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Mass Media Trends: Implementation of AI, ML, SEO and Data Journalism in the Worldwide Media Landscape

https://doi.org/10.25205/1818-7919-2022-21-6-59-68

Abstract

This article refers to the use and implementation of new technologies, like AI (Artificial Intelligence), ML (Machine Learning), Big Data, data journalism and SEO optimization in traditional media, new media and brand journalism. The author explains what is “data journalism” in the opinion of Russian and English-speaking researchers. The article describes the main accidents and dates of the establishing of date journalism in the USA, Russia. The author examines how artificial intelligence and machine learning are implemented by editorial offices, as an example author takes “Forbes”, “Reuters”, “The Guardian” and other media outlets. This study is based on both primary and secondary sources of qualitative data to understand the challenges and opportunities to work in the ever-changing media landscape. Based on the empirical base, the author examines the implementation of data journalism in Latvia – using the materials published in collaboration with the author in the print magazine “Forbes Latvija” during the period of 2017– 2019. Based on research methods and the use of SEO tools while writing articles in English for an IT company in the period from 2019–2021, the author examines the impact of SEO promotion in traditional media, new media and brand journalism. There is a tendency nowadays that brand journalism is affecting the agenda of media, and having considered this issue in more detail, the author gives examples of specific SEO optimization and promotion tools that help brand journalism in achieving this goal.

About the Author

A. A. Shnaider
Baltic International Academy
Latvia

Anna A. Shnaider, Doctor of Sciences (Philology)

Riga



References

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Review

For citations:


Shnaider A.A. Mass Media Trends: Implementation of AI, ML, SEO and Data Journalism in the Worldwide Media Landscape. Vestnik NSU. Series: History and Philology. 2022;21(6):59-68. (In Russ.) https://doi.org/10.25205/1818-7919-2022-21-6-59-68

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ISSN 1818-7919 (Print)