Araştırma Makalesi
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MEASURING THE EFFECTIVENESS OF SOCIAL MEDIA: AN INVESTIGATION OF COMPANIES’ TWITTER USE

Yıl 2020, Cilt: 20 Sayı: 1, 121 - 146, 24.03.2020
https://doi.org/10.11616/basbed.v20i53206.644619

Öz

The effective use of social media, which has become an indispensable tool for companies due to the advantages it has achieved in reaching the target group, has become an important issue. In this study, the feeds of Twitter users about competitors in the cosmetics, marketplace and electronic sectors and the feeds shared by the companies’ corporate Twitter accounts during February 2018 were analysed using Social Media Mining method. A success ranking was made by evaluating the measurement criteria of Twitter effectiveness consisting of number of tweets, tweet value, follower gain, number of responses, number of retweets and number of likes and it was determined that the most successful company was from cosmetics and the lowest ranked company was from electronic sector. In order to determine tweet value, Sentiment Analysis was carried out and the number of positive tweets was found to be higher for the cosmetics companies.

Kaynakça

  • Ayata, D., Saraçlar, M. ve Özgür, A. (2017), Turkish Tweet Sentiment Analysis with Word Embedding and Machine Learning, Proceedings of the 25th Signal Processing and Communications Applications Conference (SIU), s.1-4.
  • Bonzanini, M. (2016), Mastering Social Media Mining with Python, Birmingham: Packt Publishing.
  • Boyd, D. M. ve Ellison, N. B. (2007), Social Network Sites: Definition, History, and Scholarship, Journal of Computer-Mediated Communication, 13(1), s.210-230.
  • Can, U. ve Alatas, B. (2017), Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi, International Journal of Pure and Applied Sciences, 3(1), s.75-111.
  • Çoban, Ö., Özyer, B. ve Özyer, G. T. (2015), Sentiment Analysis for Turkish Twitter Feeds, Proceedings of the 23rd Signal Processing and Communications Applications Conference, s.2388-2391.
  • Danneman, N. ve Heimann R. (2014), Social Media Mining with R, Packt Publishing Ltd.
  • Evans, D. (2008), Social Media Marketing: An Hour a Day, Willey Publishing.
  • Freelon, D. (2013), ReCal OIR: Ordinal, Interval, and Ratio Intercoder Reliability as a Web Service, International Journal of Internet Science, Vol.8, No:1, ss.10-16.
  • Gunelius, S. (2011), 30 Minute Social Media Marketing: Step By Step Techniques to Spread The Words About Your Business, New York: McGraw Hill.
  • Hoffman, D. L. ve Fodor, M. (2010), Can You Measure the ROI of Your Social Media Marketing?, MIT Sloan Management Review, 52(1), s.41-49.
  • İşeri, İ., Atasoy, Ö. F. ve Alçiçek, H. (2017), Sentiment Classification of Social Media Data for Telecommunication Companies in Turkey, Proceedings of the International Conference on Computer Science and Engineering (UBMK), s.1015-1019.
  • Java, A., Song, X., Finin, T. ve Tseng, B. (2007), Why We Twitter: Understanding Microblogging Usage and Communities, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, s.56-65.
  • Kaplan, A. M. ve Haenlein, M. (2010), Users of The World, Unite! The Challenges and Opportunities of Social Media, Business Horizons, 53(1), s.59-68.
  • Kietzmann, J. H., Hermkens, K., Mccarthy, I. P. ve Bruno S. Silvestre (2011), Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media, Business Horizons, 54(3), s.241-251.
  • Kwak, H., Lee, C., Park, H. ve Moon, S. (2010), What is Twitter, A Social Network or A News Media?, Proceedings of the 19th International Conference on World Wide Web, s.591-600.
  • Lee, K., Oh, W. Y. ve Kim, N. (2013), Social Media for Socially Responsible Firms: Analysis of Fortune 500’s Twitter Profiles and Their CSR/CSIR Ratings, Journal of Business Ethics, 118(4), s.791-806.
  • Lietsala, K. ve Sirkkunen, E. (2008), Social Media: Introduction to The Tools And Processes of Participatory Economy, Finland: University Of Tampere.
  • Liu, B. ve Zhang, L. (2012), A Survey of Opinion Mining and Sentiment Analysis. Mining Text Data, (eds.) Charu C. Aggarwal, ChengXiang Zhai, Springer, Boston, MA, s.415-463.
  • Liu, H., Morstatter, F., Tang, J. ve Zafarani R. (2016), The Good, The Bad, And The Ugly: Uncovering Novel Research Opportunities in Social Media Mining, International Journal of Data Science and Analytics, 1(3-4), s.137-143.
  • Lomborg, S. ve Bechmann, A. (2014), Using APIs for Data Collection on Social Media, The Information Society, 30(4), s.256-265.
  • Nili, Alireza, Tate, M. ve Barros, A. (2017), A Critical Analysis of Inter-Coder Reliability Methods in Information Systems Research, Proceedings of the Australasian Conference on Information Systems, s.1-11.
  • Ravindran, S. K. ve Garg, V. (2015). Mastering Social Media Mining with R, Birmingham: Packt Publishing.
  • Russell, M. A. (2013), Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, O'Reilly Media, Inc.
  • Safko, L. (2012), The Social Media Bible: Tactics, Tools and Strategies for Business Success (3rd ed), John Wiley&Son.
  • Saravanakumar, M. ve Suganthalakshmi, T. (2012), Social Media Marketing. Life Science Journal, 9(4), s.4444-4451.
  • Sterne, Jim (2010), Social Media Metrics: How to Measure and Optimize Your Marketing Investment, John Wiley&Sons.
  • Stieglitz, S., Dang-Xuan, L., Bruns, A. ve Neuberger, C. (2014), Social Media Analytics. Business & Information Systems Engineering, 6(2), s.89-96.
  • Weinberg, T. (2009), The New Community Rules: Marketing on The Social Web, O'Reilly Media Inc.
  • Zafarani, R., Abbasi, M. A. ve Liu, H. (2014). Social Media Mining: An Introduction. New York: Cambridge University Press.

SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME

Yıl 2020, Cilt: 20 Sayı: 1, 121 - 146, 24.03.2020
https://doi.org/10.11616/basbed.v20i53206.644619

Öz

Bu çalışmanın amacı farklı sektörlerde faaliyet gösteren lider rakip firmaların sosyal medya etkinliklerinin ölçülmesidir. Bu kapsamda, 2018 Şubat ayı boyunca Twitter kullanıcılarının kozmetik, pazaryeri ve elektronik sektöründe faaliyet gösteren rakip firmalar hakkında yaptıkları paylaşımlar ve bu firmaların kurumsal Twitter hesaplarından yapmış oldukları paylaşımlar Sosyal Medya Madenciliği yöntemi ile analiz edilmiştir. Firmalar hakkındaki tweet sayısı, tweet değeri (olumlu, olumsuz, nötr), takipçi kazanımı, yanıt sayısı, retweet sayısı ve beğeni sayısı başlıklarından oluşan Twitter etkinliğinin ölçüm kriterleri ele alınarak bir başarı sıralaması yapılmış ve en başarılı firmanın kozmetik, en düşük sıralamaya sahip firmanın ise elektronik sektöründen olduğu tespit edilmiştir. Tweet değerini saptayabilmek için Duygu Analizi gerçekleştirilmiştir ve olumlu tweet oranının kozmetik firmaları için daha fazla olduğu sonucuna ulaşılmıştır.

