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Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi

Yıl 2020, Sayı: 58, 241 - 272, 30.07.2020

Zafer Kıyan Nurcan Törenli

https://doi.org/10.26650/CONNECTIST2020-0002

Öz

Literatür incelemesi niteliğindeki bu çalışma büyük veriye iletişim perspektifinden yaklaşan 62 makaleyi analiz etmektedir. Çalışma bu haliyle konuya ilgi duyan araştırmacılara büyük veri etrafında dönen tartışmaları, büyük veri analizi için tercih edilen kuramsal yaklaşımları ve kullanılan yöntemleri bir arada görebilme olanağı tanımaktadır. Çalışma kapsamına dâhil edilen makaleler niteliksel içerik analiz tekniği kullanılarak analiz edilmiştir. Bu yöntem, sistematik kodlamaya ve nitel yoruma olanak tanıdığı için literatür çalışmaları için oldukça elverişlidir. Analiz, incelemeye konu olan makalelerin iki ayrı gruba ayrıldıklarını ortaya koymuştur. Buna göre ilk gruptaki 45 makale doğrudan büyük veriye odaklanıp onu sorunsallaştırırken; ikinci gruptaki 17 makale büyük veriyi dolaylı olarak ele almakta ve iletişim eksenli bir konuyu ya da sorunu büyük bir veri kümesiyle analiz etmektedir. Bu ayrışma hem büyük veri olgusunun iletişim alanına özgü çalışmalarda yeni oluşundan hem de araştırmacıların karşısına çıkan çok sayıda araçsal ve bilişsel zorluklardan, sorunlardan kaynaklanmaktadır. Bu ayrışma elde edilen bulguları da farklılaştırmıştır. Çalışma bu haliyle odaklandığı konu ve ulaştığı sonuçlar açısından Türkçe literatürde önemli bir boşluğu dolduracak niteliktedir.

Anahtar Kelimeler

Büyük veri iletişim çalışmaları sosyal medya veri analizi etik

Destekleyen Kurum

Yazarlar bu çalışma için finansal destek almadığını beyan etmiştir.

