Deep Learning for Journalism: The Bibliometric Analysis of Deep Learning for News Production in the Artificial Intelligence Era
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1711-8_19
Reference13 articles.
1. Caled, D., Silva, M.J.: Digital media and misinformation: An outlook on multidisciplinary strategies against manipulation. Springer Singapore (2022). https://doi.org/10.1007/s42001-021-00118-8
2. Greif, H.: Analogue models and universal machines. Paradigms of epistemic transparency in artificial intelligence. Minds Mach. 32, 111–133 (2022). https://doi.org/10.1007/s11023-022-09596-9
3. Che, S.P., Wang, X., Zhang, S., Kim, J.H.: Effect of daily new cases of COVID-19 on public sentiment and concern: deep learning-based sentiment classification and semantic network analysis. J. Public Heal. (2023). https://doi.org/10.1007/s10389-023-01833-4
4. Mallick, C., Mishra, S., Senapati, M.R.: A cooperative deep learning model for fake news detection in online social networks. J. Ambient. Intell. Humaniz. Comput. 14, 4451–4460 (2023). https://doi.org/10.1007/s12652-023-04562-4
5. Wong, K.F., Lam, X.Y., Jiang, Y., Yeung, A.W.K., Lin, Y.: Artificial intelligence in orthodontics and orthognathic surgery: a bibliometric analysis of the 100 most-cited articles. Head Face Med. 19, 38 (2023). https://doi.org/10.1186/s13005-023-00383-0
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