基于情感计算的涉农突发事件网络舆情态势分析与引导策略

doi:10.13998/j.cnki.issn1002-1248.25-0317

Abstract

Abstract:

[Purpose/Signficance] In the context of the increasingly widespread adoption of digital communication, agriculture-related emergencies often trigger complex and ever-changing public opinion online due to their high level of specialization and the significant cognitive barriers they pose to the general public. Emotional factors play a pivotal role in the evolution and governance of online public opinion. However, current research into how public opinion is guided in relation to agricultural emergencies still fails to systematically address emotional factors. [Method/Process] Therefore, the study constructed an analytical framework for emotional guidance in agricultural-related public opinion, integrating information subjects, information content, and the information environment. The framework was based on three complementary theories: information ecology theory, social amplification of risk theory, and negativity bias theory. It explored the correlations and combined effects of emotional factors with individual audiences, media, and the information environment. A total of 31 online public opinion cases involving agriculture, rural areas, and farmers were selected from the "Public Opinion Daily Reports" published by the People's Daily Online Public Opinion Data Center, covering the period from January 2021 to June 2025. The Weibo platform was chosen for this study, and data were collected by searching for case names and related topics on Weibo to capture raw data for conditional and outcome variables. Sentiment analysis was introduced to identify and quantify emotional characteristics in public opinion, and fuzzy-set qualitative comparative analysis (fsQCA) was employed to investigate how various factors collectively influence the guidance of online public opinion in public emergencies. The aim is to reveal the emotional guidance mechanisms and the logic behind effect formation in online public opinion regarding agricultural emergencies. [Results/Conclusions] The study found that public opinion in agriculture exhibits typical characteristics of equifinal multiple causation, whereby various combinations of factors can produce similar guiding effects. In contexts of high emotional polarisation, the pathways may rely on traffic restriction and emotional substitution regulation. In contexts of low emotional polarization, they may rely on the construction of emotional framing by authoritative media and opinion leaders. In different contexts, information clarity and netizens' emotional involvement can form a substitution relationship with the degree to which the platform intervenes in emotional regulation. This necessitates dynamic adjustments to guidance strategies based on specific situations. Based on this, the governance of agriculture-related public opinion online should shift towards a systematic emotional governance framework that leverages affective computing to expand the range of channels and strengthen the basis of public opinion. Efforts should also be devoted to strengthening dynamic response mechanisms based on real-time emotional monitoring. The aim should be to construct a sentiment guidance system for public opinion featuring dynamic allocation, multi-party collaboration, and precise reach.

Key words: affective computing, robot, agricultural-related emergencies, agricultural-related online public opinion, configurational paths

CLC Number:

HAO Yali, SONG Yifei, A Zhongping, LIANG Ying. Analysis of Online Public Opinion Situations Related to Agricultural Emergencies Based on Affective Computing and Guidance Strategy[J].Journal of library and information science in agriculture, 2025, 37(10): 37-52.

Figures/Tables 6

Fig.1

Table 1

Table 2

Operational indicator table for online public opinion in agriculture-related emergencies"

