Abstract
This research aims to evaluate the articles published from 2018 to 2023. We focused on the deep learning issues that have risen in the last decade. Deep learning is the popular approach in news research, especially in the classification or detection of the news. Moreover, in Artificial Intelligence (AI), numbers of applications are invented to help journalists to optimization their work. On the other hand, it can be the dark side of AI if used without wisdom. We have used the bibliometric method to extract the total data Nā=ā69 to be analyzed, and we used several parameters such as scholarly landscape, keyword plus theme, co-networking, and evolution of research theme. The result of this research is that we found the matrix of research direction for future works, and it should be observed closely to the news classification and detection research. Since the large language model was invented, news production has changed and influenced journalism practices.
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Acknowledgement
This work was supported in part by Chaoyang University of Technology (CYUT) and the Ministry of Science and Technology of Taiwan, R.O.C. (Grant No NSTC 112-2410-H-324-004). We also express our thanks for supports from Chaoyang University of Technology.
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Mayopu, R.G., Chen, LS. (2024). Deep Learning for Journalism: The Bibliometric Analysis of Deep Learning for News Production in the Artificial Intelligence Era. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2074. Springer, Singapore. https://doi.org/10.1007/978-981-97-1711-8_19
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DOI: https://doi.org/10.1007/978-981-97-1711-8_19
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