Abstract
Data collection and data management research is a multidisciplinary field that covers a wide range of subjects, each of which serves as a fundamental prerequisite for Human Dynamics Research utilizing Social Media/Big Data. The ever-changing academic landscape of this field has been characterized by rapid expansion of various applications and dynamic collaboration across multiple disciplines, yielding an increasing number of publications. This chapter reviews the research trend of such techniques and methodologies over the past ten years (2010–2019). Specifically, we conducted Bibliometric analysis to examine growth of output during 2010–2019, distribution of output in subject categories and journals, most cited documents, geographic and institutional distribution of publications, institution collaboration network, and keywords. The keyword analysis reveals that “big data”, “social media”, “data collection”, “Twitter”, “Facebook”, and “privacy”, were popular throughout the past 10 years. Additional keywords such as “data management”, “cloud computing”, “machine learning”, “data mining”, “Internet of Things”, “big data analytics”, “crowdsourcing”, “data analytics”, “data science”, “big data management”, “Hadoop”, “MapReduce”, “sentiment analysis”, “surveillance”, “business intelligence”, and “IoT” have attracted increasing attention, further reflecting research trends.
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Notes
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A spatial trajectory is a trace indicating a moving vehicle or induvial in geographical spaces.
References
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11, 959–975.
Bao, J., Zheng, Y., & Mokbel, M. F. (2012). Location-based and Preference-aware Recommendation Using Sparse Geo-social Networking Data. In International Conference on Advances in Geographic Information Systems (pp. 199–208). ACM.
Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for windows: Software for social network analysis. Harvard, MA: Analytic Technologies, 6.
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the Association for Information Science and Technology, 62, 1382–1402.
Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51, 107–113.
Depoux, A., Martin, S., Karafillakis, E., Preet, R., Wilder-Smith, A., & Larson, H. (2020). The pandemic of social media panic travels faster than the COVID-19 outbreak. Journal of Travel Medicine. https://doi.org/10.1093/jtm/taaa031
Ding, Y., Chowdhury, G. G., & Foo, S. (2001). Bibliometric cartography of information retrieval research by using co-word analysis. Information Processing and Management, 37, 817–842.
Garfield, E. (1970). Citation indexing for studying science. Nature, 227, 669–671.
Glänzel, W., & Schubert, A. (2005). Analysing scientific networks through co-authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). The Use of Publication and Patent Statistics in Studies of S&T Systems.
Gong, J., Li, S., Ye, X., & Peng, Q. (2020). Measuring the dynamic impact of high-speed railways on urban interactions in China. ArXiv201008182 Cs
He, Y., & Cheung Hui, S. (2002). Mining a web citation database for author co-citation analysis. Information Processing and Management, 38, 491–508.
Hussain, A., & Vatrapu, R. (2014). Social data analytics tool: Design, development, and demonstrative case studies. In 2014 IEEE 18th International Enterprise Distributed Object Computing Conference Workshops and Demonstrations (pp. 414–417).
Karamshuk, D., Noulas, A., Scellato, S., Nicosia, V., & Mascolo, C. (2013). Geo-spotting: Mining online location-based services for optimal retail store placement. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 793–801).
Li, Q., Wei, W., Xiong, N., Feng, D., Ye, X., & Jiang, Y. (2017). Social media research, human behavior, and sustainable society. Sustainability, 9, 384.
Mayr, P., & Weller, K. (2017). Think before you collect: Setting up a data collection approach for social media studies. The SAGE Handbook of Social Media Research Methods, 679.
Philip Chen, C. L., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314–347.
Pritchard, A. (1969). Statistical bibliography or bibliometrics. J Doc, 25, 348–349.
Schmoch, U., & Schubert, T. (2007). Are international co-publications an indicator for quality of scientific research? Scientometrics, 74, 361–377.
Shaw, S.-L., Tsou, M.-H., & Ye, X. (2016). Editorial: Human dynamics in the mobile and big data era. International Journal of Geographical Information Science, 30, 1687–1693.
Thorson, K., Driscoll, K., Ekdale, B., Edgerly, S., Thompson, L. G., Schrock, A., Swartz, L., Vraga, E. K., & Wells, C. (2013). Youtube, Twitter and the occupy movement. Information, Communication & Society, 16, 421–451.
Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84, 523–538.
Wang, Z., Lam, N. S. N., Obradovich, N., & Ye, X. (2019). Are vulnerable communities digitally left behind in social responses to natural disasters? An evidence from Hurricane Sandy with Twitter data. Applied Geography, 108, 1–8.
Wang, Z., & Ye, X. (2018). Social media analytics for natural disaster management. International Journal of Geographical Information Science, 32, 49–72.
Ye, X., She, B., Li, W., Kudva, S., & Benya, S. (2020a). What and where are we tweeting about black friday? In R. R. Thakur, A. K. Dutt, S. K. Thakur, & G. M. Pomeroy (Eds.), Urban and regional planning and development. 20th century forms 21st century transform (pp. 173–186). Springer International Publishing.
Ye, X., Li, S., & Peng, Q. (2021). Measuring interaction among cities in China: A geographical awareness approach with social media data. Cities, 109, 103041.
Ye, X., Zhao, B., Nguyen, T. H., & Wang, S. (2020b). Social media and social awareness. In Manual of digital earth (pp. 425–440). Springer.
Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: Concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology, 5, 38:1–38:55.
Acknowledgments
This material is partially based upon work supported by the National Science Foundation under Grant Nos. 1739491 and 1937908. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
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Peng, Q., Ye, X. (2021). Research Trends in Social Media/Big Data with the Emphasis on Data Collection and Data Management: A Bibliometric Analysis. In: Nara, A., Tsou, MH. (eds) Empowering Human Dynamics Research with Social Media and Geospatial Data Analytics. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-83010-6_4
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