[1] 中国互联网络信息中心.第49次《中国互联网络发展状况统计报告》[R/OL].[2022-11-01]. http://www.cnnic.cn/NMediaFile/old_attach/P020220721404263787858.pdf.
[2] 李玥琪,王晰巍,王小天,等.突发事件情境下的社交媒体风险管理[J].图书馆论坛,2022,42(8):119-128.
[3] 王来华,林竹,毕宏音.对舆情、民意和舆论三概念异同的初步辨析[J].新视野,2004(5):64-66.
[4] 张倩楠,杨尊琦,史浩.有限理性转发者的社会网络舆情演化分析[J].情报杂志,2014,33(9):129-134.
[5] BROWN J J,REINGEN P H.Social ties and word-of-mouth referral behavior[J]. Journal of Consumer Research,1987,14(3):350-362.
[6] 赵荣,潘薇.两级传播理论在情报用户研究中的引入[J].农业图书情报学刊,2007,19(2):22-24.
[7] NOELLE NEUMANN E.The spiral of silence:A theory of public opinion[J]. Journal of Communication,1974,24(2):43-51.
[8] GLYNN C J."Elisabeth Noelle-Neumann":The spiral of silence(Book Review)[J]. Social Forces,1986,64(3):828-829.
[9] 谢新洲. “沉默的螺旋”假说在互联网环境下的实证研究[J]. 现代传播,2003(6):17-22.
[10] KENNAMER J D.Self-serving biases in perceiving the opinions of others:Implications for the spiral of silence[J]. Communication Research,1990,17(3):393-404.
[11] CHAMBERS J R.Explaining false uniqueness:Why we are both better and worse than others[J]. Social and Personality Psychology Compass,2008,2(2):878-894.
[12] NEUBAUM G,KRÄMER N C. Monitoring the opinion of the crowd:Psychological mechanisms underlying public opinion perceptions on social media[J]. Media Psychology,2017,20(3):502-531.
[13] 贾生华,陈宏辉.利益相关者的界定方法述评[J].外国经济与管理,2002,24(5):13-18.
[14] 郑昌兴,苏新宁,刘喜文.突发事件网络舆情分析模型构建——基于利益相关者视阈[J].情报杂志,2015,34(4):71-75.
[15] 方洁,龚立群,魏疆.基于利益相关者理论的微博舆情中的用户分类研究[J].情报科学,2014, 32(1):18-22.
[16] 任立肖,张亮.食品安全突发事件网络舆情的分析模型——基于利益相关者的视角[J].图书馆学研究,2014,324(1):65-70.
[17] VIGNAL LAMBRET C,BARKI E.Social media crisis management:Aligning corporate response strategies with stakeholders’ emotions online[J]. Journal of Contingencies and Crisis Management,2018,26(2):295-305.
[18] 安璐,杜廷尧,李纲,等.突发公共卫生事件利益相关者在社交媒体中的关注点及演化模式[J].情报学报,2018,37(4):394-405.
[19] LIU M,RONG L.An online multi-dimensional opinion dynamic model with misinformation diffusion in emergency events[J]. Journal of Information Science,2021,48(5):640-659.
[20] 刘举胜,何建佳,韩景倜,等. 观点动力学研究现状及进展述评[J].复杂系统与复杂性科学,2021,18(2):9-20.
[21] KIM S M,HOVY E.Determining the sentiment of opinions[C]//COLING’04:Proceedings of the 20th International Conference on Computational Linguistics. Stroudsburg,PA:Association for Computational Linguistics,2004:1367-1373.
[22] 黄传超,胡斌,闫钰炜,等.网络暴力下突发事件中观点决策与舆情反转[J].管理工程学报,2019,33(1):252-258.
[23] 邵鹏,胡平.复杂网络特殊用户对群体观点演化的影响[J]. 电子科技大学学报,2019,48(4):604-612.
[24] 李亚男,戴建华. 基于动态参考点的网络舆情个体观点演化建模研究[J].情报理论与实践,2020,43(11):122-128.
[25] 陈旭辉,柯惠新. 网民意见表达影响因素研究——基于议题属性和网民社会心理的双重视角[J]. 现代传播(中国传媒大学学报),2013,35(3):117-122.
[26] WANG G,WANG Y,LIU K,et al.Multidimensional influencing factors of public opinion information dissemination in social media:Evidence from Weibo dataset[J]. International Journal of Modern Physics B,2019,33(31):1950375.
[27] 邓春林,周舒阳,隆征帆.基于情绪归因理论的突发事件微博用户情绪演化分析[J].科技情报研究,2021,3(3):60-72.
[28] 徐新然,纪雪梅,李长荣.社交媒体中社交关系对突发事件舆论表达的影响——基于平衡理论的研究[J]. 情报资料工作,2019,40(6):51-62.
[29] YAN X,GUO J,LAN Y,et al.A biterm topic model for short texts[C]//WWW’13:Proceedings of the 22nd international conference on World Wide Web. New York:ACM,2013:1445-1456.
[30] 朱晓霞,宋嘉欣,张晓缇.基于主题挖掘技术的文本情感分析综述[J]. 情报理论与实践,2019,42(11):156-163.
[31] 安璐,胡俊阳,李纲.基于主题一致性和情感支持的评论意见领袖识别方法研究[J].管理科学,2019,32(1):3-13.
[32] 纪雪梅,翟冉冉,王芳.突发公共事件政务微博回应方式对公众评论情感的影响研究[J].情报理论与实践,2020,43(12):126-132.
[33] 徐红霞,于倩倩,钱力. 基于主题模型和情感分析的话题交互数据观点对抗性分析[J]. 数据分析与知识发现,2020,4(7):110-117.
[34] 高俊峰,黄微.网络舆情信息受众情感极化的生发机理及干预措施分析[J]. 情报理论与实践,2019,42(5):122-127.
[35] TIAN H,GAO C,XIAO X,et al.SKEP:sentiment knowledge enhanced pre-training for sentiment analysis[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg,PA:Association for Computational Linguistics,2020:4067-4076.
[36] 安璐,欧孟花.突发公共卫生事件利益相关者的社会网络情感图谱研究[J].图书情报工作,2017,61(20):120-130.
[37] 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.
[38] WANG Z,LIU H,LIU W,et al.Understanding the power of opinion leaders’ influence on the diffusion process of popular mobile games:Travel Frog on Sina Weibo[J]. Computers in Human Behavior,2020,109:106354.
[39] FIEDLER K.Beware of samples! A cognitive-ecological sampling approach to judgment biases[J]. Psychological Review,2000,107(4):659-676.
[40] SHAH A K,OPPENHEIMER D M.Heuristics made easy:An effort-reduction framework[J]. Psychological Bulletin,2008,134(2):207-222.
[41] XIONG J,FENG X,TANG Z.Understanding user-to-user interaction on government microblogs:An exponential random graph model with the homophily and emotional effect[J]. Information Processing & Management,2020,57(4):102229.
[42] DONG W,HUANG Y,LEHANE B,et al.XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring[J]. Automation in Construction,2020,114:103155.
[43] KE G,MENG Q,FINLEY T,et al.Lightgbm:A highly efficient gradient boosting decision tree[C]//NIPS’17:Proceedings of the 31st International Conference on Neural Information Processing Systems. New York:Curran Associates Inc.,2017:3149-3157.
[44] LUNDBERG S M,LEE S I.A unified approach to interpreting model predictions[C]//NIPS’17:Proceedings of the 31st International Conference on Neural Information Processing Systems. New York:Curran Associates Inc.,2017:4768-4777.