Self-positioning in space science communication: A corpus-assisted discourse study.

This study incorporates text mining into critical discourse analysis to examine how government science agencies in China and the United States position themselves in space science communication on social media. Two specialized corpora have been built by collecting posts from government science agencies on Weibo from China and X (formerly Twitter) from the U.S. With the help of the text mining tool KH Coder, this study gives a corpus-assisted discourse analysis of the particular ways of positioning at different levels of discourse: (1) topics/themes, (2) addressing terms, and (3) those words that co-occur with self-addressing terms in a sentence. The findings reveal significant differences in their preferential ways of positioning. Chinese government science agencies present themselves as state-affiliated yet approachable institutions, blending achievements and operational efficiency with patriotism and collective pride. Their use of diverse addressing terms and co-occurrence patterns portrays them as experienced, supportive guides, balancing national identity with interpersonal closeness. In contrast, U.S. government science agencies emphasize professionalism, focusing on research, space exploration, mission execution, and audience engagement, with minimal reference to state affiliation. Their addressing terms are formal and standardized, with pride centred on mission success and discovery, highlighting expertise and scientific leadership. Their preferential ways of positioning are further explained in their respective contexts in order to present a proper understanding of these differences.