信息生态视角下突发公共事件网络舆情热度生成机理研究

doi:10.13998/j.cnki.issn1002-1248.25-0084

Abstract

Abstract:

[Purpose/Significance] Public emergencies frequently trigger online public opinion, exacerbating public panic and threatening social stability. The intrinsic linkage between public emergencies and online discourse amplifies the dissemination of public emotions, attitudes, and perspectives across online platforms, creating a feedback loop that influences event dynamics. Investigating the generation mechanism of public opinion on hot topics in such contexts provides critical theoretical foundations for mitigating cyber discourse risks, while enhancing the accuracy and efficiency of governmental mangement over online public opinion. [Method/Process] From an information ecology perspective, this study employs fuzzy-set qualitative comparative analysis to examine the online public opinion heat of 50 public emergencies between 2020 and 2022. We analyze eight conditional variables across four dimensions - information, information person, information technology, and information environment - including peak propagation speed, peak event popularity, netizen attention, opinion leaders' communication power, important media participation, central media coverage, the proportion of the overall public opinion field, and event duration. Single-factor necessity detection and configuration analysis were performed, and robustness was tested by adjusting calibration points and consistency thresholds. Finally, based on empirical findings, we interpreted case studies and proposed a mechanism for the generation of online public opinion heat in public emergencies. [Results/Conclusions] The results reveal that information and information people are the primary drivers and key causes of hot public opinion. Although information environment and information technology are not necessary conditions, they still contribute to the process. In public emergencies, multiple factors jointly influence online public opinion, and no single factor alone determines its intensity. Rather, the complementarity of multiple factors can, to some extent, substitute for seemingly necessary conditions. The key findings reveal that the event's peak plays a dominant role in driving high online public opinion intensity, and directly triggers its rapid outbreak, while the absence of major media participation and short event duration - core conditions for non-hot events - significantly reduce public engagement due to limited coverage and transient attention. Additionally, opinion leaders' communication power exhibits a strong positive correlation with public opinion on hot topics, as their amplified expressions attract more attention from netizens and further amplify the momentum of the discourse. These findings will provide valuable insights for effectively managing and controlling online public opinion during emergencies. Future research should examine the impact of emotional shifts, such as positive, negative, and neutral emotions, on the virality of online public opinion during emergencies, while also exploring the underlying mechanisms of such emotional shifts. Additionally, future studies should differentiate between policy stages in emergency development and examine how policy interventions shape the dynamics of public opinion. Finally, network analysis techniques (e.g., forwarding relationship networks, key evolutionary network structures) should be employed to uncover the mechanisms that drive public opinion heat in emergency-related discourse.

Key words: information ecology, fuzzy set qualitative comparative analysis, public emergencies, internet public opinion heat, social media, information behavior

CLC Number:

G353.1

YOU Ge, LI Jielin, ZHANG Fangshun. Generating Mechanism of Online Public Opinion Heat in Public Emergencies from the Perspective of Information Ecology: Fuzzy Set Qualitative Comparative Analysis Based on 50 Cases[J].Journal of library and information science in agriculture, 2025, 37(1): 86-99.

Figures/Tables 7

Fig.1

Table 1

Research case database"

编号	发生年份	典型事件	影响力指数	编号	发生年份	典型事件	影响力指数
1	2022	东航搭载132人客机MU5735在广西梧州藤县坠毁	94.0	26	2022	广东广州新增本土无症状1例	74.1
2	2022	四川泸定发生6.8级地震	90.9	27	2022	广东惠州发生4.1级地震	65.1
3	2022	唐山一烧烤店内多名男子殴打女生	89.9	28	2022	长春一餐厅起火致17人死亡	64.2
4	2022	佩洛西窜访台湾	89.9	29	2022	兰州野生动物园观光车侧翻16人受伤	62.1
5	2022	重庆多处发生山火	84.4	30	2022	台湾花莲发生5.9级地震	62.1
6	2022	四川雅安市芦山县发生6.1级地震	82.6	31	2022	新疆阿勒泰7名工人因极寒天气遇难	60.8
7	2022	长沙一楼体发生倒塌	82.1	32	2022	宁波发生一火灾事故致7人死亡	58.1
8	2022	动车D2809在贵州榕江站撞上泥石流脱线	79.1	33	2021	西北工业大学遭受境外网络攻击	57.7
9	2022	青海省海北藏族自治州门源县发生6.9级地震	78.8	34	2021	珠海隧道透水事故14名被困人员遇难	74.7
10	2022	新疆一高层住宅楼火灾致10人死亡	78.2	35	2021	安徽太湖一皮卡车坠入山沟12人遇难	65.6
11	2022	四川彭州一河道突发山洪	78.2	36	2021	山东发现首例新冠变异毒株感染确诊患者	62.7
12	2022	台湾连发多次地震	77.6	37	2021	呼和浩特一小区爆炸致1死17伤	61.4
13	2021	河南遭遇特大暴雨	98.4	38	2021	中印边防人员冲突	79.2
14	2021	广东再现本土确诊病例	93.1	39	2020	台风“鹦鹉”在广东阳江海陵岛登陆	71.5
15	2021	山西多地遭暴雨袭击	85.2	40	2020	全国医务人员确诊新冠肺炎1 716例	70.7
16	2021	安徽再现本土确诊病例	84.4	41	2020	西藏那曲6.6级地震	66.7
17	2021	四川泸州6.0级地震	82.8	42	2020	湖北武汉中心医院染新冠医生胡卫锋离世	66.4
18	2021	上海再增本土确诊病例	80.4	43	2020	湖北黄梅近500名考生因暴雨被困	66.3
19	2020	青海发生7.4级地震	79.7	44	2020	武汉一工地塔吊倒塌	65.0
20	2020	福建泉州一指定隔离酒店倒塌	85.4	45	2020	宁波通报疫情典型案例：25人参加聚会被确诊	62.6
21	2020	疫情“吹哨人”李文亮医生因新冠肺炎去世	84.8	46	2020	长沙一男子持刀行凶致3死2伤	61.1
22	2020	武汉宣布“封城”	84.5	47	2020	山西五台山景区发生森林大火	58.5
23	2020	四川西昌突发森林大火	83.7	48	2020	南昌街头发生恶性伤人交通事故	58.1
24	2020	贵州安顺一公交车坠入水库	83.5	49	2020	台湾出现首例新冠病毒变种病例	57.9
25	2020	浙江温岭一油罐车爆炸	80.6	50	2020	陕西煤矿事故被困七人全部遇难	56.8

