Abstract
In recent years, the rapid development of academic social networks has greatly promoted academic exchanges and scientific research collaborations among users. At the same time, with the various social behaviors of many users, massive log text data are accumulated on academic social networks. In this regard, in order to extract the available information from the massive log text data, this paper takes the case of implicit interactive information generated by scholars on SCHOLAT (https://www.scholat.com) as a research, and analyzes the user behavior in the past year based on the user’s spatiotemporal behavior characteristics and preference behavior characteristics. Thus, ScholatAna, a framework based on big data technology for Academic Social Networking (ASN) is proposed. Considering that the information generated by users is up to tens of millions of chaotic log files. Therefore, this paper combines distributed computing methods, uses Hadoop ecosystem technology to extract available data, and uses TF-IDF and social collaborative filtering algorithms to perform faster and more accurate statistics and analysis of data. The experimental results are demonstrated and evaluated by using visual analysis techniques. To a certain extent, these results revealing the user's behavior trends and regulars in the domain of academic social, which affects the development of scientific research.
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This work was supported by National Natural Science Foundation of China under grant number U1811263, by National Natural Science Foundation of China under grant number 61772211.
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Ma, W., Lin, R., Li, J., Mao, C., Xu, Q., Wen, A. (2021). ScholatAna: Big Data-Based Academic Social Network User Behavior Preference System. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_52
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DOI: https://doi.org/10.1007/978-981-16-2540-4_52
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