Kaynakça

  • Altunışık, R. (2015). Büyük veri: Fırsatlar kaynağı mı yoksa yeni sorunlar yumağı mı? Yıldız Social Science Review, 1(1), 45–76. https://dergipark.org.tr/tr/pub/yssr/issue/21899/235390
  • Andrejevic, M. (2014). The big data divide. International Journal of Communication, 8, 1673–1689.
  • Arsenault, A., H. (2017). The datafication of media: Big data and the media industries. International Journal of Media & Cultural Politics, 13(1-2), 7–24. https://doi.org/10.1386/macp.13.1-2.7_1
  • Arslan, F., & Kahraman, H. (2019). Yapay zekâ tabanlı büyük veri yönetim aracı. Journal of Investigations on Engineering and Technology, 2(1), 8–21. https://dergipark.org.tr/tr/pub/jiet/issue/48409/602203
  • Assarroudi, A., Nabavi, F. H., Armat, M. R., Ebadi, A., & Vaismoradi, M. (2018). Directed qualitative content analysis: The description and elaboration of its underpinning methods and data analysis process. Journal of Research in Nursing, 23(1), 42–55. https://doi.org/10.1177/1744987117741667
  • Athique, A. (2018). The dynamics and potential of big data for audience research. Media, Culture & Society, 40(1), 59–74. https://doi.org/10.1177/0163443717693681
  • Ayvaz, S., & Salman, Y. (2020). Türkiye’de firmaların büyük veri teknolojileri bilinirliği ve kullanımı analizi. Avrupa Bilim ve Teknoloji Dergisi, 14(18), 728–737. https://doi.org/10.31590/ejosat.675247
  • Baruh, L., & Popescu, M. (2017). Big data analytics and the limits of privacy self-managements. New Media & Society, 19(4), 579–596. https://doi.org/10.1177/1461444815614001
  • boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662– 679. https://doi.org/10.1080/1369118X.2012.678878
  • Broussard, M. (2016). Big data in practice, enabling computational journalism through code-sharing and reproducible research. Digital Journalism, 4(2), 266–279. https://doi.org/10.1080/21670811.2015.1074863
  • Carpenter, C. J., & Amaravadi, C. S. (2019). A big data approach to assessing the impact of social norms: Reporting one’s exercise to a social media audience. Communication Research, 46(2), 236–249. https://doi. org/10.1177/0093650216657776
  • Chan, A. (2015). Big data interfaces and the problem of inclusion. Media, Culture and Society, 37(7), 1078–1083. https://doi.org/10.1177/0163443715594106
  • Chen, H., & Zhou L. (2018). The myth of big data: Chinese advertising practitioners’ perspective. International Journal of Advertising, 37(4), 633–649. https://doi.org/10.1080/02650487.2017.1340865
  • Cibaroğlu, M., & Yalçınkaya, B. (2019). Belge ve arşiv yönetimi süreçlerinde büyük veri analitiği ve yapay zekâ uygulamaları. Bilgi Yönetimi, 2(1), 44–58. https://doi.org/10.33721/by.570634
  • Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in twitter using big data. Journal of Communication, 64(2), 317–332. https://doi.org/10.1111/jcom.12084
  • Couldry, N., & Mejias, U. A. (2019). Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media, 20(4), 336–349. https://doi.org./10.1177/1527476418796632
  • Couldry, N., & Turow, J. (2014). Advertising, big data, and the clearance of the public realm: Marketers’ new approaches to the content subsidy. International Journal of Communication, 8, 1710–1726.
  • Crawford, K., Miltner, K., & Gray, M. L. (2014). Critiquing big data: Politics, ethics, epistemology. International Journal of Communication, 8, 1663–1672.
  • Driscoll, K., & Walker, S. (2014). Working within a black box: Transparency in the collection and production of big Twitter data. International Journal of Communication, 8, 1745–1764.
  • Elo, S., & Kyngäs, H. (2008) The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
  • Fairfield, J., & Shtein H. (2014). Big data, big problems: Emerging issues in the ethics of data science and journalism. Journal of Mass Media Ethics, 29, 38–51. https://doi.org/10.1080/08900523.2014.863126
  • Feng, G. C. (2019). A comparative study of the online film ratings of US and Chinese audiences: An analytical approach based on big data. International Communication Gazette, 81(3), 283–302. https://doi. org/10.1177/1748048518767799
  • Filibeli, T. (2019). Big data, artificial intelligence, and machine learning algorithms: A descriptive analysis of the digital threats in the post-truth era. Galatasaray Üniversitesi İletişim Dergisi, 31, 91–110. https://doi. org/10.16878/gsuilet.626260
  • Fuchs, C. (2017a). From digital positivism and administrative big data analytics towards critical digital and social media research! European Journal of Communication, 32(1), 37–49. https://doi.org/10.1177/0267323116682804
  • Fuchs, C. (2017b). Günter Anders’ undiscovered critical theory of technology in the age of big data capitalism. Triple C, 15(2), 582–611.
  • Guo, L., & Vargo, C, J. (2017). Global intermedia agenda setting: A big data analysis of international news flow. Journal of Communication, 67(4), 499–520. https://doi.org/10.1111/jcom.12311
  • Guo, L., & Vargo, C. (2015). The power of message networks: A big-data analysis of the network agenda setting model and issue ownership. Mass Communication and Society, 18(5), 557–576. https://doi.org/10.1080/1520 5436.2015.1045300
  • Guo, L., Vargo, C. J., Pan, Z., Ding, W., & Ishwar, P. (2016). Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling. Journalism & Mass Communication Quarterly, 93(2), 332–359. https://doi.org/10.1177/1077699016639231
  • Halavais, A. (2015). Bigger sociological imaginations: framing big social data theory and methods. Information, Communication & Society, 18(5), 583–594. https://doi.org/10.1080/1369118X.2015.1008543
  • Hammond, P. (2017). From computer-assisted to data-driven: Journalism and big data. Journalism, 18(4), 408– 424. https://doi.org/10.1177/1464884915620205.
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  • Konitzer, T., Rothschild, D., Hill, S., & Wilbur, K. C. (2019). Using big data and algorithms to determine the effect of geographically targeted advertising on vote intention: Evidence from the 2012 U.S. presidential election. Political Communication, 36(1), 1–16. https://doi.org/10.1080/10584609.2018.1467985
  • Kosterich, A. (2016). Reconfiguring the ‘hits’: The new portrait of television program success in an era of big data. International Journal on Media Management, 18(1), 43–58. https://doi.org/10.1080/14241277.2016.1166431
  • Kshetri, N. (2014). Big Data’s impact on privacy security, and consumer welfare. Telecommunications Policy, 38, 1134–1145. http://dx.doi.org/10.1016/j.telpol.2014.10.002
  • Leckner, S., & Severson P. (2019). Exploring the meaning problem of big and small data through digital method triangulation. Nordicom Review, 40(1), 79–94. https://doi.org/10.2478/nor-2019-0015
  • Lewis, S. C., & Westlund, O. (2014). Big data and journalism: Epistemology, expertise, economics, and ethics. Digital Journalism, 3(3), 447–466. https://doi.org/10.1080/21670811.2014.976418
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  • Light, B., Mitchell, P., & Wikström, P. (2018). Big data, method and the ethics of location: A case study of a hookup app for men who have sex with men. Social Media + Society, 4(2), 1–10. https://doi.org/10.1177/2056305118768299
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  • Mahrt, M., & Scharkow, M. (2013). The value of big data in digital media research. Journal of Broadcasting & Electronic Media. 57(1), 20–33. https://doi.org/10.1080/08838151.2012.761700
  • Mann, M., & Daly, A. (2019). (Big) data and the north-in-south: Australia’s informational imperialism and digital colonialism. Television & New Media, 20(4), 379–395. https://doi.org/10.1177/1527476418806091
  • Margolin, D., & Markowitz, D. M. (2018). A multitheoretical approach to big text data: Comparing expressive and rhetorical logics in yelp reviews. Communication Research, 45(5), 688–718. https://doi.org/10.1177/0093650217719177
  • Nelson, J. L., & Webster, J. G. (2016). Audience currencies in the age of big data. International Journal on Media Management, 18(1), 9–24. https://doi.org/10.1080/14241277.2016.1166430
  • Neuman, W.R., Guggenheim, L., Jang, S. M., & Bae, S. Y. (2014). The dynamics of public attention: Agenda‐setting theory meets big data. Journal of Communication, 64(2), 193–214. https://doi.org/10.1111/jcom.12088
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Communicative Approaches to Big Data: A Systematic Analysis of Web of Science Publications

