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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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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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