{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:52:55Z","timestamp":1760151175404,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. Among these approaches, some works have studied dynamism in scientific literature by employing text analysis techniques that rely on topic models to study the dynamics of research topics. Unlike topic models that do not delve deeper into the content of scientific publications, for the first time, this paper uses temporal word embeddings to automatically track the dynamics of scientific keywords over time. To this end, we propose Vec2Dynamics, a neural-based computational history approach that reports stability of k-nearest neighbors of scientific keywords over time; the stability indicates whether the keywords are taking new neighborhood due to evolution of scientific literature. To evaluate how Vec2Dynamics models such relationships in the domain of Machine Learning (ML), we constructed scientific corpora from the papers published in the Neural Information Processing Systems (NIPS; actually abbreviated NeurIPS) conference between 1987 and 2016. The descriptive analysis that we performed in this paper verify the efficacy of our proposed approach. In fact, we found a generally strong consistency between the obtained results and the Machine Learning timeline.<\/jats:p>","DOI":"10.3390\/bdcc6010021","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:24:21Z","timestamp":1645475061000},"page":"21","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Vec2Dynamics: A Temporal Word Embedding Approach to Exploring the Dynamics of Scientific Keywords\u2014Machine Learning as a Case Study"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0185-103X","authenticated-orcid":false,"given":"Amna","family":"Dridi","sequence":"first","affiliation":[{"name":"School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0339-4474","authenticated-orcid":false,"given":"Mohamed Medhat","family":"Gaber","sequence":"additional","affiliation":[{"name":"School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"},{"name":"Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4013-5415","authenticated-orcid":false,"given":"Raja Muhammad Atif","family":"Azad","sequence":"additional","affiliation":[{"name":"School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1160-9140","authenticated-orcid":false,"given":"Jagdev","family":"Bhogal","sequence":"additional","affiliation":[{"name":"School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TBDATA.2016.2641460","article-title":"Big Scholarly Data: A Survey","volume":"3","author":"Xia","year":"2017","journal-title":"IEEE Trans. Big Data"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.eswa.2018.11.021","article-title":"FAST2: An intelligent assistant for finding relevant papers","volume":"120","author":"Yu","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s11192-017-2514-8","article-title":"Identifying dynamic knowledge flow patterns of business method patents with a hidden Markov model","volume":"113","author":"An","year":"2017","journal-title":"Scientometrics"},{"key":"ref_4","unstructured":"Anderson, A., McFarland, D., and Jurafsky, D. (2012, January 10). Towards a Computational History of the ACL: 1980\u20132008. Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries, Jeju Island, Korea."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Effendy, S., and Yap, R.H. (2017, January 3\u20137). Analysing Trends in Computer Science Research: A Preliminary Study Using The Microsoft Academic Graph. Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.","DOI":"10.1145\/3041021.3053064"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hall, D., Jurafsky, D., and Manning, C.D. (2008, January 25\u201327). Studying the History of Ideas Using Topic Models. Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP \u201908, Honolulu, HI, USA.","DOI":"10.3115\/1613715.1613763"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/2500892","article-title":"Trends in Computer Science Research","volume":"56","author":"Hoonlor","year":"2013","journal-title":"Commun. ACM"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1007\/s11192-018-2695-9","article-title":"Emerging trends and new developments in information science: A document co-citation analysis (2009\u20132016)","volume":"115","author":"Hou","year":"2018","journal-title":"Scientometrics"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1016\/j.ijinfomgt.2016.07.007","article-title":"A Computational Literature Review of the Technology Acceptance Model","volume":"36","author":"Mortenson","year":"2016","journal-title":"Int. J. Inf. