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Mining research trends with anomaly detection models: the case of social computing research

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Abstract

We proposed in this study to use anomaly detection models to discover research trends. The application was illustrated by applying a rule-based anomaly detector (WSARE), which was typically used for biosurveillance purpose, in the research trend analysis in social computing research. Based on articles collected from SCI-EXPANDED and CPCI-S databases during 2000 to 2013, we found that the number of social computing studies went up significantly in the past decade, with computer science and engineering among the top important subjects. Followed by China, USA was the largest contributor for studies in this field. According to anomaly detected by the WSARE, social computing research gradually shifted from its traditional fields such as computer science and engineering, to the fields of medical and health, and communication, etc. There was an emerging of various new subjects in recent years, including sentimental analysis, crowdsourcing and e-health. We applied an interdisciplinary network evolution analysis to track changes in interdisciplinary collaboration, and found that most subject categories closely collaborate with subjects of computer science and engineering. Our study revealed that, anomaly detection models had high potentials in mining hidden research trends and may provided useful tools in the study of forecasting in other fields.

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References

  • Abuadlla, Y., Kvascev, G., Gajin, S., & Jovanovic, Z. (2014). Flow-based anomaly intrusion detection system using two neural network stages. Computer Science and Information Systems, 11(2), 601–622.

    Article  Google Scholar 

  • Agarwal, N. (2011). Collective learning: An integrated use of social media in learning environment. In Social media tools and platforms in learning environments (pp. 37–51). Springer, Berlin.

  • Antheunis, M. L., Tates, K., & Nieboer, T. E. (2013). Patients’ and health professionals’ use of social media in health care: Motives, barriers and expectations. Patient Education and Counseling, 92(3), 426–431.

    Article  Google Scholar 

  • Basu, S., & Meckesheimer, M. (2007). Automatic outlier detection for time series: An application to sensor data. Knowledge and Information Systems, 11(2), 137–154.

    Article  Google Scholar 

  • Brabham, D. C., Ribisl, K. M., Kirchner, T. R., & Bernhardt, J. M. (2014). Crowdsourcing applications for public health. American Journal of Preventive Medicine, 46(2), 179–187.

    Article  Google Scholar 

  • Carter, R. R., DiFeo, A., Bogie, K., Zhang, G.-Q., & Sun, J. (2014). Crowdsourcing awareness: Exploration of the ovarian cancer knowledge gap through Amazon Mechanical Turk. PLOS ONE, 9(1). doi:10.1371/journal.pone.0085508

  • Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41(3), 1–72.

    Article  Google Scholar 

  • Chen, Z., Yue, W., Shi, J., & Bu, X. (2011). A multi-agent based social computing collaboration selection approach in stable states. Journal of Computational Information Systems, 7(16), 5785–5790.

    Google Scholar 

  • Cruz-Cunla, M. M., Tavares, A. J., & Simoes, R. (2010). Handbook of research on developments in e-health and telemedicine: Technological and social perspectives. Hershey: Medical Information Science Reference.

    Book  Google Scholar 

  • Dasgupta, D., Yu, S., & Majumdar, N. (2005). MILA—Multilevel immune learning algorithm and its application to anomaly detection. Soft Computing, 9(3), 172–184.

    Article  MATH  Google Scholar 

  • Faggiani, A., Gregori, E., Lenzini, L., Luoni, V., & Vecchio, A. (2014). Smartphone-based crowdsourcing for network monitoring: Opportunities, challenges, and a case study. IEEE Communications Magazine, 52(1), 106–113.

    Article  Google Scholar 

  • Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82–89.

    Article  Google Scholar 

  • Fiore, U., Palmieri, F., Castiglione, A., & De Santis, A. (2013). Network anomaly detection with the restricted Boltzmann machine. Neurocomputing, 122, 13–23.

    Article  Google Scholar 

  • Fu, J.-Y., Zhang, X., Zhao, Y.-H., Chen, D.-Z., & Huang, M.-H. (2012). Global performance of traditional Chinese medicine over three decades. Scientometrics, 90(3), 945–958.

    Article  Google Scholar 

  • Garfield, E. (2004). Historiographic mapping of knowledge domains literature. Journal of Information Science, 30(2), 119–145.

    Article  Google Scholar 

  • Glanzel, W. (2000). Science in Scandinavia: A bibliometric approach. Scientometrics, 48(2), 121–150.

    Article  Google Scholar 

  • Glanzel, W. (2013). High-end performance or outlier? Evaluating the tail of scientometric distributions. Scientometrics, 97(1), 13–23.

    Article  Google Scholar 

  • Glanzel, W. & Moed, H. F. (2013). Opinion paper: Thoughts and facts on bibliometric indicators. Scientometrics, 96(1), 381–394.

