Abstract
With the development of social interaction techniques and social tagging mechanisms, online academic community as a new platform has greatly changed the way users organize and share knowledge. The large amount of social tagging data occurred on online academic community provides us a channel to systematically understand users’ tagging behavior. Based on data collected from a specific online academic community, this research first classifies users into two categories: active and inactive users. After that, growth models (damped exponential model, normal model and fluctuating model) are employed to investigate tagging behavior for both active and inactive users. Factors that might influence the likelihood of the growth models are also identified based on multinomial logistic regression. This research expands our understanding on users’ tagging behavior and factors that may affect their tagging behavior in the context of online academic community.
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References
Cattuto, C. (2006). Semiotic dynamics in online social communities. European Physical Journal C-Particles and Fields, 46, 33–37.
Cattuto, C., Baldassarri, A., Servedio, V. D. P., & Loreto, V. (2007). "Vocabulary growth in collaborative tagging systems". arXiv preprint arXiv:0704.3316.
Chen, Y.-N., & Ke, H.-R. (2013). An analysis of users' behavior patterns in the organization of information: A case study of CiteULike. Online Information Review, 37(4), 638–656.
Choi, Y., & Syn, S. Y. (2016). Characteristics of tagging behavior in digitized humanities online collections. Journal of the Association for Information Science and Technology, 67(5), 1089–1104.
Dhir, A., Kaur, P., & Rajala, R. (2018). Why do young people tag photos on social networking sites?. Explaining user intentions. International Journal of Information Management, 38(1), 117–127.
Farooq, U., Kannampallil, T. G., Song, Y., Ganoe, C. H., Carroll, J. M., & Giles, L. (2007). Evaluating tagging behavior in social bookmarking systems: Metrics and design heuristics. In Proceedings of the 2007 international ACM conference on Supporting group work (pp. 351–360).
Figueiredo, F., Pinto, H., BeléM, F., Almeida, J., GonçAlves, M., Fernandes, D., & Moura, E. (2013). Assessing the quality of textual features in social media. Information Processing & Management, 49(1), 222–247.
Golbeck, J., Koepfler, J., & Emmerling, B. (2011). An experimental study of social tagging behavior and image content. Journal of the Association for Information Science and Technology, 62(9), 1750–1760.
Golder, S., & Huberman, B. A. (2005). The structure of collaborative tagging systems. Journal of Information Science, 32(2).
Gupta, M., Li, R., Yin, Z., & Han, J. (2011). An overview of social tagging and applications. In C. Aggarwal (Ed.), Social network data analytics. Boston: Springer.
Halpin, H., Robu, V., & Shepherd, H. (2007). The complex dynamics of collaborative tagging. In Proceedings of the 16th international conference on World Wide Web (pp. 211–220). ACM.
Hammond, T.,Lund, B., Flack, M., Hannay, T., & NeoReality, I. (2005). "Social bookmarking tools (II)". D-Lib magazine, Vol.11 No.4, pp.1082–9873.
Huang, C. L., Yeh, P. H., Lin, C. W., & Wu, D. C. (2014). Utilizing user tag-based interests in recommender systems for social resource sharing websites. Knowledge-Based Systems, 56, 86–96.
Klašnja-Milićević, A., Ivanović, M., Vesin, B., & Budimac, Z. (2018). Enhancing e-learning systems with personalized recommendation based on collaborative tagging techniques. Applied Intelligence, 48(6), 1519–1535.
Lee, C. (2018). Introduction: Discourse of social tagging (Vol. 22, pp. 1–3). Discourse context & Media.
Lee, T. Q., Yeh, C. W., & Fang, C. C. (2014). Bayesian software reliability prediction based on Yamada delayed S-shaped model. Applied Mechanics and Materials, 490-491, 1267–1278.
Li, X., Thelwall, M., & Giustini, D. (2011). Validating online reference managers for scholarly impact measurement. Scientometrics, 91(2), 461–471.
Lin, Y. l., Trattner, C., Brusilovsky, P., & He, D. (2015). The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags. Journal of the Association for Information Science and Technology, 66(9), 1785–1798.
Ma, J. (2012). The sustainability and stabilization of tag vocabulary in CiteULike: An empirical study of collaborative tagging. Online Information Review, 36(5), 655–674.