Kaynakça

  • Ayata, D., Saraçlar, M. ve Özgür, A. (2017), Turkish Tweet Sentiment Analysis with Word Embedding and Machine Learning, Proceedings of the 25th Signal Processing and Communications Applications Conference (SIU), s.1-4.
  • Bonzanini, M. (2016), Mastering Social Media Mining with Python, Birmingham: Packt Publishing.
  • Boyd, D. M. ve Ellison, N. B. (2007), Social Network Sites: Definition, History, and Scholarship, Journal of Computer-Mediated Communication, 13(1), s.210-230.
  • Can, U. ve Alatas, B. (2017), Duygu Analizi ve Fikir Madenciliği Algoritmalarının İncelenmesi, International Journal of Pure and Applied Sciences, 3(1), s.75-111.
  • Çoban, Ö., Özyer, B. ve Özyer, G. T. (2015), Sentiment Analysis for Turkish Twitter Feeds, Proceedings of the 23rd Signal Processing and Communications Applications Conference, s.2388-2391.
  • Danneman, N. ve Heimann R. (2014), Social Media Mining with R, Packt Publishing Ltd.
  • Evans, D. (2008), Social Media Marketing: An Hour a Day, Willey Publishing.
  • Freelon, D. (2013), ReCal OIR: Ordinal, Interval, and Ratio Intercoder Reliability as a Web Service, International Journal of Internet Science, Vol.8, No:1, ss.10-16.
  • Gunelius, S. (2011), 30 Minute Social Media Marketing: Step By Step Techniques to Spread The Words About Your Business, New York: McGraw Hill.
  • Hoffman, D. L. ve Fodor, M. (2010), Can You Measure the ROI of Your Social Media Marketing?, MIT Sloan Management Review, 52(1), s.41-49.
  • İşeri, İ., Atasoy, Ö. F. ve Alçiçek, H. (2017), Sentiment Classification of Social Media Data for Telecommunication Companies in Turkey, Proceedings of the International Conference on Computer Science and Engineering (UBMK), s.1015-1019.
  • Java, A., Song, X., Finin, T. ve Tseng, B. (2007), Why We Twitter: Understanding Microblogging Usage and Communities, Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, s.56-65.
  • Kaplan, A. M. ve Haenlein, M. (2010), Users of The World, Unite! The Challenges and Opportunities of Social Media, Business Horizons, 53(1), s.59-68.
  • Kietzmann, J. H., Hermkens, K., Mccarthy, I. P. ve Bruno S. Silvestre (2011), Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media, Business Horizons, 54(3), s.241-251.
  • Kwak, H., Lee, C., Park, H. ve Moon, S. (2010), What is Twitter, A Social Network or A News Media?, Proceedings of the 19th International Conference on World Wide Web, s.591-600.
  • Lee, K., Oh, W. Y. ve Kim, N. (2013), Social Media for Socially Responsible Firms: Analysis of Fortune 500’s Twitter Profiles and Their CSR/CSIR Ratings, Journal of Business Ethics, 118(4), s.791-806.
  • Lietsala, K. ve Sirkkunen, E. (2008), Social Media: Introduction to The Tools And Processes of Participatory Economy, Finland: University Of Tampere.
  • Liu, B. ve Zhang, L. (2012), A Survey of Opinion Mining and Sentiment Analysis. Mining Text Data, (eds.) Charu C. Aggarwal, ChengXiang Zhai, Springer, Boston, MA, s.415-463.
  • Liu, H., Morstatter, F., Tang, J. ve Zafarani R. (2016), The Good, The Bad, And The Ugly: Uncovering Novel Research Opportunities in Social Media Mining, International Journal of Data Science and Analytics, 1(3-4), s.137-143.
  • Lomborg, S. ve Bechmann, A. (2014), Using APIs for Data Collection on Social Media, The Information Society, 30(4), s.256-265.
  • Nili, Alireza, Tate, M. ve Barros, A. (2017), A Critical Analysis of Inter-Coder Reliability Methods in Information Systems Research, Proceedings of the Australasian Conference on Information Systems, s.1-11.
  • Ravindran, S. K. ve Garg, V. (2015). Mastering Social Media Mining with R, Birmingham: Packt Publishing.
  • Russell, M. A. (2013), Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, O'Reilly Media, Inc.
  • Safko, L. (2012), The Social Media Bible: Tactics, Tools and Strategies for Business Success (3rd ed), John Wiley&Son.
  • Saravanakumar, M. ve Suganthalakshmi, T. (2012), Social Media Marketing. Life Science Journal, 9(4), s.4444-4451.
  • Sterne, Jim (2010), Social Media Metrics: How to Measure and Optimize Your Marketing Investment, John Wiley&Sons.
  • Stieglitz, S., Dang-Xuan, L., Bruns, A. ve Neuberger, C. (2014), Social Media Analytics. Business & Information Systems Engineering, 6(2), s.89-96.
  • Weinberg, T. (2009), The New Community Rules: Marketing on The Social Web, O'Reilly Media Inc.
  • Zafarani, R., Abbasi, M. A. ve Liu, H. (2014). Social Media Mining: An Introduction. New York: Cambridge University Press.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makaleleri
Yazarlar

Büşra Ayan 0000-0002-5212-2144

Mustafa Can 0000-0002-7786-5198

Umman Tuğba Gürsoy 0000-0002-5143-4058

Yayımlanma Tarihi 24 Mart 2020
Gönderilme Tarihi 9 Kasım 2019
Yayımlandığı Sayı Yıl 2020 Cilt: 20 Sayı: 1

Kaynak Göster

APA Ayan, B., Can, M., & Gürsoy, U. T. (2020). SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 20(1), 121-146. https://doi.org/10.11616/basbed.v20i53206.644619
AMA Ayan B, Can M, Gürsoy UT. SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME. ASBİ. Mart 2020;20(1):121-146. doi:10.11616/basbed.v20i53206.644619
Chicago Ayan, Büşra, Mustafa Can, ve Umman Tuğba Gürsoy. “SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME”. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 20, sy. 1 (Mart 2020): 121-46. https://doi.org/10.11616/basbed.v20i53206.644619.
EndNote Ayan B, Can M, Gürsoy UT (01 Mart 2020) SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 20 1 121–146.
IEEE B. Ayan, M. Can, ve U. T. Gürsoy, “SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME”, ASBİ, c. 20, sy. 1, ss. 121–146, 2020, doi: 10.11616/basbed.v20i53206.644619.
ISNAD Ayan, Büşra vd. “SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME”. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 20/1 (Mart 2020), 121-146. https://doi.org/10.11616/basbed.v20i53206.644619.
JAMA Ayan B, Can M, Gürsoy UT. SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME. ASBİ. 2020;20:121–146.
MLA Ayan, Büşra vd. “SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME”. Bolu Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, c. 20, sy. 1, 2020, ss. 121-46, doi:10.11616/basbed.v20i53206.644619.
Vancouver Ayan B, Can M, Gürsoy UT. SOSYAL MEDYA ETKİNLİĞİNİN ÖLÇÜMÜ: FİRMALARIN TWITTER KULLANIMINA İLİŞKİN BİR İNCELEME. ASBİ. 2020;20(1):121-46.

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E-posta: sbedergi@ibu.edu.tr