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Big Data in China: Bigger Problems, Easier Solutions or Both?

Year 2019, Volume: 3 Issue: 1, 101 - 120, 01.06.2019

Abstract

Just like the case in any other power centers of the world, such as U.S., Europe and Russia, big data in China have information-wise polarized the researchers. On the one hand, some have been excited about the opportunities big data will bring to scientific research (e.g. new discoveries already made in medical research); on the other, some others are worried about increased level of privacy violations. Possibly because of the difficulties in self-expression within the political context of China, other more detailed objections to the use of big data by governments and corporations are rarely discussed with regard to China. While Chinese government implements the social credit system which is based on the big data of each citizen collected by any surveillance device –unethical ones included, so far this system has not been discussed extensively within academic circles. However, such a system can be a model for other countries and become a global nightmare rather than a Chinese one only. This social credit system is closely related with the notion of Internet of Things which appears to be benign at first blush, but has the potential for misuse and abuse in the hands of authoritarian governments and greedy corporations. The lines between public and private will be blurred by the advent of Internet of Things which will make any electronic device capable of transferring data to the Big Brother.

In this article, big data discussions, both pros and cons are presented and other themes that are understudied in China are also listed.  

References

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  • Cao, X., & Wang, Y. (2016). An Approach to Granting Subsidies to College Students in China Using Big Data. Journal of Education and Practice, 7(26), 1-4.
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  • Joh, E. E. (2016). The new surveillance discretion: Automated suspicion, big data, and policing. Harvard Law & Policy Review, 10, 15-42.
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  • Lammers, J., & Burgmer, P. (2018). Power increases the self‐serving bias in the attribution of collective successes and failures. European Journal of Social Psychology.
  • Laterza, V. (2018). Cambridge Analytica, independent research and the national interest. Anthropology Today, 34(3), 1-2.
  • Li, N., Brossard, D., Scheufele, D. A., Wilson, P. H., & Rose, K. M. (2018). Communicating data: interactive infographics, scientific data and credibility. Journal of Science Communication, 17(2), A06.
  • Lieder, F., Griffiths, T. L., Huys, Q. J., & Goodman, N. D. (2018). The anchoring bias reflects rational use of cognitive resources. Psychonomic bulletin & review, 25(1), 322-349.
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  • Sedgwick, P., & Greenwood, N. (2015). Understanding the Hawthorne effect. Bmj, 351, h4672.
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Year 2019, Volume: 3 Issue: 1, 101 - 120, 01.06.2019

