References
- Nichols T. The death of expertise: the campaign against established knowledge and why it matters. New York (NY): Oxford University Press; 2017.
- Susskind RE, Susskind D. The future of the professions: how technology will transform the work of human experts. Oxford: Oxford University Press; 2017.
- Choi YS. Artificial intelligence: will it replace human medical doctors? Korean Med Educ Rev. 2016;18(2):47-50. https://doi.org/10.17496/kmer.2016.18.2.47
- Ellaway RH, Pusic MV, Galbraith RM, Cameron T. Developing the role of big data and analytics in health professional education. Med Teach. 2014;36(3):216-22. https://doi.org/10.3109/0142159X.2014.874553
- Tighe PJ, Harle CA, Hurley RW, Aytug H, Boezaart AP, Fillingim RB. Teaching a machine to feel postoperative pain: combining highdimensional clinical data with machine learning algorithms to forecast acute postoperative pain. Pain Med. 2015;16(7):1386-401. https://doi.org/10.1111/pme.12713
- Howe D, Costanzo M, Fey P, Gojobori T, Hannick L, Hide W, et al. Big data: the future of biocuration. Nature. 2008;455(7209):47-50. https://doi.org/10.1038/455047a
- Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920-30. https://doi.org/10.1161/CIRCULATIONAHA.115.001593
- Lee M. Big data and utilization of public data. Internet Inf Secur. 2011;2(2):47-64.
- Kang B, Song M, Jho W. A study on opinion mining of newspaper texts based on topic modeling. J Korean Soc Libr Inf Sci. 2013;47(4):315-34. https://doi.org/10.4275/KSLIS.2013.47.4.315
- Park JH, Park E, Jo DJ. Automated text analysis of North Korean new year addresses, 1946-2015. Korean Polit Sci Assoc. 2015;49(2):27-61. https://doi.org/10.18854/kpsr.2015.49.2.002
- Jockers ML. Text analysis with R for students of literatures. New York (NY): Springer; 2014.
- An JJ. Asbestos, a silent killer. Paju: Hanul; 2008.
- Park EK. Environmental molecular epidemiological mechanism studies on asbestos-related diseases. Environ Health Toxic. 2012;10:87-9.
- Jung JS, Jung HS, Lee JY, Lee WS, Kwon OS, Kim SM. A study of asbestos characteristics and correlation of environmental factors in naturally occurring asbestos areas. Korean Soc Living Environ Syst. 2015;22(5):639-46. https://doi.org/10.21086/ksles.2015.10.22.5.639
- Ham T, Jeong M. A review of legal issues over relief of damages resulting from asbestos. Environ Law Policy. 2011;6:179-216. https://doi.org/10.18215/envlp.6..201105.179
- Coleman S. New mediation and direct representation: reconceptualizing representation in the digital age. New Media Soc. 2005;7(2):177-98. https://doi.org/10.1177/1461444805050745
- Wilkerson JD, Casas A. Large-scale computerized text analysis in political science: opportunities and challenges. Annu Rev Polit Sci. 2017;20:529-44. https://doi.org/10.1146/annurev-polisci-052615-025542
- Jacobi C, van Atteveldt W, Welbers K. Quantitative analysis of large amounts of journalistic texts using topic modelling. Digit Journal. 2016;4(1):89-106. https://doi.org/10.1080/21670811.2015.1093271
- Lakoff G. Don't think of an elephant!: know your values and frame the debate. White River Junction (VT): Chelsea Green Publishing; 2014.
- Lakoff G. Moral politics: how liberals and conservatives think. 2nd ed. Chicago (IL): University of Chicago Press; 2002.
- Scheufele DA. Framing as a theory of media effects. J Commun. 1999;49(1):103-22. https://doi.org/10.1111/j.1460-2466.1999.tb02784.x
- Kim SJ, Cheong YG. Non-reporting, media ethics and ideological conflicts in South Korea: focus on media coverage relating to surveillance of civilians by the National Intelligence Service and the Defense Security Command. Korean J Commun Inf. 2011;53:5-28.
- Castillo C. Effective web crawling. ACM SIGIR Forum. 2005;39(1):55-6. https://doi.org/10.1145/1067268.1067287
- Myers D, McGuffee JW. Choosing Scrapy. J Comput Sci Coll. 2015;31(1):83-9.
- Park EL, Cho S. KoNLPy: Korean natural language processing in Python. Proceedings of the 26th Annual Conference on Human & Cognitive Language Technology; 2014 Oct 10; Chuncheon, Korea. Seoul: Korean Society of Speech Sciences; 2014.