Yıl 2020, Sayı: 58, 241 - 272, 30.07.2020

Zafer Kıyan Nurcan Törenli

https://doi.org/10.26650/CONNECTIST2020-0002

Öz

This study was designed as a literature review analysis of 62 journal articles that approach big data from a communicative perspective. The study allows researchers interested in the subject to gain an overview of the discussions revolving around big data, as well as the preferred theoretical approaches for big data analysis and the methodologies used. The articles included in the study were analyzed using qualitative content analysis. This method is suitable for literature studies since it allows for systematic coding and qualitative interpretations. The analysis revealed that the articles under examination were divided into two groups with 45 focused directly on big data and 17 indirectly analyzing big data. The articles in the first group generally problematized big data, whereas those in the second group analyzed a communication-based topic or problem using a large data set. This differentiation arises both from big data phenomenon being new in the field of communication and from numerous instrumental and cognitive difficulties and problems that researchers face in this field. This study fills an important gap in Turkish literature on the topic both in terms of its focus and conclusions drawn.

Anahtar Kelimeler

Big data communication studies social media data analysis ethics

Kaynakça

  • Altunışık, R. (2015). Büyük veri: Fırsatlar kaynağı mı yoksa yeni sorunlar yumağı mı? Yıldız Social Science Review, 1(1), 45–76. https://dergipark.org.tr/tr/pub/yssr/issue/21899/235390
  • Andrejevic, M. (2014). The big data divide. International Journal of Communication, 8, 1673–1689.
  • Arsenault, A., H. (2017). The datafication of media: Big data and the media industries. International Journal of Media & Cultural Politics, 13(1-2), 7–24. https://doi.org/10.1386/macp.13.1-2.7_1
  • Arslan, F., & Kahraman, H. (2019). Yapay zekâ tabanlı büyük veri yönetim aracı. Journal of Investigations on Engineering and Technology, 2(1), 8–21. https://dergipark.org.tr/tr/pub/jiet/issue/48409/602203
  • Assarroudi, A., Nabavi, F. H., Armat, M. R., Ebadi, A., & Vaismoradi, M. (2018). Directed qualitative content analysis: The description and elaboration of its underpinning methods and data analysis process. Journal of Research in Nursing, 23(1), 42–55. https://doi.org/10.1177/1744987117741667
  • Athique, A. (2018). The dynamics and potential of big data for audience research. Media, Culture & Society, 40(1), 59–74. https://doi.org/10.1177/0163443717693681
  • Ayvaz, S., & Salman, Y. (2020). Türkiye’de firmaların büyük veri teknolojileri bilinirliği ve kullanımı analizi. Avrupa Bilim ve Teknoloji Dergisi, 14(18), 728–737. https://doi.org/10.31590/ejosat.675247
  • Baruh, L., & Popescu, M. (2017). Big data analytics and the limits of privacy self-managements. New Media & Society, 19(4), 579–596. https://doi.org/10.1177/1461444815614001
  • boyd, d., & Crawford, K. (2012). Critical questions for big data. Information, Communication & Society, 15(5), 662– 679. https://doi.org/10.1080/1369118X.2012.678878
  • Broussard, M. (2016). Big data in practice, enabling computational journalism through code-sharing and reproducible research. Digital Journalism, 4(2), 266–279. https://doi.org/10.1080/21670811.2015.1074863
  • Carpenter, C. J., & Amaravadi, C. S. (2019). A big data approach to assessing the impact of social norms: Reporting one’s exercise to a social media audience. Communication Research, 46(2), 236–249. https://doi. org/10.1177/0093650216657776
  • Chan, A. (2015). Big data interfaces and the problem of inclusion. Media, Culture and Society, 37(7), 1078–1083. https://doi.org/10.1177/0163443715594106
  • Chen, H., & Zhou L. (2018). The myth of big data: Chinese advertising practitioners’ perspective. International Journal of Advertising, 37(4), 633–649. https://doi.org/10.1080/02650487.2017.1340865
  • Cibaroğlu, M., & Yalçınkaya, B. (2019). Belge ve arşiv yönetimi süreçlerinde büyük veri analitiği ve yapay zekâ uygulamaları. Bilgi Yönetimi, 2(1), 44–58. https://doi.org/10.33721/by.570634
  • Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting political orientation and measuring political homophily in twitter using big data. Journal of Communication, 64(2), 317–332. https://doi.org/10.1111/jcom.12084
  • Couldry, N., & Mejias, U. A. (2019). Data colonialism: Rethinking big data’s relation to the contemporary subject. Television & New Media, 20(4), 336–349. https://doi.org./10.1177/1527476418796632
  • Couldry, N., & Turow, J. (2014). Advertising, big data, and the clearance of the public realm: Marketers’ new approaches to the content subsidy. International Journal of Communication, 8, 1710–1726.
  • Crawford, K., Miltner, K., & Gray, M. L. (2014). Critiquing big data: Politics, ethics, epistemology. International Journal of Communication, 8, 1663–1672.
  • Driscoll, K., & Walker, S. (2014). Working within a black box: Transparency in the collection and production of big Twitter data. International Journal of Communication, 8, 1745–1764.
  • Elo, S., & Kyngäs, H. (2008) The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
  • Fairfield, J., & Shtein H. (2014). Big data, big problems: Emerging issues in the ethics of data science and journalism. Journal of Mass Media Ethics, 29, 38–51. https://doi.org/10.1080/08900523.2014.863126
  • Feng, G. C. (2019). A comparative study of the online film ratings of US and Chinese audiences: An analytical approach based on big data. International Communication Gazette, 81(3), 283–302. https://doi. org/10.1177/1748048518767799
  • Filibeli, T. (2019). Big data, artificial intelligence, and machine learning algorithms: A descriptive analysis of the digital threats in the post-truth era. Galatasaray Üniversitesi İletişim Dergisi, 31, 91–110. https://doi. org/10.16878/gsuilet.626260
  • Fuchs, C. (2017a). From digital positivism and administrative big data analytics towards critical digital and social media research! European Journal of Communication, 32(1), 37–49. https://doi.org/10.1177/0267323116682804
  • Fuchs, C. (2017b). Günter Anders’ undiscovered critical theory of technology in the age of big data capitalism. Triple C, 15(2), 582–611.
  • Guo, L., & Vargo, C, J. (2017). Global intermedia agenda setting: A big data analysis of international news flow. Journal of Communication, 67(4), 499–520. https://doi.org/10.1111/jcom.12311
  • Guo, L., & Vargo, C. (2015). The power of message networks: A big-data analysis of the network agenda setting model and issue ownership. Mass Communication and Society, 18(5), 557–576. https://doi.org/10.1080/1520 5436.2015.1045300
  • Guo, L., Vargo, C. J., Pan, Z., Ding, W., & Ishwar, P. (2016). Big social data analytics in journalism and mass communication: Comparing dictionary-based text analysis and unsupervised topic modeling. Journalism & Mass Communication Quarterly, 93(2), 332–359. https://doi.org/10.1177/1077699016639231
  • Halavais, A. (2015). Bigger sociological imaginations: framing big social data theory and methods. Information, Communication & Society, 18(5), 583–594. https://doi.org/10.1080/1369118X.2015.1008543
  • Hammond, P. (2017). From computer-assisted to data-driven: Journalism and big data. Journalism, 18(4), 408– 424. https://doi.org/10.1177/1464884915620205.
  • Hogan, M., & Shepherd, T. (2015). Information ownership and materiality in an age of big data surveillance. Journal of Information Policy, 5, 6–31. https://www.jstor.org/stable/10.5325/jinfopoli.5.2015.0006
  • Hokka, J., & Nelimarkka, M. (2019). Affective economy of national-populist images: Investigating national and transnational online networks through visual big data. New Media & Society, 1–23. https://doi. org/10.1177/1461444819868686
  • Hsieh, H. F., & Shannon, S. E. (2005). Three approaches to qualitative content analysis. Qualitative Health Research, 15(9), 1277–1288. https://doi.org/10.1177/1049732305276687
  • Ingrams, A. (2018). Public values in the age of big data: A public information perspective. Policy and Internet, 11(2), 128–148. https://doi.org/10.1002/poi3.193
  • Japec, F., Kreuter, F., Berg, M., Biemer, P., Decker, P., Lampe, C. … , Lane, J., O’nell, C., & Usher, A. (2015). Big data in survey research. Public Opinion Quarterly, 79(4), 839–880. https://doi.org/10.1093/poq/nfv039
