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International Journal of Big Data Intelligent Technology, 2024, 5(1); doi: 10.38007/IJBDIT.2024.050104.

How Artificial Intelligence Innovations in Journalism Can Overcome the Digital Divide


Yan Li

Corresponding Author:
Yan Li

Moscow state university, Journalism faculty, Moscow, 119991, Russi


With the rapid development of information technology, artificial intelligence has become a key force driving innovation in journalism. However, this technology-driven innovation has also exacerbated the problem of digital divide, especially the difference in information acquisition and processing capabilities. The purpose of this paper is to explore how the application of AI in journalism can effectively bridge this divide. Using case studies and empirical research, it provides an in-depth analysis of the application of AI technologies such as natural language processing, machine learning and big data analytics in the news gathering, editing and distribution process, and how these technologies contribute to the democratization of information and improve the accessibility of news content. By comparing the information accessibility and satisfaction of audiences in different socio-economic contexts, the paper reveals the potential of AI technologies in improving the quality and distribution efficiency of news content. Attention is also given to how AI can enhance user experience and engagement while ensuring news authenticity and transparency. The paper concludes that the judicious application of AI technologies can not only address the digital divide facing journalism, but also provide the impetus to build a more inclusive and interactive news ecosystem. This finding is important for guiding news organizations on how to leverage AI technology innovation to achieve broader social inclusion.


Artificial Intelligence; Journalism Innovation; Digital Divide

Cite This Paper

Yan Li. How Artificial Intelligence Innovations in Journalism Can Overcome the Digital Divide. International Journal of Big Data Intelligent Technology (2024), Vol. 5, Issue 1: 23-35. https://doi.org/10.38007/IJBDIT.2024.050104.


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