İletişim Biliminde Büyük Verinin Yükselişi: Literatürün Bibliyometrik Haritalaması

Abstract

Günümüz dijital dünyası, iletişim ve bilgi teknolojilerindeki ilerlemelerle karakterize edilir. İnternet teknolojisi, sosyal medya platformları, sosyal ağ siteleri, arama motorları, bloglar, forumlar, web siteleri ve e-postalar gibi çeşitli iletişim kanalları sunmaktadır. Bu kanalların kullanıcıları, sosyal bilimlerdeki iletişim çalışmalarında büyük verinin ana kaynağı olan dijital izler yaratmaktadır. tanımlanmış neden sonuç ilişkilerinden ziyade mevcut durumları tam olarak anlamak için nicel göstergeler sağlamaktadır. Bu çalışma, 2014-2018 yılları arasında sosyal bilimlerde “büyük veri ve iletişim” konusundaki çalışmaları incelemeyi amaçlamaktadır. Web of Science Sosyal Bilimler Atıf Dizini dergileri araştırma alanının sistematik ve kantitatif analizini sunmak için seçilmiştir. Bibliyometrik analiz sonuçları, daha önce bu özel konuyla ilgili diğer incelemelerde ele alınmayan iletişim alanında büyük verinin kullanımı ve yayılımı hakkında bilgiler vermektedir. Bibliyometrik araçlar, sosyal bilimler kapsamında büyük veri ve iletişim çalışmalarındaki araştırma kümelerini, temel araştırma konularını, ağ ve işbirliği modellerini belirlemeye yardımcı olmuştur. Alanın bu bibliyometrik haritalaması, zaman içindeki çalışmaların evrimini görsel olarak gösterir ve takipçilere yönelik mevcut araştırma ilgi alanlarını ve gelecekteki yönelimleri tanımlar.

Keywords

• Büyük veri
• iletişim
• sosyal medya
• bibliyometrik analiz
• ortak atıf ağı

The Rise of Big Data in Communication Sciences: A Bibliometric Mapping of the Literature

Abstract

Today’s digital world is characterized by advances in communication and information technologies. Internet technology provides a variety of communication channels like social media platforms, social network sites, search engines, blogs, forums, websites and e-mails. The users of these channels create digital traces which are the main source of big data in communication studies in social sciences. Big social data analytics in communication studies provides quantitative indicators to fully understand current situations rather than pre-defined cause and effect relationships. This study aims to investigate the studies in “big data and communication” in social sciences between the years 2014 and 2018. Web of Science Social Science Citation Index journals are selected to present systematic and quantitative analysis of the research field. Bibliometric analysis results provide insights about big data usage and expansion in the communication field not previously grasped by other reviews on this special topic. Bibliometric tools helped to identify research clusters, key research topics, and network and collaboration patterns in big data and communication studies in a social sciences context. This bibliometric mapping of the field visually illustrates the evolution of studies over time and identifies current research interests and future directions for the followers.

Keywords

• Big data
• communication
• social media
• bibliometric analysis
• co-citation network

Supporting Institution

The authors declared that this study has received no financial support.

References

1. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
2. Bello-Orgaz, G., Jung, J. J., & Camacho, D. (2016). Social big data: Recent achievements and new challenges. Information Fusion, 28, 45-59. https://doi.org/10.1016/j.inffus.2015.08.005
3. Borgman, C. L., & Furner, J. (2002). Scholarly communication and bibliometrics. Annual review of information science and technology, 36(1), 2-72. Retrieved from https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/ aris.1440360102
4. Borgman, C. L., & Rice, R. E. (1992). The convergence of information science and communication: A bibliometric analysis. Journal of the American Society for Information Science, 43(6), 397-411. Retrieved from https://www. dhi.ac.uk/san/waysofbeing/data/health-jones-borgman-1992.pdf
5. Bornmann, L., Mutz, R., & Daniel, H. D. (2008). Are there better indices for evaluation purposes than the h index? A comparison of nine different variants of the h index using data from biomedicine. Journal of the American Society for Information Science and technology, 59(5), 830-837. https://doi.org/10.1002/asi.20806
6. 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
7. Calhoun, C. (2011). Plenary| Communication as Social Science (and More). International Journal of Communication, 5, 18. https://doi.org/10.1590/S1809- 58442012000100014
8. Cappella, J. N. (2017). Vectors into the future of mass and interpersonal communication research: Big data, social media, and computational social science. Human Communication Research, 43(4), 545-558. https://doi. org/10.1111/hcre.12114

