Televizyon Dizilerindeki Psikolojik Hastalık Tasvirlerinin İnternet Aramaları Üzerindeki Etkisi: Google Trends Verilerine Dayalı Bir Analiz

Abstract

Son yıllarda popüler televizyon dizilerinde psikolojik hastalık tasvirleri sıklıkla yer almaktadır. Medyanın akıl sağlığıyla ilgilenmesi, izleyicilerin davranışları üzerinde potansiyel kültürel etkiye sahiptir. Bu çalışma, psikoloji temalı dizilerin ruh sağlığına yönelik internet arama ilgisini önemli oranda tetikleyebileceğini öne sürmektedir. Çalışmada herkese açık bir veri tabanı olan Google Trends aracılığıyla 2019-2021 yılları arasında dizilerde gösterilen psikolojik hastalıklara toplumun dijital ilgisi izlenmiştir. Çalışmanın örneklemi Kırmızı Oda (2020- ) ve Masumlar Apartmanı (2020- ) dizileridir. Çalışmada nicel ve tanımlayıcı bir yöntem kullanılmıştır. Analizler sonucunda “paranoid kişilik bozukluğu,” “Cotard sendromu,” “panik atak,” “major depresyon,” “obsesif kompulsif bozukluk,” “enürezis,” “dispozofobi” ve “borderline kişilik bozukluğu” gibi terimlerin her birinin dizilerin hikâyesiyle bağlantılı olarak bir arama zirvesine sahip olduğu gözlemlenmiştir. Bulgular, televizyon dizilerinin psikiyatrik bozukluklar gibi çeşitli sosyal sorunların internet aramalarında güçlü çıkışları teşvik edebileceğini göstermektedir.

Keywords

• Televizyon dizileri
• internet arama motoru
• Google Trends
• big data kullanımı
• psikiyatrik bozukluklar

The Effect of Psychological Disease Portrayals in TV Series on Internet Searches: A Google Trends Based Analysis

Abstract

This study suggests that TV series about psychology will significantly trigger internet search interest in mental health. The study observed public digital interest of psychiatric disorders represented in TV series through Google Trends, a public database between 2019-2021. The present study explored the social impact of two TV series based on real stories and focused on psychological analysis. These serials were adapted from the novel Madalyonun İçi (2004). Kırmızı Oda (2020- ) exhibits the processes of psychotherapy, and Masumlar Apartmanı (2020- ) narrates the daily lives of individuals with psychiatric disorders. The terms searched in Google Trends such as “paranoid personality disorder,” “Cotard syndrome,” “panic attack,” “major depression,” “obsessive compulsive disorder,” “enuresis,” “disposophobia,” and “borderline personality disorder” were peaked about the story of these TV series. The findings showed that TV shows spurred substantial rises in internet searches of various social problems, such as psychiatric disorders.

Keywords

• TV Series
• Interner search engine
• Google Trends
• big data utilization
• psychiatric disorders

References

1. Abedi, V., Mbaye, M., Tsivgoulis, G., Male, S., Goyal, N., Alexandrov, A. V., & Zand, R. (2015). Internet-based information-seeking behavior for transient ischemic attack. International Journal of Stroke, 10, 1212-1216. doi:10.1111/ijs.12593
2. Acar Gündüz, G. (2020). Covid-19 ve Psikoloji. Retrieved from https://www.uspsikiyatri.com.tr/Makaleler/Ruh_Sagligi/Covid-19_ve_Psikoloji/
3. Anastasio, P. A., Rose, K. C., & Chapman, J. (1999). Can the media create public opinion? A social-identity approach. Current Directions in Psychological Science, 8, 152-155. doi:10.1111/1467-8721.00036
4. Arendt, F., & Scherr, S., (2017). Optimizing online suicide prevention: a search engine-based tailored approach optimizing online suicide prevention: a search engine-based tailored approach. Health Communication, 32, 1403-1408. doi.org/10.1080/10410236.2016.1224451
5. Askitas, N. & Zimmermann, K. (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly, 55, 107-120. Retrieved from http://ftp.iza.org/dp4201.pdf
6. Banerjee, S. (2018). How does the world Google the internet, anxiety, and happiness? Cyberpsychology, Behavior, and Social Networking, 21, 569-574. doi:10.1089/cyber.2018.0206
7. Beck, C. S., Aubuchon, S. M., McKenna, T. P., Ruhl, S., & Simmons, N. (2014). Blurring personal health and public priorities: An analysis of celebrity health narratives in the public sphere. Health Communication, 29, 244-256. doi:10.1080/10410236.2012.741668
8. Berlin, F. S., & Malin, H. M. (1991). Media distortion of the public’s perception of recidivism and psychiatric rehabilitation. American Journal of Psychiatry, 148, 1572-1576. doi:10.1176/ajp.148.11.1572

