Covid-19 Disinformation and Public Perceptions: Empowering Affective Analysis through Large Language Model

Social media is flooded with disinformation about outbreaks, and emotional and ideological appeals can significantly influence public opinion more than objective facts. This study delves into the cognitive and affective responses of the public when confronted with disinformation about the Covid-19 epidemic on Weibo. To cope with the complexity of emotions, we designed a multidimensional complex affective space to depict the intricate cognition of the public. Drawing from human thinking processes, we proposed a fine-tuning method based on the large language model ChatGLM2-6B to augment semantic information and bolster the sentiment analysis capabilities of the large language model. The experimental results indicated that incorporating semantic information enhances the prediction accuracy of the large language model, with varying effects across different sentiment dimensions. Moreover, we analyze the public’s topics concerning epidemic disinformation, and the findings demonstrate that the public tends to generate similar topics for such fake news. Emojis have emerged as a means for individuals to supplement the expression of complex emotions.