  • Kelly, J. P. (2019). Television by the numbers: The challenges of audience measurement in the age of big data. Convergency: The International Journal of Research into New Media Technologies, 25(1), 113–132. https://doi. org/10.1177/1354856517700854
  • Kim, J., Brossard, D., Scheufele, D. A., & Xenos, M. (2016). ‘Shared’ information in the age of big data: Exploring sentiment expression related to nuclear energy on Twitter. Journalism & Mass Communication Quarterly, 93(2), 430–445. https://doi.org/10.1177/1077699016640715
  • Konitzer, T., Rothschild, D., Hill, S., & Wilbur, K. C. (2019). Using big data and algorithms to determine the effect of geographically targeted advertising on vote intention: Evidence from the 2012 U.S. presidential election. Political Communication, 36(1), 1–16. https://doi.org/10.1080/10584609.2018.1467985
  • Kosterich, A. (2016). Reconfiguring the ‘hits’: The new portrait of television program success in an era of big data. International Journal on Media Management, 18(1), 43–58. https://doi.org/10.1080/14241277.2016.1166431
  • Kshetri, N. (2014). Big Data’s impact on privacy security, and consumer welfare. Telecommunications Policy, 38, 1134–1145. http://dx.doi.org/10.1016/j.telpol.2014.10.002
  • Leckner, S., & Severson P. (2019). Exploring the meaning problem of big and small data through digital method triangulation. Nordicom Review, 40(1), 79–94. https://doi.org/10.2478/nor-2019-0015
  • Lewis, S. C., & Westlund, O. (2014). Big data and journalism: Epistemology, expertise, economics, and ethics. Digital Journalism, 3(3), 447–466. https://doi.org/10.1080/21670811.2014.976418
  • Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in era of big data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media, 57(1), 34–52. https://doi.or g/10.1080/08838151.2012.761702
  • Light, B., Mitchell, P., & Wikström, P. (2018). Big data, method and the ethics of location: A case study of a hookup app for men who have sex with men. Social Media + Society, 4(2), 1–10. https://doi.org/10.1177/2056305118768299
  • Luka, M. E., & Millette, M. (2018). (Re)framing big data: Activating situated knowledges and a feminist ethics of care in social media research. Social Media + Society, 4(2), 1–10. https://doi.org/10.1177/2056305118768297
  • Mahrt, M., & Scharkow, M. (2013). The value of big data in digital media research. Journal of Broadcasting & Electronic Media. 57(1), 20–33. https://doi.org/10.1080/08838151.2012.761700
  • Mann, M., & Daly, A. (2019). (Big) data and the north-in-south: Australia’s informational imperialism and digital colonialism. Television & New Media, 20(4), 379–395. https://doi.org/10.1177/1527476418806091
  • Margolin, D., & Markowitz, D. M. (2018). A multitheoretical approach to big text data: Comparing expressive and rhetorical logics in yelp reviews. Communication Research, 45(5), 688–718. https://doi.org/10.1177/0093650217719177
  • Nelson, J. L., & Webster, J. G. (2016). Audience currencies in the age of big data. International Journal on Media Management, 18(1), 9–24. https://doi.org/10.1080/14241277.2016.1166430
  • Neuman, W.R., Guggenheim, L., Jang, S. M., & Bae, S. Y. (2014). The dynamics of public attention: Agenda‐setting theory meets big data. Journal of Communication, 64(2), 193–214. https://doi.org/10.1111/jcom.12088
  • Panger, G. (2016). Reassessing the Facebook experiment: Critical thinking about the validity of big data research. Information, Communication & Society, 19(8), 1108–1126. https:/doi.org/10.1080/1369118X.2015.1093525
  • Papacharissi, Z. (2015). The unbearable lightness of information and the impossible gravitas of knowledge: Big Data and the makings of a digital orality. Media, Culture & Society, 37(7), 1095–1100. https://doi. org/10.1177/0163443715594103
  • Parasie, S. (2015). Data-driven revelation? Epistemological tensions in investigative journalism in the age of ‘big data’. Digital Journalism, 3(3), 364–380. http://dx.doi.org/10.1080/21670811.2014.976408
  • Park, J., Baek, Y. M., & Cha, M. (2014). Cross‐cultural comparison of nonverbal cues in emoticons on Twitter: Evidence from big data analysis. Journal of Communication, 64(2), 333–354. https://doi.org/10.1111/ jcom.12086
  • Pentzold, C., Brantner, C., & Fölsche, L. (2019). Imagining big data: Illustrations of ‘big data’ in US news articles, 2010–2016. New Media & Society, 21(1), 139–167. https://doi.org/10.1177/1461444818791326