9. Bragazzi, N. L. (2013). A google trends-based approach for monitoring NSSI. Psychology Research and Behavior Management, 7, 1-8. doi.org/10.2147/PRBM.S44084
10. Brodeur A., Clark A. E., Fleche S., & Powdthavee, N. (2020). COVID-19, lockdowns and well- being: evidence from Google Trends. Journal of Public Economics, 193, 104346. doi.org/10.1016/j.jpubeco.2020.104346
11. Budayıcıoğlu, G. (2004). Madalyonun içi. İstanbul, Turkey: Remzi.
12. Budayıcıoğlu, G. (2011). Hayata dön. İstanbul, Turkey: Remzi.
13. Budayıcıoğlu, G. (2019). Camdaki kız. İstanbul, Turkey: Doğan.
14. Calhoun, A. J., & Gold, J. A. (2020). “I Feel Like I Know Them”: the positive effect of celebrity self-disclosure of mental illness. Academic Psychiatry, 44, 237-241. doi:10.1007/s40596-020-01200-5
15. Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2-9. doi:10.1111/j.1475-4932.2012.00809.x
16. Comstock, G., Lindsey, G. & Fisher, M. (1975). Television and Human Behavior: The Research Horizon, Future and Present. California, USA: The Rand Corporation.
17. Comstock, G., Chaffee, S., Katzman, N., McCombs, M. & Roberts, D. (1978). Television and Human Behavior. New York, USA: Columbia University.
18. Cram, P., Fendrick, A. M., Inadomi, J., Cowen, M., Carpenter, D., & Vijan, S. (2003). The impact of a celebrity promotional campaign on the use of colon cancer screening. Archives of Internal Medicine, 163, 1601-1605. doi:10.1001/archinte.163.13.1601
19. Dal Cin, S., Gibson, B., Zanna, M. P., Shumate, R., & Fong, G. T. (2007). Smoking in movies, implicit associations of smoking with the self, and intentions to smoke. Psychological Science, 18, 559-563. doi:10.1111/j.1467-9280.2007.01939
20. Damjanović, A., Vuković, O., Jovanović, A. A., & Jašović-Gašić, M. (2009). Psychiatry and movies. Psychiatria Danubina, 21, 230-235. Retrieved from https://pubmed.ncbi.nlm.nih.gov/19556954/
21. De Vogli, R., Marmot, M., & Stuckler, D. (2013). Excess suicides and attempted suicides in Italy attributable to the great recession. Journal of Epidemiology and Community Health, 67, 378-379. doi.org/10.1136/jech-2012-201607
22. Digital 2021 global overview report (2021). Retrieved form https://wearesocial.com/digital-2021
23. Dill, K. E. (2009). How fantasy becomes reality: Seeing through media influence. New York, US: Oxford University.
24. Ding, D., del Pozo Cruz, B., Green, M. A., & Bauman, A. E. (2020). Is the COVID-19 lockdown nudging people to be more active: a big data analysis. British Journal of Sports Medicine, 54, 1183-1184. doi:10.1136/bjsports-2020-102575
25. Economou, M., Madianos, M., Peppou, L.E., Theleritis, C., Patelakis A., & Stefanis, C. (2013). Suicidal ideation and reported suicide attempts in Greece during the economic crisis. World Psychiatry, 12, 53-59. doi:10.1002/wps.20016
26. Eysenbach, G. (2011). Infodemiology and infoveillance tracking online health information and cyberbehavior for public health. American Journal of Preventive Medicine, 40, 154-158. doi:10.1016/j.amepre.2011.02.006
27. FAQ about Google Trends data. (2021). Retrieved from TrendsHelp.GoogleSupport.https://support.google.com/trends/answer/4365533?hl=en(2021)
28. Fawcett, K. (2015). How mental illness is portrayed in the misrepresented in the media: insidious portrayals on TV shape perceptions about real-life people with psychological disorders. US News & World Report 2015. Retrieved from https://health.usnews.com/health-news/health-wellness/articles/2015/04/16/how-mental-illness-is-misrepresented-in-the-media
29. Gerbner, G. (1980). Dreams that hurt: Mental illness in the mass media. In R. C. Baron, I. D. Rutman & G. Klaczynska (Ed.), The Community Imperative (pp. 19-23). Philadelphia, US: Horizon House Institute.
