Y L. Temporal and spatial evolution of online public sentiment on emergencies[EB/OL].[2022-05-07]. https://www.sciencedirect.com/science/article/pii/S03064573183 10781.
[12] 赵宇翔,范哲,朱庆华.用户生成内容(UGC)概念解析及研究进展[J].中国图书馆学报,2012,38(5):68-81.
[13] HAN H,ZHANG Y,ZHANG Y, et al. Improving the performance of lexicon-based review sentiment analysis method by reducing additional introduced sentiment bias[EB/OL].[2022-05-07]. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0202523.
[14] CURISKIS S A,DRAKE B,OSBORN T R, et al. An evaluation of document clustering and topic modelling in two online social networks: Twitter and Reddit[EB/OL].[2022-05-07]. https://www.sciencedirect.com/science/article/abs/pii/S0306457318307805.
[15] LI J X, Li X,ZHU B. User opinion classification in social media: A global consistency maximization approach[J]. Information processing & management, 2016, 53(8): 987-996.
[16] 王兰成,张思龙,许和旭.网络舆情事理图谱构建及应用[J].中华医学图书情报杂志,2021,30(5):17-23.
[17] ZHAO Y, JIN X, WANG Y, et al. Semi-supervised auto-encoder based event detection in constructing knowledge graph for social good [C] // IEEE /WIC /ACM international conference on Web intelligence. Greece: Thessaloniki, 2019: 478-485.
[18] ZHAO S, WANG Q, MASSUNG S, et al. Constructing and embedding abstract event causality networks from text snippets[C]//Proceedings of the tenth ACM international conference on Web search and data mining. New York: Association for Computing Machinery,2017: 335-344.
[19] 周京艳,刘如,李佳娱,等.情报事理图谱的概念界定与价值分析[J].情报杂志,2018,37(5):31-36,42.
[20] 鞠海龙,彭珺.基于评论挖掘的用户购买行为因果事理图谱分析[J].情报科学,2021,39(10):170-177.
[21] QIU J, DU Y, WANG Y. Extraction and representation of fea-ture events based on a knowledge model[C]//2008 IEEE/WIC/ACM international conference on Web intelligence and intelligent agent technology. Sydney:IEEE Computer Society, 2008: 219-222.
[22] 单晓红,庞世红,刘晓燕,等.基于事理图谱的网络舆情事件预测方法研究[J].情报理论与实践,2020,43(10):165-170,156.
[23] 田依林,李星.基于事理图谱的新冠肺炎疫情网络舆情演化路径分析[J].情报理论与实践,2021,44(3):76-83.
[24] WANG X,DAN Z,YANG M,et al. Public opinion dissemination on mobile internet- a case of Ebola[J]. Information Discov-ery and Delivery,2017,45(2):87-100.
[25] 郭顺利,步辉,何宏国.基于G-Kmeans的网络问答社区话题用户信息需求聚合方法及应用研究[J/OL].情报理论与实践:1-16[2022-03-14]. http://kns.cnki.net/kcms/detail/11.1762.G3.20220111.1500.004.html.
[26] 河南郑州"7·20"特大暴雨灾害调查报告公布[J].中国应急管理,2022(2):4.
[27] 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.
[28] 知微事见 [EB/OL]. [2022-05-28]. https://ef.zhiweidata.com/event/8265e34c64b90e6510054700/trend.
[29] 知微事见 [EB/OL]. [2022-05-28]. https://ef.zhiweidata.com/event/8265e34c64b90e6510054700/aggregation.
[30] 安璐,吴一丹.突发事件情境下政务微博的舆情引导能力成熟度诊断模型[J/OL].情报理论与实践:1-11[2022-03-17].http://kns.cnki.net/kcms/detail/11.1762.g3.20220124.1903.006.html.
[31] PIAN W, CHI J,MA F. The causes, impacts and countermeasures of COVID-19 "Infodemic": a systematic review using narrative synthesis[EB/OL].[2022-05-07]. https://www.sciencedirect.com/science/article/pii/S0306457321001977.
[32] 张柳,王晰巍,黄博,等.基于LDA模型的新冠肺炎疫情微博用户主题聚类图谱及主题传播路径研究[J].情报学报,2021,40(3):234-244.