    Article  Google Scholar 

  • Grubbs, F. E. (1969). Procedures for detecting outlying observations in samples. Technometrics, 11, 1–21.

    Article  Google Scholar 

  • Havre, S., Hetzler, E., Whitney, P., & Nowell, L. (2002). ThemeRiver: Visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 8(1), 9–20.

    Article  Google Scholar 

  • Hoonlor, A., Szymanski, B. K., & Zaki, M. J. (2013). Trends in computer science research. Communications of the ACM, 56(10), 74–83.

    Article  Google Scholar 

  • Howe, J. (2006). The rise of crowdsourcing. Wired Magazine, 14(6), 1–4.

    MathSciNet  Google Scholar 

  • Hu, Y., Sun, J., Li, W., & Pan, Y. (2014). A scientometric study of global electric vehicle research. Scientometrics, 98(2), 1269–1282.

    Article  Google Scholar 

  • Kademani, B. S., Sagar, A., Surwase, G., & Bhanumurthy, K. (2013). Publication trends in materials science: A global perspective. Scientometrics, 94(3), 1275–1295.

    Article  Google Scholar 

  • Kierkegaard, P. (2013). eHealth in Denmark: A case study. Journal of Medical Systems, 37(6). doi:10.1007/s10916-013-9991-y

  • King, I., Li, J., & Chan, K. T. (2009). A brief survey of computational approaches in social computing. In IEEE international joint conference on neural networks (IJCNN) (pp. 2699–2706).

  • Lai, K., & Wu, S. (2005). Using the patent co-citation approach to establish a new patent classification system. Information Processing & Management, 41(2), 313–330.

    Article  Google Scholar 

  • Laurikkala, J., Juhola, M., & Kentala, E. (2000). Informal identification of outliers in medical data. Berlin.

  • Liu, X., Zhang, L., & Hong, S. (2011). Global biodiversity research during 1900–2009: A bibliometric analysis. Biodiversity and Conservation, 20(4), 807–826.

    Article  Google Scholar 

  • Liu, X., Zhan, F. B., Hong, S., Niu, B., & Liu, Y. (2012). A bibliometric study of earthquake research: 1900–2010. Scientometrics, 92(3), 747–765.

    Article  Google Scholar 

  • Lugano, G. (2012). Social computing: A classification of existing paradigms. In Proceedings—2012 ASE/IEEE international conference on privacy, security, risk and trust and 2012 ASE/IEEE international conference on social computing, SocialCom/PASSAT 2012 (pp. 377–382). Amsterdam.

  • Niu, Z., Shi, S., Sun, J., & He, X. (2011). A Survey of outlier detection methodologies and their applications. In Artificial intelligence and computational intelligence, volume 7002 of lecture notes in artificial intelligence (pp. 380–387).

  • Palmieri, F., & Fiore, U. (2010). Network anomaly detection through nonlinear analysis. Computers & Security, 29(7), 737–755.

    Article  Google Scholar 

  • Palmieri, F., Fiore, U., & Castiglione, A. (2014). A distributed approach to network anomaly detection based on independent component analysis. Concurrency and COmputation—Practice & Experience, 26(5), 1113–1129. doi:10.1002/cpe.3061

    Article  Google Scholar 

  • Parameswaran, M., & Whinston, A. B. (2007). Research issues in social computing. Journal of the Association for Information Systems, 8(6), 336–350.

    Google Scholar 

  • Pascu, C. (2008). An empirical analysis of the creation, use and adoption of social computing applications. Technical report, Institute for Prospective Technological Studies.

  • Prathap, G. (2014). Single parameter indices and bibliometric outliers. Scientometrics, 101(3), 1781–1787.

    Article  Google Scholar 

  • Ricci, F., Rokach, L., & Shapira, B. (2011). Introduction to recommender systems handbook. In Recommender systems handbook, (pp. 1–35). Springer, New York.

  • Shoemaker, L., & Hall, L. (2011). Anomaly detection using ensembles. Multiple classifier systems. Volume 6713 of lecture notes in computer science (pp. 6–15). Springer, Berlin.

  • Shuai, X., Pepe, A., & Bollen, J. (2012). How the scientific community reacts to newly submitted preprints: Article downloads, twitter mentions, and citations. PLoS ONE, 7(11), doi:10.1371/journal.pone.0047523

  • Sinha, B. (2012). Global biopesticide research trends: A bibliometric assessment. Indian Journal of Agricultural Sciences, 82(2), 95–101.

    Google Scholar 

  • SocialCom. (2011). http://www.asesite.org/conferences/socialcom/2011/. Accessed 2011.

  • Srivastava, A. N., & Zane-Ulman, B. (2005). Discovering recurring anomalies in text reports regarding complex space systems. In 2005 IEEE aerospace conference (vols. 1–4, pp. 3853–3862).