9. Chae, B. K. (2015). Insights from hashtag# supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247-259. https://doi.org/10.1016/j.ijpe.2014.12.037
10. Chang, R. M., Kauffman, R. J., & Kwon, Y. (2014). Understanding the paradigm shift to computational social science in the presence of big data. Decision Support Systems, 63, 67-80. https://doi.org/10.1016/j.dss.2013.08.008
11. Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166. https://doi.org/10.1016/j.joi.2010.10.002
12. Coddington, M. (2015). Clarifying journalism’s quantitative turn: A typology for evaluating data journalism, computational journalism, and computer-assisted reporting. Digital journalism, 3(3), 331-348. https://doi.or g/10.1080/21670811.2014.976400
13. 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
14. Conway, B. A., Kenski, K., & Wang, D. (2015). The rise of Twitter in the political campaign: Searching for intermedia agenda-setting effects in the presidential primary. Journal of Computer-Mediated Communication, 20(4), 363-380. https://doi.org/10.1111/jcc4.12124
15. Demchenko, Y., Grosso, P., De Laat, C., & Membrey, P. (2013, May). Addressing big data issues in scientific data infrastructure. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 48-55). IEEE. https://doi.org/10.1109/CTS.2013.6567203
16. Dumbill, E. (2013). Making sense of big data. https://doi.org/10.1089/big.2012.1503
17. Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131-152. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.395.9064&rep=rep1&type=pdf
18. Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., ... & Weeg, C. (2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26(2), 159-169. https://doi.org/10.1177/0956797614557867
19. Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses. The FASEB journal, 22(2), 338-342. https://doi.org/10.1096/ fj.07-9492LSF
20. Feeley, T. H. (2008). A bibliometric analysis of communication journals from 2002 to 2005. Human Communication Research, 34(3), 505-520. https://doi.org/10.1111/j.1468-2958.2008.00330.x
21. Gan, C., & Wang, W. (2015). Research characteristics and status on social media in China: A bibliometric and co-word analysis. Scientometrics, 105(2), 1167-1182. https://doi.org/10.1007/s11192-015-1723-2
22. Golder, S. A., & Macy, M. W. (2014). Digital footprints: Opportunities and challenges for online social research. Annual Review of Sociology, 40, 129-152. https://doi.org/10.1146/annurev-soc-071913-043145
23. Hasan, S., & Ukkusuri, S. V. (2014). Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C: Emerging Technologies, 44, 363-381. https://doi.org/10.1016/j. trc.2014.04.003
24. Ishikawa, H. (2015). Social big data mining. Florida, USA: CRC Press.
25. Kalantari, A., Kamsin, A., Kamaruddin, H. S., Ebrahim, N. A., Gani, A., Ebrahimi, A., & Shamshirband, S. (2017). A bibliometric approach to tracking big data research trends. Journal of Big Data, 4(1), 30. https://doi. org/10.1186/s40537-017-0088-1
26. Kaur, N., & Sood, S. K. (2017). Dynamic resource allocation for big data streams based on data characteristics (5Vs). International Journal of Network Management, 27(4), 1-16. https://doi.org/10.1002/nem.1978
27. Kollanyi, B., Howard, P., & Woolley, S. C. (2016). Bots and automation over Twitter during the first U.S. presidential debate. Comprop Data Memo 2016.1. Retrieved from https://regmedia.co.uk/2016/10/19/data-memo-first-presidential-debate.pdf
28. Koseoglu, M. A., Rahimi, R., Okumus, F., & Liu, J. (2016). Bibliometric studies in tourism. Annals of Tourism Research, 61, 180-198. https://doi.org/10.1016/j.annals.2016.10.006
29. Kramer, A. D., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788-8790. https://doi. org/10.1073/pnas.1320040111
30. Kreiss, D., & Jasinski, C. (2016). The tech industry meets presidential politics: Explaining the Democratic Party’s technological advantage in electoral campaigning, 2004– 2012. Political Communication, 33(4), 544-562. https://doi.org/10.1080/10584609.2015.1121941