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1007\/s11192-018-2709-7","article-title":"Structure and evolution of innovation research in the last 60 years: Review and future trends in the field of business through the citations and co-citations analysis","volume":"115","author":"Rossetto","year":"2018","journal-title":"Scientometrics"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1007\/s11192-018-2651-8","article-title":"Bibliometric analysis to identify an emerging research area: Public Relations Intelligence","volume":"115","year":"2018","journal-title":"Scientometrics"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1007\/s11192-017-2503-y","article-title":"How to identify metaknowledge trends and features in a certain research field? Evidences from innovation and entrepreneurial ecosystem","volume":"113","author":"Zhang","year":"2017","journal-title":"Scientometrics"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11192-017-2560-2","article-title":"A content-based citation analysis study based on text categorization","volume":"114","author":"Taskin","year":"2018","journal-title":"Scientometrics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.eswa.2019.06.026","article-title":"Multi-sense embeddings through a word sense disambiguation process","volume":"136","author":"Ruas","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"176414","DOI":"10.1109\/ACCESS.2019.2957440","article-title":"Leap2Trend: A Temporal Word Embedding Approach for Instant Detection of Emerging Scientific Trends","volume":"7","author":"Dridi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1007\/s11192-017-2555-z","article-title":"Identifying emerging research fields: A longitudinal latent semantic keyword analysis","volume":"113","author":"Weismayer","year":"2017","journal-title":"Scientometrics"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.eswa.2019.06.014","article-title":"Technical analysis and sentiment embeddings for market trend prediction","volume":"135","author":"Picasso","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s11192-017-2609-2","article-title":"Toward predicting research proposal success","volume":"114","author":"Boyack","year":"2018","journal-title":"Scientometrics"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Huang, Z., Yan, Y., and Chen, Y. (2015, January 18\u201322). Science Navigation Map: An Interactive Data Mining Tool for Literature Analysis. Proceedings of the 24th International Conference on World Wide Web, WWW\u201915 Companion, Florence, Italy.","DOI":"10.1145\/2740908.2741733"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.eswa.2019.02.001","article-title":"Geoscience keyphrase extraction algorithm using enhanced word embedding","volume":"125","author":"Qiu","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1007\/s11192-017-2548-y","article-title":"RTRS: A recommender system for academic researchers","volume":"113","author":"Alam","year":"2017","journal-title":"Scientometrics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1007\/s11192-017-2543-3","article-title":"Sleeping beauties in Computer Science: Characterization and early identification","volume":"113","author":"Dey","year":"2017","journal-title":"Scientometrics"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Effendy, S., Jahja, I., and Yap, R.H. (2014, January 7\u201311). Relatedness Measures Between Conferences in Computer Science: A Preliminary Study Based on DBLP. Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea.","DOI":"10.1145\/2567948.2579035"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fuhr, N., Kov\u00e1cs, L., Risse, T., and Nejdl, W. (2016). The Problem of Categorizing Conferences in Computer Science. Research and Advanced Technology for Digital Libraries, Springer.","DOI":"10.1007\/978-3-319-43997-6"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1007\/s11192-017-2572-y","article-title":"Computing research in the academy: Insights from theses and dissertations","volume":"114","author":"Kim","year":"2018","journal-title":"Scientometrics"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/S0950-5849(02)00049-6","article-title":"Research in software engineering: An analysis of the literature","volume":"44","author":"Glass","year":"2002","journal-title":"Inf. Softw. Technol."},{"key":"ref_27","unstructured":"Schlagenhaufer, C., and Amberg, M. (2015, January 26\u201329). A descriptive literature review and classification framework for gamification in information systems. Proceedings of the Twenty-Third European Conference on Information Systems (ECIS), M\u00fcnster, Germany."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1177\/002188638602200207","article-title":"Grounded Theory and Organizational Research","volume":"22","author":"Martin","year":"1986","journal-title":"J. Appl. Behav. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e119","DOI":"10.7717\/peerj-cs.119","article-title":"How are topics born? Understanding the research dynamics preceding the emergence of new areas","volume":"3","author":"Salatino","year":"2017","journal-title":"PeerJ Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3389\/frma.2018.00009","article-title":"Predictive Effects of Novelty Measured by Temporal Embeddings on the Growth of Scientific Literature","volume":"3","author":"He","year":"2018","journal-title":"Front. Res. Metrics Anal."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dridi, A., Gaber, M.M., Azad, R.M.A., and Bhogal, J. (2019, January 14\u201319). DeepHist: Towards a Deep Learning-based Computational History of Trends in the NIPS. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852140"},{"key":"ref_32","first-page":"3111","article-title":"Distributed Representations of Words and Phrases and their Compositionality","volume":"26","author":"Mikolov","year":"2013","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., and Manning, C.D. (2014, January 25\u201329). Glove: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.","DOI":"10.3115\/v1\/D14-1162"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1162\/tacl_a_00051","article-title":"Enriching Word Vectors with Subword Information","volume":"5","author":"Bojanowski","year":"2017","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_35","unstructured":"Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv."},{"key":"ref_36","unstructured":"Mikolov, T., Yih, W.t., and Zweig, G. (2013, January 9\u201314). Linguistic Regularities in Continuous Space Word Representations. Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, GA, USA."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dridi, A., Gaber, M.M., Azad, R.M.A., and Bhogal, J. (2018). k-NN Embedding Stability for word2vec Hyper-Parametrisation in Scientific\nText. International Conference on Discovery Science, Springer.","DOI":"10.1007\/978-3-030-01771-2_21"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Cudr\u00e9-Mauroux, P., Heflin, J., Sirin, E., Tudorache, T., Euzenat, J., Hauswirth, M., Parreira, J.X., Hendler, J., Schreiber, G., and Bernstein, A. (2012). Mining Semantic Relations between Research Areas. The Semantic Web\u2014ISWC 2012, Springer.","DOI":"10.1007\/978-3-642-35173-0"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Orkphol, K., and Yang, W. (2019). Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2vec and WordNet. Future Internet, 11.","DOI":"10.3390\/fi11050114"},{"key":"ref_40","unstructured":"(2021, December 01). Wikipedia. Timeline of Machine Learning. Available online: https:\/\/en.wikipedia.org\/wiki\/Timeline_of_machine_learning."},{"key":"ref_41","unstructured":"Ho, T.K. (1995, January 14\u201316). Random Decision Forests. Proceedings of the Third International Conference on Document Analysis and Recognition, ICDAR\u201995, Montreal, QC, Canada."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/S0004-3702(01)00129-1","article-title":"Deep Blue","volume":"134","author":"Campbell","year":"2002","journal-title":"Artif. Intell."},{"key":"ref_45","unstructured":"Le, Q.V., Ranzato, M., Monga, R., Devin, M., Chen, K., Corrado, G.S., Dean, J., and Ng, A.Y. (July, January 26). Building High-level Features Using Large Scale Unsupervised Learning. Proceedings of the 29th International Coference on International Conference on Machine Learning, ICML\u201912, Edinburgh, UK."},{"key":"ref_46","first-page":"1097","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"Volume 1","author":"Krizhevsky","year":"2012","journal-title":"Proceedings of the 25th International Conference on Neural Information Processing Systems, NIPS\u201912"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014, January 23\u201328). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR\u201914, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F.F. (2009, January 20\u201325). ImageNet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_51","unstructured":"Collobert, R., Bengio, S., and Mari\u00e9thoz, J. (2002). Torch: A modular machine learning software library. Technical Report IDIAP-RR 02-46, IDIAP."},{"key":"ref_52","unstructured":"Mani, I., and Maybury, M.T. (1999). Advances in Automatic Text Summarization, MIT Press."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/2.781637","article-title":"Chameleon: Hierarchical clustering using dynamic modeling","volume":"32","author":"Karypis","year":"1999","journal-title":"Computer"}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/1\/21\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:23:49Z","timestamp":1760135029000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/1\/21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,21]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["bdcc6010021"],"URL":"https:\/\/doi.org\/10.3390\/bdcc6010021","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2022,2,21]]}}}