Abstract

References

  • Adame, B. J. (2016). Training in the mitigation of anchoring bias: A test of the consider-the-opposite strategy. Learning and Motivation, 53, 36-48.
  • Baack, S. (2015). Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism. Big Data & Society, 2(2), 2053951715594634.
  • Begue, L., & Bastounis, M. (2003). Two spheres of belief in justice: Extensive support for the bidimensional model of belief in a just world. Journal of Personality, 71(3), 435-463.
  • Berghel, H. (2018). Malice Domestic: The Cambridge Analytica Dystopia. Computer, 5, 84-89.
  • Berthelot, J. M., Nizard, J., & Maugars, Y. (2018). The Negative Hawthorne Effect: Explaining Pain Overexpression. Joint Bone Spine. doi: 10.1016/j.jbspin.2018.10.003
  • Berwick, A. (2018). How ZTE helps Venezuela create China-style social control.
  • Reuters, 14.11.2018. https://www.reuters.com/investigates/special-report/venezuela-zte/
  • Bissell, K., & Parrott, S. (2013). Prejudice: The role of the media in the development of social bias. Journalism & Communication Monographs, 15(4), 219-270.
  • Botsman, R. (2017). Big data meets Big Brother as China moves to rate its citizens. Wired UK, 21.10.2017. https://www.wired.co.uk/article/chinese-government-social-credit-score-privacy-invasion
  • Buraschi, D., Bustillos, A., & Huici, C. (2018). Attitudes toward Immigrants, Beliefs about Causes of Poverty and Effects of Perspective-Taking. The Spanish journal of psychology, 21.
  • Cao, X., & Wang, Y. (2016). An Approach to Granting Subsidies to College Students in China Using Big Data. Journal of Education and Practice, 7(26), 1-4.
  • Chagnon, N. J. (2017). Racialized Culpability: Victim Blaming and State Violence. In Race, Ethnicity and Law (pp. 199-219). Emerald Publishing Limited.
  • Chen, L. F., Vander Weg, M. W., Hofmann, D. A., & Reisinger, H. S. (2015). The Hawthorne effect in infection prevention and epidemiology. infection control & hospital epidemiology, 36(12), 1444-1450.
  • D’Ariano, G. M. (2003). On the Heisenberg principle, namely on the information‐disturbance trade‐off in a quantum measurement. Fortschritte der Physik: Progress of Physics, 51(4‐5), 318-330.
  • Dunleavy, D. (2015). Data visualization and infographics. Visual Communication Quarterly, 22(1), 68-68.
  • Ellison, N. B., Hancock, J. T., & Toma, C. L. (2012). Profile as promise: A framework for conceptualizing veracity in online dating self-presentations. new media & society, 14(1), 45-62.
  • Feng, G., & China, O. D. (2015). Open data: concepts, status and opportunities. Big Data Research, (2), 2.
  • Fernando, W. D. A. (2018). Exploring character in the new capitalism: a study of mid-level academics’ in a British research-intensive university. Studies in Higher Education, 43(6), 1045-1057.
  • Fitzi, G., & Mele, V. (2017). The corrosion of character: Work and personality in the modern age. Journal of Classical Sociology, 17(2), 143-155.
  • Furnham, A. (2003). Belief in a just world: Research progress over the past decade. Personality and individual differences, 34(5), 795-817.
  • Gentile, B., Twenge, J. M., Freeman, E. C., & Campbell, W. K. (2012). The effect of social networking websites on positive self-views: An experimental investigation. Computers in Human Behavior, 28(5), 1929-1933.
  • Gezgin, U. B. (2018). An invitation to critical social science of big data: from critical theory and critical research to omniresistance. AI & SOCIETY, 1-9.doi: 10.1007/s00146-018-0868-y
  • Godfrey, E. B., & Wolf, S. (2016). Developing critical consciousness or justifying the system? A qualitative analysis of attributions for poverty and wealth among low-income racial/ethnic minority and immigrant women. Cultural Diversity and Ethnic Minority Psychology, 22(1), 93-103.
  • Gonzales, A. L., & Hancock, J. T. (2011). Mirror, mirror on my Facebook wall: Effects of exposure to Facebook on self-esteem. Cyberpsychology, behavior, and social networking, 14(1-2), 79-83.
  • Goodman, S., & Carr, P. (2017). The just world hypothesis as an argumentative resource in debates about unemployment benefits. Journal of Community & Applied Social Psychology, 27(4), 312-323.
  • Guowei, W. (2009). A Tentative Discussion on Establishing Social Credit System in China. Journal of Shanxi Radio & TV University, (6), 34. ttp://en.cnki.com.cn/Article_en/CJFDTOTAL-SXGB200906034.htm
  • Hai-yan, W. A. N. G. (2010). A Legal & Economic Analysis on Social Credit Crisis in China. Credit Reference, (6), 5.
  • http://en.cnki.com.cn/Article_en/CJFDTOTAL-HNJG201006005.htm
  • Hirst, M. (2014). “Big Data” Is Creating a Surveillance Economy. Issues magazine, 109, 19-21.
  • Hodson, H. (2015). Big brother is rating you (if you’re Chinese). New Scientist, 228(3043), 22–23. doi:10.1016/s0262-4079(15)31412-3
  • Howard, A., & Borenstein, J. (2018). The ugly truth about ourselves and our robot creations: the problem of bias and social inequity. Science and engineering ethics, 24(5), 1521-1536.
  • Huang, J. X., & Wu, Y. (2002). Some problems in establishment of social credit system in China [J]. Journal of Harbin Institute of Technology (Social Sciences Edition), 3.
  • http://en.cnki.com.cn/Article_en/CJFDTOTAL-HRBG200203014.htm
  • Huang, X. (2018, August). Research on the Legal Protection of Personal Information in China in the Environment of Big Data. In 3rd International Conference on Judicial, Administrative and Humanitarian Problems of State Structures and Economic Subjects (JAHP 2018). Atlantis Press.
  • https://download.atlantis-press.com/article/25902184.pdf
  • Huogen, W. (2013). The Development Trend and Its System Construction of Social Credit in China [J]. Chongqing Social Sciences, 7.
  • http://en.cnki.com.cn/Article_en/CJFDTOTAL-CQSK201307008.htm