30. Gerbner, G., Gross, L., Morgan, M., & Signorielli, N. (1982). Charting the mainstream: Tv's contributions to political orientations. Journal of Communication 32, 100-127. doi:10.1111/j.1460-2466.1982.tb00500.x
31. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457, 1012-1014. doi:10.1038/nature07634
32. Gledhill, C. (1987). The Melodramatic Field: An Investigation. In C. Gledhill (Ed.), Home is Where the Heart is: Studies in Melodrama and the Woman’s Film (pp. 5-39). London, UK: BFI.
33. Green, M. C., & Dill, K. E. (2013). Engaging with stories and characters: Learning, persuasion, and transportation into narrative worlds. In K. E. Dill (Ed.), The Oxford handbook of media psychology (pp. 449- 461). New York, US: Oxford University.
34. Greiner, B., Lee, A., Checketts, J., & Hartwell, M. (2020). Public awareness of tetralogy of Fallot after Jimmy Kimmel Live! television episode: A cross-sectional analysis. Experimental Results (2020), 1, e26, 1-7. doi:10.1017/exp.2020.34
35. Google Trendler Verileri Hakkında (2021). Retrieved from https://support.google.com/trends/answer/4365533?hl=tr
36. Gunn III, J. F., & Lester, D. (2013). Using google searches on the internet to monitor suicidal behavior. Journal of Affective Disorders, 148, 411-412. doi.org/10.1016/j.jad.2012.11.004
37. Güvenatam, O. (Producer). Kiremitçi Bozkurt, B. (Screenplay), Pars, V. (Screenplay), Karcı, C. (Director), Tairi, B. (Director), Balcı, A. (Director). (2020-2021). Kırmızı Oda [TV Series]. Turkey: OGM Pictures.
38. Güvenatam, O. (Producer), Madanoğlu, D. (Screenplay), Mamatlıoğlu, R. (Screenplay), Vila Lostuvalı, Ç. (Director), Mıhlar, E. (Director), Bozali, Ç. (Director). (2020-2021). Masumlar Apartmanı [TV Series]. Turkey: OGM Pictures.
39. Heath, E. (2019). Mental disorders in popular film how Hollywood uses, shames, and obscures mental diversity. Lanham, Boulder, New York, London, UK, US: Lexington.
40. Hoffner, C. A., & Cohen, E. L. (2015). Portrayal of mental illness on the TV series Monk: Presumed influence and consequences of exposure. Health Communication, 30, 1046-1054. doi:10.1080/10410236.2014.917840
41. Hyler, S. E. (1988). DSM-III at the cinema: Madness in the movies. Comprehensive Psychiatry 29, 195-206. doi.org/10.1016/0010-440X(88)90014-4
42. Jack, B. (2014). Goethe’s Werther and its effects. The Lancet Psychiatry, 1, 18-19. doi:10.1016/S2215-0366(14)70229-9.
43. Jamieson, P. E., & Romer, D. (2014). Violence in popular U.S. prime time TV dramas and the cultivation of fear: A time series analysis. Media and Communication, 2, 31-41. Retrieved from https://repository.upenn.edu/cgi/viewcontent.cgi?article=1365&context=asc_papers
44. Jansen, B. J., Spink, A., & Taksa, I. (2009). Research and methodological foundations of transaction log analysis. In Jansen, B. J., Spink, A. & Taksa, I. (Ed.), Handbook of research on web log analysis (pp. 1-17). HersheyPA, US: IGI. doi:10.4018/978-1-59904-974-8
45. Johnson, J. K. (2012). The Hero with a thousand dysfluencies: The changing portrayals of people who stutter. In L. C. Rubin (Ed.), Mental illness in popular media essays on the representation of disorders (pp. 9-25). Jefferson, North Carolina, London, US, UK: McFarland & Co.
46. Johnson, A. L., Torgerson, T., Cooper, C., Khojasteh, J., & Vassar, M. (2020). Public awareness of cleidocranial dysplasia after season releases of Stranger Things. JAMA Otolaryngology-Head & Neck Surgery, 146, 377-378. doi.org/10.1001/jamaoto.2019.4791
47. Juliette V. (2017, 28 Ekim). 10 TV Shows that got mental illness (Mostly) Right. Retrieved from https://themighty.com/2017/10/good-portrayals-mental-illness-tv-shows/
