Analyzing drivers of attitudes toward machine video news: A Xinhua Zhiyun case study

Abstract

The COVID-19 pandemic has highlighted the need for reliable information and news sources. In 2020, Xinhua News Agency launched “Zhiyun,” an epidemic reporting robot that can generate COVID-19 news reports based on visual data. This development raises key issues regarding the effectiveness of machine-generated news compared to traditional sources, especially news related to major public health events such as the pandemic. Using the Cognitive-Affective-Conative Model and ANCOVA method, this paper conducts experimental research to obtain data and studies the impact of machine-made news on the audience’s attitude towards COVID-19 news. The analysis used a 2 × 2 factorial online experimental method to test the impact of two variables: “theme” and “news format.” The research results indicate that the theme and news format significantly affect the audience’s attitude towards epidemic news, and machine-generated video news received a more positive response than news written by human journalists. Based on the results of this study, it can be concluded that machine-generated news has great potential to provide accessible and reliable information during major public health events such as COVID-19. This study has significant implications for the news industry, indicating the possibility of increasing the use of machine news production in the future.