How to Identify the Emerging Topics of a Research Topic?
Keywords:Emerging Topics, Bibliographic Research, Bibliometric, Co-occurrence Network, Learning Assessment
The words needed to create a co-occurrence analysis can be collected from the author's article titles, abstracts and keywords. These different approaches allow identifying sub-areas in each field and studying their characteristics and trends, portraying the global research profile, finding important topics, disruptive trends, looking for cooperative relationships and interpreting patterns of collaboration between the authors. This work has the objective of presenting two approaches for the interpretation of the results of a bibliographic research. The first approach aims to identify the degree of evolution of publications on a given topic using the logistic curve. The second aims to identify emerging topics from the analysis of keyword co-occurrence. To exemplify the two approaches, a survey was carried out on the Scopus database on the subject of learning assessment. The analysis of the evolution of publications revealed that the topic is still growing and is expected to reach saturation around 2040. The identification of emerging topics indicated 20 keywords that were indexed in 2021. It is concluded that the approaches are relevant to analyze the evolution of a theme and to indicate opportunities to explore emerging topics.
Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An Open Source Software for Exploring and Manipulating Networks. In: Proceedings of the Third International ICWSM Conference, 361-362.
Bautista-Bernal, I., Quintana-García, C., & Marchante-Lara, M. (2021). Research trends in occupational health and social responsibility: A bibliometric analysis. Safety Science, 137, Art. 105167.
Burg, D., Schachter, E., Meyer, P., & Ausubel, J. (2017). Loglet Lab (Version 4.0) [Software]. Disponível em: http://logletlab.com. Acesso em: 05Mar2020.
Chan, C. K. Y., & Lee, K. K. W. (2021). Reflection literacy: A multilevel perspective on the challenges of using reflections in higher education through a comprehensive literature review. Educational Research Review, 32, Art. 100376.
Chen, Y. H., Chen, C. Y., & Lee, S. C. (2010). Technology forecasting of new clean energy: The example of hydrogen energy and fuel cell. African Journal of Business Management, 4 (7), 1372–1380.
Choi, J., Yi, S., & Lee, K. C. (2011). Analysis of keyword networks in MIS research and implications for predicting knowledge evolution. Information & Management, 48(8), 371–381.
Daim, T. U., Rueda, G. R., & Martin, H. T. (2005). Technology forecasting using bibliometric analysis and system dynamics. In Technology management: In a unifying discipline for melting the boundaries - IEEE, 112–122).
Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing & Management, 37(6), 817-842.
Dotsika, F., & Watkins, A. (2017). Identifying potentially disruptive trends by means of keyword network analysis. Technological Forecasting and Social Change, 119, 114–127.
Ernst, H. (1997). The use of patent data for technological forecasting: the diffusion of CNC - technology in the machine tool industry. Small Business Economics, 9 (4), 361–381.
Gan, C., & Wang, W. (2015). Research characteristics and status on social media in China: A bibliometric and co-word analysis. Scientometrics, 105(2), 1167–1182.
Khaldi, H., & Prado-Gascó, V. (2021). Bibliometric maps and co-word analysis of the literature on international cooperation on migration. Quality & Quantity, Article in Press.
Khasseh, A. A., Soheili, F., Moghaddam, H.S., & Chelak, A .M. (2017). Intellectual structure of knowledge in iMetrics: a co-word analysis. Information Processing & Management, 53 (3), 705–720.
Kucharavy, D.; De Guio, R. (2011). Application of S-shaped curves. Procedia Engineering, 9, 559-572.
Lang, Z., Liu, H., Meng, N., Wang, H., Wang, H., & Kong, F. (2021). Mapping the knowledge domains of research on fire safety – an informetrics analysis. Tunnelling and Underground Space Technology, 108, Art. 103676.
Lee, T. S., Lee, Y. S., Lee, J., & Chang, B. C. (2018). Analysis of the intellectual structure of human space exploration research using a bibliometric approach: Focus on human related factors. Acta Astronautica, 143, 169–182.
Lezama-Nicolás, R., Rodríguez-Salvador, M., Río-Belver, R., & Bildosola, I. (2018). A bibliometric method for assessing technological maturity: the case of additive manufacturing. Scientometrics 117, 1425–1452.
Liu, G.-Y., Hu, J.-M., & Wang, H.-L. (2012) A co-word analysis of digital library field in China. Scientometrics, 91 (1), 203–217.
Liu, J. S., Lu, L. Y. Y., & Lu, W. M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33–45.
Lv, P. H., Wang, G.-F., Wan, Y., Liu, J., Liu, Q., & Ma, F. (2011). Bibliometric trend analysis on global graphene research. Scientometrics, 88(2), 399–419.
Milojevi?, S., Sugimoto, C. S., Yan, E., & Ding, Y. (2011). The cognitive structure of library and information science: Analysis of article title words. Journal of the American Society for Information Science and Technology, 62(10), 1933–1953.
Newman, M. E. J. (2001a). Scientific collaboration networks: I. Network construction and fundamental results. Physical Review E, 64, 016131.
Newman, M. E. J. (2001b). Scientific collaboration networks: II. Shortest paths, weighted networks, and centrality. Physical Review E, 64, 016132.
Olczyk, M. (2016). Bibliometric approach to tracking the concept of international competitiveness, Journal of Business Economics & Management, 17 (6), 945–959.
Pech, M., Vrchota, J., & Bedná?, J. (2021). Predictive Maintenance and Intelligent Sensors in Smart Factory: Review. Sensors, 21(4), Art. 1470, 1-40.
Prabhakaran, T., Lathabai, H. H., & Changat, M. (2015). Detection of paradigm shifts and emerging fields using scientific network: A case study of Information Technology for Engineering. Technological Forecasting and Social Change, 91, 124-145.
Ronda-Pupo, G. A., Guerras-Martin, L. A. (2012). Dynamics of the evolution of the strategy concept 1962–2008: a co-word analysis. Strategic Management Journal, 33 (2), 162–188.
Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In: Ding, Y., Rousseau, R., & Wolfram, D. (Eds.). Measuring scholarly impact: methods and practice. New York: Springer.
Van Eck, N. J., & Waltman, L. (2021). VOSviewer manual. Leiden: Universiteit Leiden.
Wang, Z. Y., Li, G., Li, C.-Y., & Li, A. (2012). Research on the semantic-based co-word analysis. Scientometrics, 90 (3), 855–875.
Wang, H., Wang, C., & Wu, F. (2020). How Multimedia Influence Group Interaction in STEM Education An Epistemic Network Analysis for Online Synchronous Collaborative Learning. In 2020 International Symposium on Educational Technology (ISET), pp. 303-306.
Xu, X., Wang, W., Liu, Y., Zhao, X., Xu, Z., & Zhou, H. (2016). A bibliographic analysis and collaboration patterns of IEEE transactions on intelligent transportation systems between 2000 and 2015. In IEEE Transactions on Intelligent Transportation Systems, 17(8), 2238–2247.
yWorks. (2021). yEd Graph Editor Manual. Disponível em: <https://yed.yworks.com/support/manual/index.html>. Acesso em: 26/02/2021.
Zhao, W., Mao, J., & Lu, K. (2018). Ranking themes on co-word networks: Exploring the relationships among different metrics. Information Processing and Management, 54(2), 203–218.
Zheng, L., Zhong, L. & Niu, J. (2021). Effects of personalised feedback approach on knowledge building, emotions, co-regulated behavioural patterns and cognitive load in online collaborative learning. Assessment & Evaluation in Higher Education, Article in Press.
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