48. Jun, S. P., Park, D. H., & Yeom, J. (2014). The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference. Technological Forecasting & Social Change, 86, 237-253. doi:10.1016/j.techfore.2013.10.0
49. Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting & Social Change, 130, 69-87. doi:10.1016/j.techfore.2017.11.009
50. Jurić, T. (2021). Google Trends as a method to predict new COVID-19 cases and socio-psychological consequences of the pandemic. Athens Journal of Mediterranean Studies, 7, 1-25. doi.org/10.30958/ajms.X-Y-Z
51. Kaleem, T., Malouff, T. D., Stross, W. C., Waddle, M. R, Miller, A. L., Seymour, A. L. ... Vallow, L. (August 10, 2019) Google Search Trends in oncology and the impact of celebrity cancer awareness. Cureus, 11, e5360. doi:10.7759/cureus.5360
52. Kang, M., Zhong, H., He, J., Rutherford, S., & Yang, F. (2013). Using Google Trends for influenza surveillance in South China. PLoS ONE, 8, e55205. doi:10.1371/journal.pone.0055205
53. Katz, E., & Lazarsfeld, P. F. (1955). Personal Influence: The Part Played by People in the Flow of Mass Communications. New Jersey, USA: Transcation Publishers.
54. Keen, S. (2006). A theory of narrative empathy. Narrative, 14, 207-236. Retrieved from https://www.jstor.org/stable/20107388
55. Khoury, M. J., & Ioannidis, J. P. (2014). Big data meets public health. Science, 346, 1054-1055. doi:10.1126/science.aaa2709
56. Klapper, J. T. (1960). The Effects of Mass Communication. Illions, USA: Free Press of Glencoe.
57. Lebo, P. B., Quehenberger, F., Kamolz, L. P., & Lumenta, D. B. (2015). The Angelina effect revisited: Exploring a media-related impact on public awareness. Cancer, 121, 3959-3964. doi:10.1002/cncr.29461
58. Lobato, R. (2019). Netflix nations the geography of digital distribution. New York University Press.
59. Ma-Kellams, C., Or, F., Baek, J. H., & Kawachi, I., (2016). Rethinking suicide surveillance: Google Search Data and self-reported suicidality differentially estimate completed suicide risk. Clinical Psychological Science, 4, 480-484. doi:10.1177/2167702615593475
60. Mavragani, A., & Ochoa, G. (2019). Google Trends in infodemiology and infoveillance: methodology framework. JMIR Public Health Surveill, 5, e13439-e39. doi: 10.2196/13439
61. McQuail, D. (2005). McQuail’s Mass Communication Theory. London: Sage.
62. McQuail, D. & Windahl, S. (2013). Communication Models for the Study of Mass Communications Second Edition. London & New York, UK & USA: Routledge.
63. Moreno, M. A., Christakis, D. A., Egan, K. G., Brockman, L. N., & Becker, T. (2012). Associations between displayed alcohol references on facebook and problem drinking among college students. Archives of Pediatrics & Adolescent Medicine 166, 157-163. doi.org/10.1001/archpediatrics.2011.180
64. Niederkrotenthaler, T., Till, B., Kapusta, N. D., Voracek, M., Dervic K., & Sonneck, G. (2009). Copycat effects after media reports on suicide: A population-based ecologic study. Social Science & Medicine, 69, 1085-1090. doi: 10.1016/j.socscimed.2009.07.041
65. Nghiem, L. T. P., Papworth, S. K., Lim, F. K. S., & Carrasco, L. R. (2016). Analysis of the capacity of Google Trends to measure interest in conservation topics and the role of online News. PLoS ONE, 11, e0152802. doi:10.1371/journal.pone.0152802
66. Ortiz, S. N., Forrest, L. N., Fisher, T. J., Hughes, M., & Smith, A. R. (2019). Changes in internet suicide search volumes following celebrity suicides. Cyberpsychology, Behavior, and Social Networking, 22, 373-380. doi:10.1089/cyber.2018.0488
67. Parker, J., Cuthbertson, C., Loveridge, S., Skidmore, M., & Dyar, W. (2017). Forecasting state-level premature deaths from alcohol, drugs, and suicides using Google Trends data. Journal of Affective Disorders, 213, 9-15. doi:10.1016/j.jad.2016.10.038