31. Larson, K., & Watson, R. (2011). The value of social media: toward measuring social media strategies. In Proceedings of ICIS 2011, Shanghai, China.
32. Lee, K., Jung, H., & Song, M. (2016). Subject–method topic network analysis in communication studies. Scientometrics, 109(3), 1761-1787. https://doi.org/10.1007/s11192- 016-2135-7
33. Leeflang, P. S., Verhoef, P. C., Dahlström, P., & Freundt, T. (2014). Challenges and solutions for marketing in a digital era. European Management Journal, 32(1), 1-12. https://doi.org/10.1016/j.emj.2013.12.001
34. Leung, X. Y., Sun, J., & Bai, B. (2017). Bibliometrics of social media research: A co-citation and co-word analysis. International Journal of Hospitality Management, 66, 35-45. https://doi.org/10.1016/j.ijhm.2017.06.012
35. Lewis, S. C., Zamith, R., & Hermida, A. (2013). Content analysis in an 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
36. Manovich, L. (2011). Trending: The promises and the challenges of big social data. Debates in the Digital Humanities, 2, 460-475. https://doi.org/10.5749/minnesota/9780816677948.003.0047
37. Marine-Roig, E. (2017). Measuring destination image through travel reviews in search engines. Sustainability, 9(8), 1425. https://doi.org/10.3390/su9081425
38. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: the management revolution. Harvard Business Review, 90(10), 60-68. Retrieved from https://wiki.uib.no/info310/images/4/4c/ McAfeeBrynjolfsson2012-BigData-TheManagementRevolution-HBR.pdf
39. McBurney, M. K., & Novak, P. L. (2002, September). What is bibliometrics and why should you care?. In Proceedings. IEEE International Professional Communication Conference (pp. 108-114). IEEE. Retrieved from https:// ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=1049094
40. Mishra, D., Gunasekaran, A., Papadopoulos, T., & Childe, S. J. (2018). Big Data and supply chain management: a review and bibliometric analysis. Annals of Operations Research, 270(1-2), 313-336. https://doi.org/10.1007/ s10479-016-2236-y
41. O’Leary, D. E. (2013). Artificial intelligence and big data. IEEE Intelligent Systems, 28(2), 96-99. https://doi. org/10.1109/MIS.2013.39
42. Olshannikova, E., Olsson, T., Huhtamäki, J., & Kärkkäinen, H. (2017). Conceptualizing big social data. Journal of Big Data, 4(1), 3. https://doi.org/10.1186/s40537-017-0063-x
43. Oracle. (2012). Oracle: Big Data for the Enterprise. Retrieved from http://www.oracle.com/us/products/database/ big-data-for-enterprise-519135.pdf
44. Özköse, H., Arı, E. S., & Gencer, C. (2015). Yesterday, today and tomorrow of big data. Procedia-Social and Behavioral Sciences, 195, 1042-1050. https://doi.org/10.1016/j.sbspro.2015.06.147
45. Paris, C., & Wan, S. (2011, May). Listening to the community: social media monitoring tasks for improving government services. In CHI’11 Extended Abstracts on Human Factors in Computing Systems (pp. 2095-2100). ACM. Retreived from https://dl.acm.org/doi/pdf/10.1145/1979742.1979878
46. Park, G., Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Kosinski, M., Stillwell, D. J., Ungar, L. H., & Seligman, M. E. P. (2015). Automatic personality assessment through social media language. Journal of Personality and Social Psychology, 108(6), 934. https://doi.org/10.1037/pspp0000020
47. Parks, M. R. (2014). Big data in communication research: Its contents and discontents. Journal of Communication, 64(2), 355-360. https://doi.org/10.1111/jcom.12090
48. Pritchard, A. (1969). Statistical bibliography or bibliometrics. Journal of documentation, 25(4), 348-349.
49. Rauchfleisch, A. (2017). The public sphere as an essentially contested concept: A co-citation analysis of the last 20 years of public sphere research. Communication and the Public, 2(1), 3-18. https://doi. org/10.1177/2057047317691054
