1.Agarwal, S. and Mittal, R. 2021. Machine learning in scientometric analysis. Journal of Informatics.

2.Becker, J., Wahle, J.P., Gipp, B. and Ruas, T. 2024. Text generation: A systematic literature review of tasks, evaluation, and challenges. arXiv preprint arXiv:2405.15604.

3.Beltagy, I., Lo, K. and Cohan, A. 2019. SciBERT: A pretrained language model for scientific text. Proceedings of EMNLP-IJCNLP, pp. 3615–3620.

4.Bezerra, D.A., Silva, F.N. and Amancio, D.R. 2025. Leveraging GANs for citation intent classification and its impact on citation network analysis. arXiv preprint arXiv:2505.21162.

5.Blei, D.M., Ng, A.Y. and Jordan, M.I. 2003. Latent Dirichlet allocation. Journal of Machine Learning Research, 3: 993–1022.

6.Bornmann, L. and Leydesdorff, L. 2014. Scientometrics in a changing research landscape. Springer.

7.Chen, C. 2020. Emerging trends in citation network analysis using AI. Scientometrics.

8.Cockburn, A., Henderson, R. and Stern, S. 2018. The impact of artificial intelligence on innovation (NBER Working Paper No. 24449). National Bureau of Economic Research.

9.Devlin, J., Chang, M.-W., Lee, K. and Toutanova, K. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT, pp. 4171–4186.

10.Floridi, L. and Cowls, J. 2019. A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1).

11.Grootendorst, M. 2022. BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794.

12.Hutson, M. 2018. Artificial intelligence faces reproducibility crisis. Science, 359(6377): 725–726.

13.Ioannidis, J.P.A. 2005. Why most published research findings are false. PLoS Medicine, 2(8): e124.

14.Jobin, A., Ienca, M. and Vayena, E. 2019. The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9): 389–399.

15.Kipf, T.N. and Welling, M. 2017. Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations.

16.Liu, Y., Zhao, L., Tu, B., Wang, J., He, Y., Jiang, R., Wu, X., Wen, W. and Liu, J. 2025. Application of artificial intelligence in echocardiography from 2009 to 2024: A bibliometric analysis. Frontiers in Medicine.

17.Mikolov, T., Chen, K., Corrado, G. and Dean, J. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

18.Mittelstadt, B.D., Allo, P., Taddeo, M., Wachter, S. and Floridi, L. 2016. The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2).

19.O’Connor, A.M., Clark, J., Thomas, J., Spijker, R., Kusa, W., Walker, V.R. and Bond, M. 2024. Large language models, updates, and evaluation of automation tools for systematic reviews: A summary of significant discussions at the Eighth Meeting of the International Collaboration for the Automation of Systematic Reviews (ICASR). Systematic Reviews, 13(1): 290.

20.Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D., Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J., Grimshaw, J.M., Hróbjartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E., McDonald, S., McGuinness, L.A., Stewart, L.A., Thomas, J., Tricco, A.C., Welch, V.A., Whiting, P. and Moher, D. 2021. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ., 372(71).

21.Radford, A., Wu, J., Child, R., Luan, D., Amodei, D. and Sutskever, I. 2019. Language models are unsupervised multitask learners. OpenAI Blog.

22.Reimers, N. and Gurevych, I. 2019. Sentence-BERT: Sentence embeddings using Siamese BERT-networks. Proceedings of EMNLP-IJCNLP, pp. 3982–3992.

23.Roman, M., Shahid, A., Khan, S., Koubaa, A. and Yu, L. 2021. Citation intent classification using word embedding. IEEE Access, 9: 9982–9995.

24.Scherbakov, D., Hubig, N., Jansari, V., Bakumenko, A. and Lenert, L.A. 2025. The emergence of large language models as tools in literature reviews: A large language model-assisted systematic review. Journal of the American Medical Informatics Association, 32(6): 1071–1086.

25.Sekaki, Y., Ziane, H. and Khazzar, A. 2025. Artificial intelligence in management studies (2021–2025): A bibliometric mapping of themes, trends, and global contributions. International Journal of Accounting, Finance, Auditing, Management and Economics, 6(9): 62–80.

26.Tang, X., Duan, X. and Cai, Z.G. 2025. Large language models for automated literature review: An evaluation of reference generation, abstract writing, and review composition. arXiv preprint arXiv:2412.13612.

27.Thelwall, M. and Kurt, Z. 2025. Research evaluation with ChatGPT: Is it age, country, length, or field biased? Scientometrics, 130(10): 5323–5343.

28.Van Eck, N.J. and Waltman, L. 2010. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2): 523–538.

29.Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, ?. and Polosukhin, I. 2017. Attention is all you need. Advances in Neural Information Processing Systems, 30.

30.Visser, R. and Dunaiski, M. 2022. Sentiment and intent classification of in-text citations using BERT. Proceedings of the 43rd Conference of the South African Institute of Computer Scientists and Information Technologists, 85: 129–145.

31.Waltman, L. 2016. A review of the literature on citation impact indicators. Journal of Informetrics, 10(2): 365–391.

32.Wang, X. et al. 2022. Graph neural networks in scientometrics. Information Processing & Management.

33.Wilkinson, M.D. et al. 2016. The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3: 160018.

34.Yan, E. and Ding, Y. 2010. Applying centrality measures to impact analysis: A co-authorship network. Journal of Informetrics.

35.Zhang, Y., Wang, Y., Sheng, Q.Z., Yao, L., Chen, H., Wang, K., Deng, C. and Zhao, R. 2025. Deep learning meets bibliometrics: A survey of citation function classification. Journal of Informetrics, 19(1): 101608.