变量属性	选取维度	细分指标	指标量化解释
条件变量	涉农信息本体	涉农舆情信息清晰度	统计该事件“平均帖文长度”“专业术语密度”指标来度量信息清晰度
	涉农信息本体	固有的情感极化程度	评估涉农事件标题及内容信息中，含有情感冲击词汇（毒害、坑农、欺骗、绝收等）的出现频率
	涉农信息主体	权威媒体信息情感偏向性	统计参与报道或评论该涉农突发事件的权威媒体账号数量，通过情感计算分析该事件的媒体发帖、评论、点赞与转发报道中标题和核心段落所使用的具有情感倾向性的词语数量信息，以此衡量该维度指标
		意见领袖的情感动员力	据粉丝数或认证类型筛选出事件讨论中具有“KOL”特征的用户（粉丝百万级别的知名人士、自媒体大V等）。计算其在涉农突发事件中原创、评论、转发的帖文中的情感倾向数值
		网民情感卷入度	通过“自我叙事”与“共情表达”得分衡量此指标。统计事件话题下独立用户的发帖数量中包含第一人称代词出现次数占帖文总词数的比例，以此衡量“自我叙事得分”；以及事件话题下独立用户的发帖文中包含“泪目了”“破防了”“狠狠共情”等表达的次数占帖文总次数的比例，以此代表“共情表达得分”。两者按比例加和计算
		政府回应的情感效能	设置“官方首次回应时滞”与“回应共情力”两个指标综合量化政府回应的情感效能。统计政府官方微博账号、调查报告等渠道首次发布权威信息的时间，计算与事件曝光时间的差值，以此衡量首次回应时滞；通过内容分析，评估官方通告中是否包含安抚、理解、共情类词汇信息，若有该类信息则赋值为1，反之为0。两者结合公式综合测度
	涉农信息环境	舆情扩散广度	根据抓取的帖文附带的用户地域标签，统计涉及的省份数量，得到舆情扩散广度数值
	涉农信息环境	平台情感调控介入度	评估平台是否具有主动推送辟谣信息、在话题下置顶权威客观报道、对煽动性内容进行“降温”限流等干预行为。若舆情治理平台进行该类干预的次数远高于平均，赋值为1；反之，平台放任不管记为0
结果变量	涉农突发事件网络舆情情感引导效果		构建引导效果评价指数来量化该维度，结合“情感倾向性”“舆情情感动能”“舆情持续时长”3个指标，通过计算公式综合判定

Table 2

Table 3

Table 4

Table 5

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变量	模糊集校准
变量	完全隶属	交叉点	完全不隶属
涉农突发事件网络舆情情感引导效果	1 896.830	68.760	43.89
信息清晰度	283.500	140.000	53.75
事件固有的情感极化程度	446.560	4.020	0.33
权威媒体信息情感偏向性	2 655.420	87.328	8.84
意见领袖情感动员力	14 754.600	458.030	1.25
网民情感卷入度	106.305	0.790	0.00
政府回应的情感效能	93.290	4.080	0.00
舆情扩散广度	31.000	21.000	7.00
平台情感调控介入程度	/	/	/

条件变量	突发事件网络舆情引导效果
条件变量	一致性	覆盖度
涉农舆情信息清晰度	0.693	0.599
~涉农舆情信息清晰度	0.688	0.537
事件固有的情感极化程度	0.758	0.793
~事件固有的情感极化程度	0.707	0.477
权威媒体信息情感偏向性	0.710	0.750
~权威媒体信息情感偏向性	0.743	0.498
意见领袖情感动员力	0.790	0.754
~意见领袖情感动员力	0.671	0.482
网民情感卷入度	0.684	0.751
~网民情感卷入度	0.705	0.461
政府回应的情感效能	0.662	0.668
~政府回应的情感效能	0.813	0.561
舆情扩散广度	0.718	0.574
~舆情扩散广度	0.644	0.542
平台情感调控介入程度	0.607	0.406
~平台情感调控介入程度	0.393	0.417

条件	A1	A2	B1	B2	C1	C2	D	E1	E2
涉农舆情信息清晰度（a1）	⊗	⊗	●	●	●	⊗	⊗	⊗	●
事件固有的情感极化程度（b1）	●	●	●	●	●	●	⊗	●	●
权威媒体信息情感偏向性（c1）	⊗	⊗	⊗	⊗	●	●	●	●	●
意见领袖情感动员力（d1）	⊗	⊗	⊗	●	●	●	●	●	●
网民情感卷入度（e1）	●	⊗	⊗	⊗	●	●	●	⊗	●
政府回应的情感效能（f1）	●	●	⊗	●	⊗	⊗	●	●	●
舆情扩散广度（g1）	⊗	⊗	●	⊗	⊗	⊗	⊗	●	●
平台情感调控介入程度（h1）	⊗	●	⊗	⊗	⊗	●	●	●	●
原覆盖率	0.109	0.138	0.137	0.130	0.108	0.169	0.124	0.151	0.200
净覆盖率	0.497	0.046	0.035	0.031	0.046	0.074	0.027	0.010	0.073
一致性	0.979	0.989	0.950	0.943	0.972	0.991	0.955	0.818	0.941
代表型案例	30	29	20	28	31	27	11	23	24
解的一致性	0.957
解的覆盖度	0.656

Analysis of Online Public Opinion Situations Related to Agricultural Emergencies Based on Affective Computing and Guidance Strategy

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