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ANALYZING TWITTER DATA OF FIRMS WITH SOCIAL MEDIA MINING

Year 2019, Issue: 23, 237 - 256, 09.04.2019
https://doi.org/10.18092/ulikidince.475092

Abstract

This study aims to determine whether Twitter data of
the firms has a significant correspondence with respect to the firms, to
cluster Twitter feeds of the firms and to find out which cluster has the
maximum interaction through analyzing the Twitter data of the rival firms
operating in different sectors. In this context, Twitter data shared by
competitors operating in the cosmetics, electronics and marketplace sectors
during 2017 were analyzed by following the process of Social Media Mining. The
significant correspondence of Twitter variables of the firms was determined by
the Correspondence Analysis. Twitter feeds of the firms were clustered with
categories “Special Offer”, “Competition & Event”, “Product”, “Social”,
“Support & Feedback” and “Special Interaction” by using a number of Text
Mining pre-processing methods. Since the majority of the interactions obtained
by the firms came from the minority of the feeds, which cluster received more
interaction was analyzed with the help of the Pareto Principle.

References

  • af Rosenborg, K., Christina, D., Buhl-Andersen, I., Nilsson, L. B., Rebild, M. P., Mukkamala, R. R., ... & Vatrapu, R. (2017). Buzz vs. sales: Big social data analytics of style icon campaigns and fashion designer collaborations on h&m’s facebook page.
  • Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 492-499). IEEE Computer Society.
  • Bian, J., Yoshigoe, K., Hicks, A., Yuan, J., He, Z., Xie, M., ... & Modave, F. (2016). Mining twitter to Assess the public perception of the “Internet of Things”. PloS one, 11(7), e0158450.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
  • Bonzanini, M. (2016). Mastering social media mining with Python. Packt Publishing Ltd.
  • 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.
  • Chatfield, A., & Brajawidagda, U. (2012). Twitter tsunami early warning network: a social network analysis of Twitter information flows.
  • Çoban, Ö., Özyer, B., & Özyer, G. T. (2015, May). Sentiment analysis for Turkish Twitter feeds. In Signal Processing and Communications Applications Conference (SIU), 2015 23th (pp. 2388-2391). IEEE.
  • Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. (2017). The power of the “like” button: The impact of social media on box office. Decision Support Systems, 94, 77-84.
  • Guidry, J. D., Messner, M., Jin, Y., & Medina-Messner, V. (2015). From# mcdonaldsfail to# dominossucks: An analysis of Instagram images about the 10 largest fast food companies. Corporate Communications: An International Journal, 20(3), 344-359.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Ishikawa, H. (2015). Social big data mining. CRC Press.
  • Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), 59-68.
  • Ki, E. J., & Nekmat, E. (2014). Situational crisis communication and interactivity: Usage and effectiveness of Facebook for crisis management by Fortune 500 companies. Computers in Human Behavior, 35, 140-147.
  • Kuflik, T., Minkov, E., Nocera, S., Grant-Muller, S., Gal-Tzur, A., & Shoor, I. (2017). Automating a framework to extract and analyse transport related social media content: The potential and the challenges. Transportation Research Part C: Emerging Technologies, 77, 275-291.
  • Kumar, S., Zafarani, R., & Liu, H. (2011, August). Understanding User Migration Patterns in Social Media. In AAAI (Vol. 11, pp. 8-11).
  • Lassen, N. B., Madsen, R., & Vatrapu, R. (2014, September). Predicting iphone sales from iphone tweets. In Enterprise Distributed Object Computing Conference (EDOC), 2014 IEEE 18th International (pp. 81-90). IEEE.
  • Lomborg, S., & Bechmann, A. (2014). Using APIs for data collection on social media. The Information Society, 30(4), 256-265.
  • McCormick, T. H., Lee, H., Cesare, N., Shojaie, A., & Spiro, E. S. (2017). Using Twitter for demographic and social science research: Tools for data collection and processing. Sociological methods & research, 46(3), 390-421.
  • Odlum, M., & Yoon, S. (2015). What can we learn about the Ebola outbreak from tweets?. American journal of infection control, 43(6), 563-571.
  • Oh, C., Roumani, Y., Nwankpa, J. K., & Hu, H. F. (2017). Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Information & Management, 54(1), 25-37.
  • Poell, T., & Borra, E. (2012). Twitter, YouTube, and Flickr as platforms of alternative journalism: The social media account of the 2010 Toronto G20 protests. Journalism, 13(6), 695-713.
  • Pournarakis, D. E., Sotiropoulos, D. N., & Giaglis, G. M. (2017). A computational model for mining consumer perceptions in social media. Decision Support Systems, 93, 98-110.
  • Ravindran, S. K., & Garg, V. (2015). Mastering social media mining with R. Packt Publishing Ltd.
  • Russell, M. A. (2013). Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. " O'Reilly Media, Inc.".
  • Sanders, R. (1987). The Pareto principle: its use and abuse. Journal of Services Marketing, 1(2), 37-40.
  • Sert, A. G. F., Tüzüntürk, S., & Gürsakal, N. (2014). NodeXL ile Sosyal Ağ Analizi:# akademikzam Örneği.
  • Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418.
  • van der Meer, T. G., & Verhoeven, P. (2013). Public framing organizational crisis situations: Social media versus news media. Public Relations Review, 39(3), 229-231.
  • Weinberg, T. (2009). The new community rules: Marketing on the social web. " O'Reilly Media, Inc.".
  • 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.
  • Ye, L., & Ki, E. J. (2017). Organizational crisis communication on Facebook: A study of BP’s Deepwater Horizon oil spill. Corporate Communications: An International Journal, 22(1), 80-92.
  • Zafarani, R., Abbasi, M. A., & Liu, H. (2014). Social media mining: an introduction. Cambridge University Press.
  • Zimbra, D., Fu, T., & Li, X. (2009). Assessing public opinions through Web 2.0: a case study on Wal-Mart. ICIS 2009 Proceedings, 67.