  • Ucar, I., Lopez-Fernandino, F., Rodriguez-Ulibarri, P., Sesma-Sanchez, L., Urrea-Mico, V., & Sevilla, J. (2014). Growth in the number of references in engineering journal papers during the 1972–2013 period. Scientometrics, 98(3), 1855–1864.

    Article  Google Scholar 

  • Wang, F.-Y., Zeng, D., Carley, K. M., & Mao, W. (2007). Social computing: From social informatics to social intelligence. IEEE Intelligent Systems, 22(2), 79–83.

    Article  Google Scholar 

  • Wang, H., He, Q., Liu, X., Zhuang, Y., & Hong, S. (2012). Global urbanization research from 1991 to 2009: A systematic research review. Landscape and Urban Planning, 104(3–4), 299–309.

    Article  Google Scholar 

  • Wang, M.-H., Li, J., & Ho, Y.-S. (2011). Research articles published in water resources journals: A bibliometric analysis. Desalination and Water Treatment, 28(1–3), 353–365.

    Article  Google Scholar 

  • Wang, T., Liu, Z., Xiu, B., Mo, H., & Zhang, Q. (2014). Characterizing the evolution of social computing research. IEEE Intelligent Systems, 29(5), 48–56.

    Article  MATH  Google Scholar 

  • Wang, W., Guyet, T., Quiniou, R., Cordier, M.-O., Masseglia, F., & Zhang, X. (2014). Autonomic intrusion detection: Adaptively detecting anomalies over unlabeled audit data streams in computer networksy. Knowledge-based system, 70, 103–117.

    Article  Google Scholar 

  • Wang, X., Wang, Z., & Xu, S. (2013). Tracing scientist’s research trends realtimely. Scientometrics, 95(2), 717–729.

    Article  Google Scholar 

  • Wiki. (2014). http://en.wikipedia.org/wiki/social_computing. Accessed 2014.

  • Wong, W., Moore, A., Cooper, G., & Wagner, M. (2002) Rule-based anomaly pattern detection for detecting disease outbreaks. In: Eighteenth national conference on artificial intelligence (AAAI-02)/fourteenth innovative applications of artificial intelligence conference (IAAI-02), proceedings (pp. 217–223).

  • Wong, W., Moore, A., Cooper, G., & Wagner, M. (2005). What’s strange about recent events (WSARE): An algorithm for the early detection of disease outbreaks. Journal of Machine Learning Research, 6, 1961–1998.

    MATH  MathSciNet  Google Scholar 

  • xsimilarity. (2014). https://code.google.com/p/xsimilarity/w/list. Accessed 2014.

  • Xu, Y., Luo, T., & He, H. (2010). Social computing research map. In Proceedings - 2010 IEEE 2nd symposium on web society, SWS 2010 (pp. 158–164). Beijing.

  • Zhang, L., Wang, M.-H., Hu, J., & Ho, Y.-S. (2010). A review of published wetland research, 1991–2008: Ecological engineering and ecosystem restoration. Ecological Engineering, 36(8), 973–980.

    Article  Google Scholar 

  • Zhao, L., & Zhang, Q. (2011). Mapping knowledge domains of Chinese digital library research output, 1994–2010. Scientometrics, 89(1), 51–87.

    Article  Google Scholar 

  • Zhuang, Y., Liu, X., Nguyen, T., He, Q., & Hong, S. (2013). Global remote sensing research trends during 1991–2010: A bibliometric analysis. Scientometrics, 96(1), 203–219.

    Article  Google Scholar 

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Acknowledgments

This research work was supported by the National Natural Science Foundation of China (No. 71301165, 91024030) and Hunan Provincial Innovation Foundation For Postgraduate under Grant No. CX2013B024. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Qing Cheng, Zhong Liu & Jincai Huang

  2. College of Information System and Management, National University of Defense Technology, Changsha, 410073, People’s Republic of China

    Xin Lu

  3. Flowminder Foundation, 17177, Stockholm, Sweden

    Xin Lu

  4. Department of Public Health Sciences, Karolinska Institutet, 17177, Stockholm, Sweden

    Xin Lu

  5. Division of Infectious Disease, Key Laboratory of Surveillance and Early-Warning on Infectious Disease, Chinese Centre for Disease Control and Prevention, Beijing, 102206, People’s Republic of China

    Xin Lu

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  1. Qing Cheng
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Correspondence to Qing Cheng.

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Cheng, Q., Lu, X., Liu, Z. et al. Mining research trends with anomaly detection models: the case of social computing research. Scientometrics 103, 453–469 (2015). https://doi.org/10.1007/s11192-015-1559-9

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