Exploring the Chinese public's affective attitudes towards digital transformation in agriculture: A social...

This study utilizes natural language processing techniques and panel vector autoregression methodology, to delve into the perceived attitudes of social media users towards the digital transformation of agriculture, and to assess its impact on total agricultural output and agricultural science and technology inputs. Data related to agricultural digital transformation were collected from Sina Weibo using web crawlers. The SnowNLP model was employed to infer users' attitudes, encompassing both positive and negative aspects. Furthermore, the study delves into the specific themes capturing users' positive attitudes and explores regional variations in focus. The findings reveal a sustained increase in users' interest in agricultural digital transformation since 2013. Positive attitudes primarily center around green development, agricultural intelligence, and global cooperation and innovation. Moreover, the study establishes a significant positive impact of users' positive attitudes on both total agricultural output value and agricultural science and technology investment, highlighting the constructive influence of user support on the agricultural industry's development.