  • Poel, M., Meyer, E. T., & Schroeder, R. (2018). Big data for policymaking: Great expectations, but with limited progress? Policy and Internet, 10(3), 347–367. https://doi.org/10.1002/poi3.176
  • Puschmann, C., & Burgess, J. (2014). Metaphors of big data. International Journal of Communication, 8, 1690– 1709.
  • Qiu, J. L. (2015). Reflections on big data: ‘Just because it is accessible does not make it ethical’. Media, Culture & Society, 37(7), 1089–1094. https://doi.org/10.1177/0163443715594104
  • Quan-Haase, A., & Sloan, L. (2017). Introduction to the handbook of social media research methods: Goals, challenges and innovations. In L. Sloan & A. Quan-Haase (Eds.), The SAGE Handbook of Social Media Research Methods (pp. 1–9). London: SAGE Publications.
  • Rubin, V. L. (2017). Deception detection and rumor debunking for social media. In L. Sloan & A. Quan-Haase (Eds.), The SAGE Handbook of Social Media Research Methods (pp. 342–363). London: SAGE Publications.
  • Sandoval-Martin, D., & La-Rosa, L. (2018). Big data as a differentiating sociocultural element of data journalism: the perception of data journalist and experts. Communication & Society, 31(4), 193–209. https://doi. org/10.15581/003.31.4.193-208
  • Schreier, M. (2014). Qualitative content analysis. In U. Flick (Ed.), The SAGE Handbook of Qualitative Data Analysis (pp. 170–183). Thousand Oaks, CA: SAGE Publications.
  • Shin, D. -H. (2015). Demystifying big data: Anatomy of big data developmental process. Telecommunications Policy, 40, 837–-854. http://dx.doi.org/10.1016/j.telpol.2015.03.007
  • Statista (2020, January 10). Forecast of big data market size, based on revenue, from 2011 to 2027. Retrieved from https://www.statista.com/statistics/254266/global-big-data-market-forecast/
  • Stockmann, D. (2018). Toward area-smart data science: Critical questions for working with big data from China. Policy & Internet, 10(4), 393–-414. https://doi.org/10.1002/poi3.192
  • Tandoc Jr., E. C., & Oh, S. K. (2017). Small departures, big continuities? Journalism Studies, 18(8), 997–1015. https://doi.org/10.1080/1461670X.2015.1104260
  • Thatcher, J. (2014). Living on fumes: Digital footprints, data fumes, and the limitations of spatial big data. International Journal of Communication, 8, 1765–1783.
  • Vargo, C. J., & Guo, L. (2017). Networks, big data, and intermedia agenda setting: An analysis of traditional, partisan, and emerging online U.S. news. Journalism & Mass Communication Quarterly, 94(4), 1031–1055. https://doi.org/10.1177/1077699016679976
  • Vargo, C. J., Guo, L., & Amazeen, M. A. (2018). The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016. New Media & Society, 20(5), 2028–2049. https://doi. org/10.1177/1461444817712086
  • Veglis, A., & Maniou, T. A. (2018). The mediated data model of communication flow: Big data and data journalism. KOME – An International Journal of Pure Communication Inquiry, 6(2), 32–43. http://doi.org/10.17646/ KOME.2018.23
  • Willson M., & Leaver, T. (2015). Zynga’s farmville, social games, and the ethics of big data mining. Communication Research and Practice, 1(2), 147–158. https://doi.org/10.1080/22041451.2015.1048039
  • Yang, F. (2016). Storytelling in the age of big data: Hong Kong students’ readiness and attitude towards data journalism. Asia Pacific Media Educator, 26(2), 148–162. https://doi.org/10.1177/1326365X16673168
  • Yeh, C. -L. (2018). Pursuing consumer empowerment in the age of big data: A comprehensive regulatory framework for data brokers. Telecomunications Policy, 42(4), 282–292. https://doi.org/10.1016/j. telpol.2017.12.001
  • Zhang, Y., & Wildemuth, B. M. (2009). Qualitative analysis of content. In B. Wildemuth (Ed.), Applications of social research methods to questions in information and library science (pp. 308–319). Westport, CT: Libraries Unlimited.
  • Zimmer, M. (2018). Addressing conceptual gaps in big data research ethics: An application of contextual integrity. Social Media + Society, 4(2), 1–11. https://doi.org/10.1177/2056305118768300
Toplam 75 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İletişim ve Medya Çalışmaları
Bölüm Araştırma Makaleleri
Yazarlar