50. Russell Neuman, W., Guggenheim, L., Mo Jang, S., & 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
51. Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19, 40. Retrieved from https:// vivomente.com/wp-content/uploads/2016/04/big-data-analytics-white-paper.pdf
52. Rust, R. T., & Huang, M. H. (2014). The service revolution and the transformation of marketing science. Marketing Science, 33(2), 206-221. https://doi.org/10.1287/mksc.2013.0836
53. Sainaghi, R., Phillips, P., Baggio, R., & Mauri, A. (2018). Cross-citation and authorship analysis of hotel performance studies. International Journal of Hospitality Management, 73, 75-84. https://doi.org/10.1016/j.ijhm.2018.02.004
54. Schroeder, R. (2016). Big data and communication research. Oxford Research Encyclopedia of Communication. https://doi.org/10.1093/acrefore/9780190228613.013.276
55. Shah, D. V., Cappella, J. N., & Neuman, W. R. (2015). Toward computational social science: Exploiting big data in the digital age. Philadelphia PA: The ANNALS of the American Academy of Political and Social Science.
56. Shelton, T., Poorthuis, A., & Zook, M. (2015). Social media and the city: Rethinking urban socio-spatial inequality using user-generated geographic information. Landscape and urban planning, 142, 198-211. https://doi. org/10.1016/j.landurbplan.2015.02.020
57. Shelton, T., Poorthuis, A., Graham, M., & Zook, M. (2014). Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’. Geoforum, 52, 167-179. https://doi.org/10.1016/j. geoforum.2014.01.006
58. Stieglitz, S., & Dang-Xuan, L. (2013). Social media and political communication: a social media analytics framework. Social network analysis and mining, 3(4), 1277-1291. https://doi.org/10.1007/s13278-012-0079-3
59. Stieglitz, S., Brockmann, T., & Dang-Xuan, L. (2012, July). Usage Of Social Media For Political Communication. In PACIS (p. 22). Retrieved from https://aisel.aisnet.org/pacis2012/22
60. Trilling, D. (2017). Big data, analysis of. In J. Matthes, C. S. Davis, R. F. Potter (Eds.) International Encyclopedia of Communication Research Methods. New York, NY: Wiley Online Library
61. Tsou, M. H. (2011, January). Mapping cyberspace: Tracking the spread of ideas on the internet. In Proceeding of the 25th International Cartographic Conference. Retrieved from https://icaci.org/files/documents/ICC_ proceedings/ICC2011/Oral%20Presentations%20PDF/D3-Internet,%20web%20services%20and%20 web%20mapping/CO-354.pdf
62. van Atteveldt, W., & Peng, T. Q. (2018). When communication meets computation: Opportunities, challenges, and pitfalls in computational communication science. Communication Methods and Measures, 12(2-3), 81- 92. https://doi.org/10.1080/19312458.2018.1458084
63. Vogel, R., & Güttel, W. H. (2013). The dynamic capability view in strategic management: A bibliometric review. International Journal of Management Reviews, 15(4), 426-446. https://doi.org/10.1111/ijmr.12000
64. We are Social and Hootsuite, Global Digital 2019 Report, Retrieved from https://dijilopedi.com/2019-turkiye-internet-kullanim-ve-sosyal-medya-istatistikleri/
65. White, M. (2012). Digital workplaces: Vision and reality. Business Information Review, 29(4), 205-214. https://doi. org/10.1177/0266382112470412
66. Xiang, Z., Du, Q., Ma, Y., & Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51-65. https://doi. org/10.1016/j.tourman.2016.10.001
67. Xiang, Z., Schwartz, Z., Gerdes Jr, J. H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44, 120-130. https://doi. org/10.1016/j.ijhm.2014.10.013
68. Youyou, W., Kosinski, M., & Stillwell, D. (2015). Computer-based personality judgments are more accurate than those made by humans. Proceedings of the National Academy of Sciences, 112(4), 1036-1040. https://doi. org/10.1073/pnas.1418680112
69. Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429-472. https://doi.org/10.1177/1094428114562629