Abstract

The continuous improvement and wide array of applications of algorithms have demonstrated both the promise of technological power and the perils of new problems and some entrenched social issues. At this time, mainstream media being a vital channel for information dissemination, their coverage of algorithms can reflect different attitudes and expectations towards algorithmic development from diverse groups. Thereby, the objective of this study is to explore how influential media tell the story of algorithms to people, how individuals from distinct walks view this contentious topic, and, accordingly, what we can expect to witness in the following report on algorithms from news agencies. Based on the Framing Theory, this study systematically analyzes the news reports about algorithms published by Xinhua News Agency over the past two years (2021-2022) on the basis of the thematic framework, responsibility framework, and emotional framework. The result of it reveals that Xinhua News Agency has paid much attention to the issues of regulation and governance of algorithms from the perspective of the government. Ethical concerns regarding its fair usage are also highly debated among the general public. Companies and research institutions mainly focus on publicizing their latest achievements in innovative applications and technological breakthroughs, while the former is absent from the discussions and draft of regulations in this line. Worthy of note, negative reports on this domain were prevalent among the public, while the Chinese government, businesses and research institutions tended to approach algorithm-related topics from a positive or neutral standpoint. In the future, relevant disputes will persist in the public opinion field, and people could enhance their algorithmic literacy throughout the process. Governance and development may go ahead in step. Co-governance with the engagement of enterprises and diversification of algorithmic applications both signify a promising tomorrow of this technology.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call