SOSYAL MEDYA MADENCİLİĞİ İLE FİRMALARIN TWITTER VERİLERİNİN İNCELENMESİ

Year 2019, Issue: 23, 237 - 256, 09.04.2019
https://doi.org/10.18092/ulikidince.475092

Abstract

Bu çalışma, farklı sektörlerde faaliyet gösteren rakip
firmaların Twitter verilerini analiz ederek, firmaların Twitter verilerinin
firmalara göre anlamlı bir uyum gösterip göstermediğinin tespit edilmesini,
firmaların Twitter’da paylaştıkları içeriklerin kümelenmesini ve hangi içerik
kümesinin en fazla etkileşime yol açtığının belirlenmesini amaçlamaktadır. Bu kapsamda,
2017 yılı boyunca kozmetik, elektronik ve pazaryeri sektörlerinde faaliyet
gösteren rakip firmalar tarafından paylaşılan Twitter verileri, Sosyal Medya
Madenciliği süreci izlenerek analiz edilmiştir. Firmaların Twitter verilerinin
firmalara göre anlamlı bir uyum gösterip göstermediği Uygunluk Analizi ile
tespit edilmiştir. Firmaların Twitter paylaşımları ise Metin Madenciliği ön
işleme metotlarından faydalanılarak “Özel Teklif”, “Yarışma & Etkinlik”,
“Ürün”, “Sosyal”, “Destek & Geri Bildirim” ve “Özel Etkileşim” kategori
başlıklarıyla kümelenmiştir. Firmaların elde ettikleri etkileşimlerin büyük bir
çoğunluğunun azınlıktaki paylaşımlardan gelmesi sebebi ile hangi içerik
kümesinin en fazla etkileşime yol açtığı Pareto İlkesi yardımı ile
belirlenmiştir.