Marlow, C., Naaman, M., Boyd, D., & Davis, M. (2006). "’". Seventeenth Conference on Hypertext and Hypermedia, pp.31–40.
Mezghani, M., Peninou, A., Zayani, C. A., Amous, I., & Sedes, F. (2017). Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information. Data & Knowledge Engineering, 108, 15–29.
Montañés, E., Ramón Quevedo, J., Díaz, I., Cortina, R., Alonso, P., & Ranilla, J. (2010). TagRanker: Learning to recommend ranked tags. Logic Journal of IGPL, 19(2), 395–404.
Pan, X., He, S., Zhu, X., & Fu, Q. (2016). How users employ various popular tags to annotate resources in social tagging: An empirical study. Journal of the Association for Information Science and Technology, 67(5), 1121–1137.
Pitsilis, G., & Wang, W. (2015). Harnessing the power of social bookmarking for improving tag-based recommendations. Computers in Human Behavior, 50, 239–251.
Redden, C. S. (2010). Social bookmarking in academic libraries: Trends and applications. The Journal of Academic Librarianship, 36(3), 219–227.
Robu, V., Halpin, H., & Shepherd, H. (2009). Emergence of consensus and shared vocabularies in collaborative tagging systems. ACM Transactions on the Web, 3(4), 1–34.
Sa, N., & Yuan, X. (2013). What motivates people use social tagging. In In international conference on online communities and social Computing (pp. 86–93).
Santos-Neto, E., Condon, D., Andrade, N., Iamnitchi, A., & Ripeanu, M. (2013). "Reuse, temporal dynamics, interest sharing, and collaboration in social tagging systems". arXiv preprint arXiv, Vol.13 No.01, pp.61–91.
Simon, H. A. (1955). On a class of skew distribution functions. Biometrika, 42, 425–440.
Sun, X., & Lin, H. (2013). Topical community detection from mining user tagging behavior and interest. Journal of the Association for Information Science and Technology, 64(2), 321–333.
Wang, Y., Wang, S., Tang, J., Qi, G. J., Liu, H., & Li, B. (2017). CLARE: A joint approach to label classification and tag recommendation. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp. 210–216.
Wei J., Zhao, D., & Liang L. (2009). Estimating the growth models of news stories on disasters. Journal of the American Society for Information Science and Technology, 60(9), 1741–1755.
Wladis, C., Conway, K., & Hachey, A. C. (2017). Using course-level factors as predictors of online course outcomes: A multi-level analysis at a US urban community college. Studies in Higher Education, 42(1), 184–200.
Wu, L. F. (2011). The accelerating growth of online tagging systems. The European Physical Journal B-Condensed Matter and Complex Systems, 83(2), 283–287.
Xia, Z., Wang, X., Sun, X., & Wang, Q. (2016). A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. IEEE Transactions on Parallel and Distributed Systems, 27(2), 340–352.
Xu, C., Ma, B., Chen, X., & Ma, F. (2013). Social tagging in the scholarly world. Journal of the Association for Information Science and Technology, 64(10), 2045–2057.
Yuan, K., Bo, F., & Zhang, W. (2016). Semi-varying coefficient multinomial logistic regression for disease progression risk prediction. Statistics in Medicine, 35(26), 4764–4778.
Zhang, Y., Zhang, B., Gao, K., Guo, P., & Sun, D. (2012). Combining content and relation analysis for recommendation in social tagging systems. Physical A: Statistical Mechanics and its Applications, 391(22), 5759–5768.
Zhou, D., Bian, J., Zheng, S., Zha, H., & Giles, C. L. (2008, April). Exploring social annotations for information retrieval. In In proceedings of the 17th international conference on World Wide Web (Vol. 715-724).
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This research was partially supported by the National Natural Science Foundation of China (71861019, 71361017, 71640021).
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Xu, Y., Yin, D. & Zhou, D. Investigating Users’ Tagging Behavior in Online Academic Community Based on Growth Model: Difference between Active and Inactive Users. Inf Syst Front 21, 761–772 (2019). https://doi.org/10.1007/s10796-018-9891-2
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DOI: https://doi.org/10.1007/s10796-018-9891-2