Table 1

Table 2

Table 3

Table 4

Table 5

Fig.2

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变量类别	变量名称		变量赋值
结果变量	舆情热度生成		事件在自媒体和网络媒体上的累积传播效果加和
条件变量	信息	峰值传播速度	事件在峰值时每小时达到的传播条数
	信息	事件热度峰值	事件在单位时间内在自媒体和网络媒体上的传播效果
	信息人	网民关注度	事件持续期间平均每小时信息传播条数
	信息人	意见领袖传播力	参与事件讨论的粉丝量排名前5的微博大V粉丝数量总和
	信息环境	重要媒体参与	网络媒体参与事件报道的数量
		央级媒体报道	中央新闻单位×参与事件报道占比
		整体舆论场占比	事件最近1小时的热度÷当前所有在更新事件的热度
	信息技术	事件持续时间	舆情疏解所耗时长

变量类型	变量名称		完全不隶属	交叉点	完全隶属
结果变量	舆情热度生成		57.84	74.70	93.37
条件变量	信息	峰值传播速度	37.40	500.00	8 381.10
	信息	事件热度峰值	954.00	12 277.00	148 720.60
	信息人	网民关注度	1.70	15.00	99.70
	信息人	意见领袖传播力	1 696.40	7931.00	53 603.30
	信息环境	重要媒体参与	9.70	82.00	173.80
		央级媒体报道	0.01	0.09	0.79
		整体舆论场占比	0.01	0.08	0.68
	信息技术	事件持续时间	74.10	137.00	575.90

变量	高热度舆情生成		非高热度舆情生成
变量	一致性	覆盖度	一致性	覆盖度
峰值传播速度	0.806 295	0.929 735	0.358 257	0.470 451
~峰值传播速度	0.540 759	0.425 262	0.946 492	0.847 668
事件热度峰值	0.830 912	0.910 659	0.380 227	0.474 569
~事件热度峰值	0.520 581	0.424 482	0.928 420	0.862 126
网民关注度	0.910 815	0.921 224	0.333 097	0.383 673
~网民关注度	0.390 638	0.339 649	0.931 609	0.922 456
意见领袖传播力	0.830 508	0.934 181	0.346 917	0.444 394
~意见领袖传播力	0.506 053	0.404 908	0.948 618	0.864 385
重要媒体参与	0.900 323	0.919 242	0.351 878	0.409 147
~重要媒体参与	0.421 307	0.363 383	0.930 546	0.914 027
央级媒体报道	0.675 948	0.710 649	0.464 564	0.556 215
~央级媒体报道	0.577 885	0.486 578	0.758 327	0.727 149
整体舆论场占比	0.887 813	0.896 496	0.355 067	0.408 313
~整体舆论场占比	0.414 044	0.360 506	0.909 993	0.902 319
事件持续时间	0.770 379	0.820 722	0.399 008	0.484 093
~事件持续时间	0.515 738	0.429 724	0.852 233	0.808 675

变量		高热度生成路径			非高热度生成路径
变量		H1	H2	H3	~H1	~H2
信息	峰值传播速度	●	●	○	○
信息	事件热度峰值	★	★	★	○	○
信息人	网民关注度	●	○	●	○	○
	意见领袖传播力	●	●	●		○
	重要媒体参与	●	○	●	☆	☆
信息环境	央级媒体报道		○	●
信息环境	整体舆论场占比		○	●	○	○
信息技术	事件持续时间	●	●	○	☆	☆
一致性		0.999	0.990	0.999	0.998	0.991
覆盖原始度		0.687	0.245	0.289	0.679	0.759
唯一覆盖度		0.354	0.023	0.013	0.015	0.095
解的一致性		0.997			0.991
解的覆盖度		0.723			0.774

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Generating Mechanism of Online Public Opinion Heat in Public Emergencies from the Perspective of Information Ecology: Fuzzy Set Qualitative Comparative Analysis Based on 50 Cases

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