- Jacobi C, van Atteveldt W, Welbers K. Quantitative analysis of large amounts of journalistic texts using topic modelling. Digit Journal. 2016;4(1):89-106. https://doi.org/10.1080/21670811.2015.1093271
- Chang J, Gerrish S, Wang C, Boyd-Graber JL, Blei DM. Reading tea leaves: how humans interpret topic models. In: Bengio Y, editor. Advances in neural information processing systems 22. New York (NY): Curran Associates Inc.; 2009. p. 288-96.
- Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, et al. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. 2015;16 Suppl 13:S8.
- Blei DM. Probabilistic topic models. Commun ACM. 2012;55(4):77-84. https://doi.org/10.1145/2133806.2133826
- Ahmed A, Xing EP. Staying informed: supervised and semi-supervised multi-view topical analysis of ideological perspective. In: Li H, Marquez L, editors. Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing; 2010 Oct 9-11; Cambridge, USA. Stroudsburg (PA): Association for Computational Linguistics; 2010. p. 1140-50.
- Blei DM, Lafferty JD. Dynamic topic models. In: Cohen W, Moore A, editors. Proceedings of the 23rd International Conference on Machine Learning; 2006 Jun 25-29; Pittsburgh, USA. New York (NY): Association of Computing Machinery; 2006. p. 113-20.
- Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P. The author-topic model for authors and documents. In: Chickering M, Halpern J, editors. Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence; 2004 Jul 7-11; Banff, Canada. Arlington (TX): Association for Uncertainty in Artificial Intelligence; 2004. p. 487-94.
- Roberts ME, Stewart BM, Tingley D, Airoldi EM. The structural topic model and applied social science. Proceedings of the advances in Neural Information Processing Systems workshop on topic models: computation, application, and evaluation; 2013 Dec 10; Lake Tahoe, USA. La Jolla (CA): Neural Information Processing System Foundation; 2013.
- R Development Core Team. R: a language and environment for statistical computing [Internet]. Vienna: R Foundation for Statistical Computing; 2008 [cited 2016 Oct 18]. Available from: http://www.R-project.org.
- Seward JB. Paradigm shift in medical data management: big data and small data. JACC Cardiovasc Imaging. 2017 Jan 12 [Epub]. https://doi.org/10.1016/j.jcmg.2016.10.013.
- Murdoch TB, Detsky AS. The inevitable application of big data to health care. JAMA. 2013;309(13):1351-2. https://doi.org/10.1001/jama.2013.393
- Lakoff G. The political mind: why you can't understand 21st-century politics with an 18th-century brain. New York (NY): Penguin Books; 2008.
- Lem SM. The argument presented by the conservative and progressive seen through controversy over the free elementary school meal project centering on the framing of the newspaper media. Korean Polit Sci Rev. 2011;45(2):251-79. https://doi.org/10.18854/kpsr.2011.45.2.011
- Foucault M. Power/knowledge: selected interviews and other writings 1972-1977. New York (NY): Pantheon; 1980.
- Feinberg M, Willer R. The moral roots of environmental attitudes. Psychol Sci. 2013;24(1):56-62. https://doi.org/10.1177/0956797612449177
- Walsko C, Ariceaga H, Seiden J. Red, white, and blue enough to be green: effects of moral framing on climate change attitudes and conservation behaviors. J Exp Soc Psychol. 2016;65:7-19. https://doi.org/10.1016/j.jesp.2016.02.005
- Chung J, Cho JJ. Use of qualitative research in the field of health. J Korean Acad Fam Med. 2008;29(8):553-62.
- Hong L, Davison BD. Empirical study of topic modeling in Twitter. Proceedings of the first workshop on social media analytics; 2010 Jul 25-28; Washington DC, USA. New York (NY): Association of Computing Machinery; 2010. p. 80-8.
- Rosenkranz SK, Wang S, Hu W. Motivating medical students to do research: a mixed methods study using Self-Determination Theory. BMC Med Educ. 2015;15:95. https://doi.org/10.1186/s12909-015-0379-1
- Obermeyer Z, Emanuel EJ. Predicting the future: big data, machine learning, and clinical medicine. N Engl J Med. 2016;375(13):1216-9. https://doi.org/10.1056/NEJMp1606181
- Kim J. Big data, health informatics, and the future of cardiovascular medicine. J Am Coll Cardiol. 2017;69(7):899-902. https://doi.org/10.1016/j.jacc.2017.01.006