68. Polgreen, P. M., Chen, Y., Pennock, D. M., & Nelson, F. D. (2008). Using internet searches for influenza surveillance. Clinical Infectious Diseases, 47, 1443-1448. doi:10.1086/593098
69. Prasanth, S., Singh, U., Kumar, A., Tikkiwal, V. A., & Chong, P. H. J. (2020). Forecasting spread of COVID-19 using Google Trends: A hybrid GWO-Deep learning approach. Chaos, Solitons & Fractals, 142, 110336. doi:10.1016/j.chaos.2020.110336
70. Ricketts, C. F., & Silva, C. G. (2017). An analysis of morbidity and mortality using Google Trends. Journal of Human Behavior in the Social Environment, 27, 559-570. doi:10.1080/10911359.2017.129965
71. Rubin, A. M. (2009). The Uses-and-Gratifications Perspective of Media Effects. In B. Jennings & M. B. Oliver (Eds.), Media Effects: Advances in Theory and Reserach (pp. 165-184). New York, USA: Rouledge.
72. Scalvini, M. (2020). 13 Reasons Why: can a TV show about suicide be “dangerous”? What are the moral obligations of a producer? Media, Culture & Society, 1-11. doi:10.1177/0163443720932502
73. Smith, S. L., Choueiti, M., Choi, A., Pieper, K., & Moutier, C. (2019). Mental health conditions in film & TV: portrayals that dehumanize and trivialize characters. USC Annenberg Inclusion Initiative, American Foundation for Suicide Prevention, and The David and Lura Lovell Foundation. Retrieved from http://assets.uscannenberg.org/docs/aii-study-mental-health-media_052019.pdf.
74. Valkenburg, P. M., Peter, J., & Walther, J. B. (2016). Media Effects: Theory and Research. Annual Review of Psychology, 67, 315-338. doi:10.1146/annurev-psych-122414-033608
75. Varian, H., & Choi, H. (2009, 5 July). Predicting initial claims for unemployment benefits. Google Technical report. Retrieved from http://static.googleusercontent.com/media/research.google.com/en/us/archive/papers/initialclaimsUS.pdf
76. Wahl, O. F. (1995). Media madness public images of mental illness.: New Brunswick, New Jersey, US: Rutgers University.
77. Wang, P., Xu, Q., Cao, R.R., Deng, F. Y., & Lei, S. F. (2021). Global public interests and dynamic trends in osteoporosis from 2004 to 2019: Infodemiology study. Journal of Medical Internet Research, 23, e25422. doi:10.2196/25422
78. Wedding, D., & Niemiec, R. (2003). The clinical use of films in psychotherapy. Journal of Clinical Psychology, 59, 207-215. doi:10.1002/jclp.10142
79. Williams, L. (1998). Melodrama Revisited. In N. Brown (Ed.), Refiguring American Film Genres: Theory and History (pp. 42-89). Berkeley & Los Angeles: University of California.
80. Wong, N. C. H., Lookadoo, K. L., & Nisbett, G. S. (2017). “I’m Demi and I have bipolar disorder”: Effect of parasocial contact on reducing stigma toward people with bipolar disorder. Communication Studies, 68, 1-20. doi:10.1080/10510974.2017.1331928
81. Yang, A. C., Huang, N. E., Peng, C. K., & Tsai, S. J. (2010). Do seasons have an influence on the incidence of depression? The use of an internet search engine query data as a proxy of human affect. PLoS ONE, 5, e13728. doi:10.1371/journal.pone.0013728
82. Yang, A. C., Tsai, S.-J., Huang, N. E., & Peng, C. K. (2011). Association of internet search trends with suicide death in Taipei City, Taiwan, 2004-2009. Journal of Affective Disorders, 132, 179-184. doi:10.1016/j.jad.2011.01.019
83. Yin, S., Ho, M., (2012). Monitoring a toxicological outbreak using Internet search query data. Clinical Toxicology, 50, 818-822. doi.org/10.3109/15563650.2012.729667
84. Young, S. D. (2012). Psychology at the movies. Wiley-Blackwell. doi/10.1002/9781119941149
85. Zigmond, D., & Stipp, H. (2010). Assessing a new advertising effect. Journal of Advertising Research, 50, 162-168. doi:10.2501/s0021849910091324