Zafer Kıyan 0000-0002-7318-5419

Nurcan Törenli Bu kişi benim 0000-0001-8520-3138

Yayımlanma Tarihi 30 Temmuz 2020
Gönderilme Tarihi 10 Ocak 2020
Yayımlandığı Sayı Yıl 2020 Sayı: 58

Kaynak Göster

APA Kıyan, Z., & Törenli, N. (2020). Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi. Connectist: Istanbul University Journal of Communication Sciences(58), 241-272. https://doi.org/10.26650/CONNECTIST2020-0002
AMA Kıyan Z, Törenli N. Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi. Connectist: Istanbul University Journal of Communication Sciences. Temmuz 2020;(58):241-272. doi:10.26650/CONNECTIST2020-0002
Chicago Kıyan, Zafer, ve Nurcan Törenli. “Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi”. Connectist: Istanbul University Journal of Communication Sciences, sy. 58 (Temmuz 2020): 241-72. https://doi.org/10.26650/CONNECTIST2020-0002.
EndNote Kıyan Z, Törenli N (01 Temmuz 2020) Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi. Connectist: Istanbul University Journal of Communication Sciences 58 241–272.
IEEE Z. Kıyan ve N. Törenli, “Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi”, Connectist: Istanbul University Journal of Communication Sciences, sy. 58, ss. 241–272, Temmuz 2020, doi: 10.26650/CONNECTIST2020-0002.
ISNAD Kıyan, Zafer - Törenli, Nurcan. “Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi”. Connectist: Istanbul University Journal of Communication Sciences 58 (Temmuz 2020), 241-272. https://doi.org/10.26650/CONNECTIST2020-0002.
JAMA Kıyan Z, Törenli N. Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi. Connectist: Istanbul University Journal of Communication Sciences. 2020;:241–272.
MLA Kıyan, Zafer ve Nurcan Törenli. “Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi”. Connectist: Istanbul University Journal of Communication Sciences, sy. 58, 2020, ss. 241-72, doi:10.26650/CONNECTIST2020-0002.
Vancouver Kıyan Z, Törenli N. Büyük Veriye İletişimsel Yaklaşımlar: Web of Science Yayınlarının Sistematik Bir Analizi. Connectist: Istanbul University Journal of Communication Sciences. 2020(58):241-72.
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