关键词: 情感计算, 机器人, 涉农突发事件, 涉农网络舆情, 组态路径

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