References

  • af Rosenborg, K., Christina, D., Buhl-Andersen, I., Nilsson, L. B., Rebild, M. P., Mukkamala, R. R., ... & Vatrapu, R. (2017). Buzz vs. sales: Big social data analytics of style icon campaigns and fashion designer collaborations on h&m’s facebook page.
  • Asur, S., & Huberman, B. A. (2010, August). Predicting the future with social media. In Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 492-499). IEEE Computer Society.
  • Bian, J., Yoshigoe, K., Hicks, A., Yuan, J., He, Z., Xie, M., ... & Modave, F. (2016). Mining twitter to Assess the public perception of the “Internet of Things”. PloS one, 11(7), e0158450.
  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
  • Bonzanini, M. (2016). Mastering social media mining with Python. Packt Publishing Ltd.
  • 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.
  • Chatfield, A., & Brajawidagda, U. (2012). Twitter tsunami early warning network: a social network analysis of Twitter information flows.
  • Çoban, Ö., Özyer, B., & Özyer, G. T. (2015, May). Sentiment analysis for Turkish Twitter feeds. In Signal Processing and Communications Applications Conference (SIU), 2015 23th (pp. 2388-2391). IEEE.
  • Ding, C., Cheng, H. K., Duan, Y., & Jin, Y. (2017). The power of the “like” button: The impact of social media on box office. Decision Support Systems, 94, 77-84.
  • Guidry, J. D., Messner, M., Jin, Y., & Medina-Messner, V. (2015). From# mcdonaldsfail to# dominossucks: An analysis of Instagram images about the 10 largest fast food companies. Corporate Communications: An International Journal, 20(3), 344-359.
  • Han, J., Pei, J., & Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
  • Ishikawa, H. (2015). Social big data mining. CRC Press.
  • Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business horizons, 53(1), 59-68.
  • Ki, E. J., & Nekmat, E. (2014). Situational crisis communication and interactivity: Usage and effectiveness of Facebook for crisis management by Fortune 500 companies. Computers in Human Behavior, 35, 140-147.
  • Kuflik, T., Minkov, E., Nocera, S., Grant-Muller, S., Gal-Tzur, A., & Shoor, I. (2017). Automating a framework to extract and analyse transport related social media content: The potential and the challenges. Transportation Research Part C: Emerging Technologies, 77, 275-291.
  • Kumar, S., Zafarani, R., & Liu, H. (2011, August). Understanding User Migration Patterns in Social Media. In AAAI (Vol. 11, pp. 8-11).
  • Lassen, N. B., Madsen, R., & Vatrapu, R. (2014, September). Predicting iphone sales from iphone tweets. In Enterprise Distributed Object Computing Conference (EDOC), 2014 IEEE 18th International (pp. 81-90). IEEE.
  • Lomborg, S., & Bechmann, A. (2014). Using APIs for data collection on social media. The Information Society, 30(4), 256-265.
  • McCormick, T. H., Lee, H., Cesare, N., Shojaie, A., & Spiro, E. S. (2017). Using Twitter for demographic and social science research: Tools for data collection and processing. Sociological methods & research, 46(3), 390-421.
  • Odlum, M., & Yoon, S. (2015). What can we learn about the Ebola outbreak from tweets?. American journal of infection control, 43(6), 563-571.
  • Oh, C., Roumani, Y., Nwankpa, J. K., & Hu, H. F. (2017). Beyond likes and tweets: Consumer engagement behavior and movie box office in social media. Information & Management, 54(1), 25-37.
  • Poell, T., & Borra, E. (2012). Twitter, YouTube, and Flickr as platforms of alternative journalism: The social media account of the 2010 Toronto G20 protests. Journalism, 13(6), 695-713.
  • Pournarakis, D. E., Sotiropoulos, D. N., & Giaglis, G. M. (2017). A computational model for mining consumer perceptions in social media. Decision Support Systems, 93, 98-110.
  • Ravindran, S. K., & Garg, V. (2015). Mastering social media mining with R. Packt Publishing Ltd.
  • Russell, M. A. (2013). Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More. " O'Reilly Media, Inc.".
  • Sanders, R. (1987). The Pareto principle: its use and abuse. Journal of Services Marketing, 1(2), 37-40.
  • Sert, A. G. F., Tüzüntürk, S., & Gürsakal, N. (2014). NodeXL ile Sosyal Ağ Analizi:# akademikzam Örneği.
  • Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418.
  • van der Meer, T. G., & Verhoeven, P. (2013). Public framing organizational crisis situations: Social media versus news media. Public Relations Review, 39(3), 229-231.
  • Weinberg, T. (2009). The new community rules: Marketing on the social web. " O'Reilly Media, Inc.".
  • 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.
  • Ye, L., & Ki, E. J. (2017). Organizational crisis communication on Facebook: A study of BP’s Deepwater Horizon oil spill. Corporate Communications: An International Journal, 22(1), 80-92.
  • Zafarani, R., Abbasi, M. A., & Liu, H. (2014). Social media mining: an introduction. Cambridge University Press.
  • Zimbra, D., Fu, T., & Li, X. (2009). Assessing public opinions through Web 2.0: a case study on Wal-Mart. ICIS 2009 Proceedings, 67.
There are 34 citations in total.

Details

Primary Language Turkish
Journal Section Articles
Authors

Büşra Ayan 0000-0002-5212-2144

Mustafa Can

Umman Tuğba Gürsoy

Publication Date April 9, 2019
Published in Issue Year 2019 Issue: 23

Cite

APA Ayan, B., Can, M., & Gürsoy, U. T. (2019). SOSYAL MEDYA MADENCİLİĞİ İLE FİRMALARIN TWITTER VERİLERİNİN İNCELENMESİ. Uluslararası İktisadi Ve İdari İncelemeler Dergisi(23), 237-256. https://doi.org/10.18092/ulikidince.475092

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Address: Karadeniz Technical University Department of Economics Room Number 213  

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