  • Isaak, J., & Hanna, M. J. (2018). User Data Privacy: Facebook, Cambridge Analytica, and Privacy Protection. Computer, 51(8), 56-59.
  • Joh, E. E. (2016). The new surveillance discretion: Automated suspicion, big data, and policing. Harvard Law & Policy Review, 10, 15-42.
  • Jun-yue, L. I. N. (2011). Social Credit System: China′ s Model for Efficiently
  • Establishing Credit Reference System [J]. Credit Reference, 2. http://en.cnki.com.cn/Article_en/CJFDTOTAL-HNJG201102002.htm
  • LaBrie, R. C., Steinke, G. H., Li, X., & Cazier, J. A. (2018). Big data analytics sentiment: US-China reaction to data collection by business and government. Technological Forecasting and Social Change, 130, 45-55.
  • Lammers, J., & Burgmer, P. (2018). Power increases the self‐serving bias in the attribution of collective successes and failures. European Journal of Social Psychology.
  • Laterza, V. (2018). Cambridge Analytica, independent research and the national interest. Anthropology Today, 34(3), 1-2.
  • Li, N., Brossard, D., Scheufele, D. A., Wilson, P. H., & Rose, K. M. (2018). Communicating data: interactive infographics, scientific data and credibility. Journal of Science Communication, 17(2), A06.
  • Lieder, F., Griffiths, T. L., Huys, Q. J., & Goodman, N. D. (2018). The anchoring bias reflects rational use of cognitive resources. Psychonomic bulletin & review, 25(1), 322-349.
  • Loubere, N., & Brehm, S. (2018). The Global Age of Algorithm: Social Credit and the Financialisation of Governance in China. Made in China: A Quarterly on Chinese Labour, Civil Society, and Rights, 3(1), 38-43.
  • Martin, E. (1981). A twist on the heisenberg principle: Or, how crime affects its measurement. Social Indicators Research, 9(2), 197-223.
  • Mei, H. (2008). Establishing Efficient Social Credit System in China from European Experience of Social Credit System/ Etablissement du Systeme de Credit Social Efficace en Chine d’apres les Experiences du Systeme de Credit Social Europeen. Canadian Social Science, 4(1), 1-3.
  • Miller, K. (2014). Total surveillance, big data, and predictive crime technology: Privacy’s perfect storm. Journal of Technology of Law & Policy, 19, 105- 146.
  • Moosa, I. A., & Ramiah, V. (2017). Overconfidence and Self-Serving Bias. In The Financial Consequences of Behavioural Biases (pp. 45-69). Palgrave Macmillan, Cham.
  • O’Connor, P., & Crome, K. (2016). Learning together: Foucault, Sennett and the crisis of the co-operative character. Journal of Co-operative Studies, 49(2), 30-42.
  • Otten, J. J., Cheng, K., & Drewnowski, A. (2015). Infographics and public policy: using data visualization to convey complex information. Health Affairs, 34(11), 1901-1907.
  • Paterson, P. (2008). The Truth About Tonkin. Naval History Magazine, 22(1), np. https://www.usni.org/magazines/naval-history-magazine/2008/february/truth-about-tonkin
  • Proverbio, A. M., La Mastra, F., & Zani, A. (2016). How negative social bias affects memory for faces: An electrical neuroimaging study. PloS One, 11(9), e0162671.
  • Rosenberg, M., Pettifor, A., Twine, R., Hughes, J. P., Gomez-Olive, F. X., Wagner, R. G., Sulaimon, A., Tollman, S., Selin, A., MacPhail, C. & Kahn, K. (2018). Evidence for sample selection effect and Hawthorne effect in behavioural HIV prevention trial among young women in a rural South African community. BMJ Open, 8(1), e019167. doi: 10.1136/bmjopen-2017-019167
  • Ruijer, E. H., & Martinius, E. (2017). Researching the democratic impact of open, government data: A systematic literature review. Information Polity, 22(4), 233-250.
  • Saran, R., Steffick, D., & Bragg-Gresham, J. (2017). The China Kidney Disease Network (CK-NET):“Big Data—Big Dreams”. American Journal of Kidney Diseases, 69(6), 713-716.
  • Schwartz B (2012) Rethinking conflict and collective memory. The case of Nanking. In J. Alexander, R. Jacobs and P. Smith (eds.). The Oxford Handbook of Cultural Sociology. New York: Oxford University Press, pp. 529–563.
  • Sedgwick, P., & Greenwood, N. (2015). Understanding the Hawthorne effect. Bmj, 351, h4672.
  • Sennett R (2001) The Corrosion of Character: The Personal Consequences of Work in the New Capitalism. New York: Routledge.
  • Sposito, G. (1969). IV. Does a generalized Heisenberg principle operate in the social sciences?. Inquiry: An Interdisciplinary Journal of Philosophy, 12(1- 4), 356-361.
  • Tao, Y., & Mengwei, Y. (2014). The internal operating mechanism of the construction of social credit system in contemporary China. Theory and Modernization, 1(1).
  • http://en.cnki.com.cn/Article_en/CJFDTotal-LYXD201401001.htm
  • Walther, J. B. (2007). Selective self-presentation in computer-mediated communication: Hyperpersonal dimensions of technology, language, and cognition. Computers in Human Behavior, 23(5), 2538-2557.
  • Wang, X. J., & Li, S. T. (2011). Thoughts on Construction of Social Credit System in Liaoning Province, China. Credit Reference, (1), 19.
  • http://en.cnki.com.cn/Article_en/CJFDTotal-HNJG201101019.htm
  • Wang, Z. (2009). Old wounds, new narratives: Joint history textbook writing and peacebuilding in East Asia. History & Memory, 21(1), 101-126.
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There are 91 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Ulaş Başar Gezgin

Fatma Yıldırım

Publication Date June 1, 2019
Published in Issue Year 2019 Volume: 3 Issue: 1

Cite

APA Gezgin, U. B., & Yıldırım, F. (2019). Big Data in China: Bigger Problems, Easier Solutions or Both?. Asya Araştırmaları Uluslararası Sosyal Bilimler Dergisi, 3(1), 101-120.
Asian Studies International Journal of Social Sciences is licensed under a Creative Commons Attribution-Non Commercial 4.0 (CC BY NC) International License