From assistance to symbiosis: a study on human-machine collaborative practices in mainstream media’s news operations from the perspective of media convergence

Abstract

From the perspective of media convergence theory, this study examines human-machine collaboration practices in mainstream media news operations, the mechanisms of political influence, and the active paths through which journalists achieve balance and adaptation. Using content analysis and case study methods, the research conducts statistical analysis of human-machine collaborative reporting in mainstream media and investigates cases including Xinhua News Agency’s “Editorial Assistant” and “CCTV’s Large Model”. The findings reveal that hard news is more constrained by political security factors than soft news, with significantly lower levels of AI application. Major thematic reports emphasize human-machine collaboration in the presentation stage; investigative and explanatory reports focus on the information collection stage; and routine reports concentrate on the content writing stage. Political security and guidance shape human-machine collaboration practices in mainstream media, and active journalists achieve adaptation through prioritization and balance in this process. In the context of deep media convergence, this adaptation provides insights for achieving the unity of orthodoxy and innovation in human-machine collaborative reporting.