Araştırma Makalesi
BibTex RIS Kaynak Göster

Sports Fans' Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final

Yıl 2021, , 62 - 75, 30.06.2021

Ahmet Atalay

https://doi.org/10.25307/jssr.892337
Cited By: 1

Öz

The purpose of the present study is to determine the words that came to the forefront of social media posts by fans for the 2018 World Cup Final, the most frequently used expressions, and the emotional tendencies of the fans. For this purpose, 56,877 tweets written in English on Twitter on the 2018 World Cup Final were extracted by the “R-Project” software and analyzed. According to the analysis results, it was concluded that a total of twenty positive statements were used with the highest frequency by fans, and it was also determined that the positive emotional trend was dominant compared to the negative trend, irrespective of what the result of the match was. In conclusion, it may be claimed that the perceptions and reactions of the fans regarding the World Cup Final are different from club matches at national level, and that mostly positive emotions came to the forefront.

Anahtar Kelimeler

Sentiment Analysis Twitter World Cup Football R Project

Kaynakça

  • Afacan, E., Onağ, Z., Demiran, D. & Çobanoğlu, G. (2017). The reasons of violence in football and the ways to prevent them according to Professional football players views: The match between Tarsus İdman Yurdu Erkutspor and Yeni Salihlispor. International Journal of Social Science Research, 6(2), 124-141.
  • Aldayel, H. & Azmi, A. M. (2016). Arabic tweets sentiment analysis – a hybrid scheme. Journal of Information Science, 42(6), 782–797. https://doi.org/10.1177/0165551515610513
  • Baudad, N., Faizi, R., Thami, R. & Chibeb, R. (2017). Sentiment analysis in Arabic: A review of the literature. Ain Shams Engineering Journal, 9(4), 2479-2490. https://doi.org/10.1016/j.asej.2017.04.007
  • Ceron, A., Curini, L., Iacus, S. M. & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340–358. https://doi.org/10.1177/1461444813480466
  • Deiner, M. S., Fathy, C., Kim, J., Niemeyer, K., Ramirez, D., Ackley, S. F. & Porco, T. C., (2017). Facebook and Twitter vaccine sentiment in response to measles outbreaks. Health Informatics Journal. https://doi.org/10.1177/1460458217740723
  • Fişek, K. (1998). Spor yönetimi (1. Baskı). Ankara: Bağırgan Yayınevi.
  • Goig, R. L. (2017). Football and politics in Spain: An empiricial analysis of the social base of the main football clubs. Journal of Iberian And Latin American Literary and Cultural Studies, 1(4), 79-100.
  • Himelboim, I., Sweetser, K. D., Tinkham, S. F., Cameron, K., Danelo, M. & West, K. (2016). Valence-based homophily on Twitter: Network analysis of emotions and political talk in the 2012 presidential election. New Media & Society, 18(7), 1382–1400. https://doi.org/10.1177/1461444814555096
  • İnce, M. (2016). Spor ile siyasetin ilişkisi üzerine bir analiz; Sporu siyasete alet etmek. Karabük Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(2), 456-464.
  • Ji, Q. & Raney, A. A. (2014). Morally judging entertainment: A case study of live tweeting during downton abbey. Media Psychology, 18(2), 221-242. https://doi.org/10.1080/15213269.2014.956939
  • Kaynar, O., Yıldız, M., Görmez, Y. & Albayrak, A. (2016). Sentiment Analysis with Machine Learning Techniques. Paper presented International Artificial Intelligence and Data Processing Sysmposium, September 28-30, 2018, Malatya, Turkey.
  • Khoo, C. S. & Johnkhan, S. B. (2018). Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons. Journal of Information Science, 44(4), 491–511. https://doi.org/10.1177/0165551517703514
  • Kim, E. H., Jeong, Y. K., Kim, Y., Kang, K. Y. & Song, M. (2016). Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news. Journal of Information Science, 42(6), 763–781. https://doi.org/10.1177/0165551515608733
  • Kreiss, D. (2016). Seizing the moment: The presidential campaigns’ use of Twitter during the 2012 electoral cycle. New Media & Society, 18(8) 1473–1490. https://doi.org/10.1177/1461444814562445
  • Krilenko, A. P. & Stepchenkova, S. O. (2017). Sochi 2014 Olympics a twitter: Perspective of hosts and guests. Tourism Management, 63(2017), 54-65. https://doi.org/10.1016/j.tourman.2017.06.007
  • Liu, B. (2012). Sentiment analysis and opinion mining (1. Edt.). Williston: Morgan & Claypool Publishers.
  • Lucas, G. M., Gratch, J., Malandrakis, N., Szablowski, E., Fessler, E. & Nichols, J. (2017). GOAALLL! Using sentiment in the World cup to explore theories. Image and Vision Computing, 65(2017), 58-65. https://doi.org/10.1016/j.imavis.2017.01.006
  • Mackey, T., Kalyanam, J., Klugman, J., Kuzmenko, E. & Gupta, R. (2018). Solution to detect, classify, and report ıllicit online marketing and sales of controlled substances via Twitter: using machine learning and web forensics to combat digital opioid access. Journal of Medical Internet Research, 20(4), e10029.
  • Mostafa, M. M. (2018). Clustering halal food consumers: A Twitter sentiment analysis. International Journal of Market Research, 61(3), 320-337. https://doi.org/10.1177/1470785318771451
  • Newson, M., Bortolini, T., Buhrmester, M., Da Silva, S. R., Da Aquino, J. N. Q. & Whitehouse, H. (2018). Brazil’s football worriors: Social bonding and inter-group violence. Evolution and Human Behavior, 39(2018), 675-683. https://doi.org/10.1016/j.evolhumbehav.2018.06.010
  • Öztürk, N. & Ayvaz, S. (2017). Sentiment analysis on twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), 136-147.
  • Pandey, A. C., Rajpoot, D. S. & Saraswat, M. (2017). Twitter sentiment analysis using hybrid cuckoo search. Information Processing and Management, 53(2017), 764-779. https://doi.org/10.1016/j.ipm.2017.02.004
  • Pang, B. & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1), 1-135.
  • Raney A.A. (2006). Why we watch and enjoy mediated sports. In: AA Raney & J Bryant (Eds), Handbook of Sports and Media. New York: Lawrence Erlbaum Associates, pp 313-329.
  • Raspaud, M. & Bastos, F. (2013). Torcedores de futebol: Violence and public policies in Brazil before the 2014 FIFA world cup. Sport in Society, Culture Commerce Media Politics, 16(2), 192–204. https://doi.org/10.1080/17430437.2013.776251
  • Sekulic, M., Kühl, S., Connert, T., Krastl, G. & Filippi, A. (2015). Dental and jaw injuries sustained by hooligans. Dental Traumatology, 31(6), 477–481. 10.1111/edt.12205.
  • Souza, A., Figueredo, M., Cacho, N., Araújo, D. & Prolo, C. A. (2016). Using big data and real-time analytics to support smart city initiatives. IFAC-PapersOnLine, 49(30), 257-262.
  • Spaaij, R. (2008). Men like us, boys like them: Violence, masculinity, and collective ıdentity in football hooliganism. Journal of Sport and Social Issues, 32(4), 369–392. https://doi.org/10.1177/0193723508324082
  • Stott, C., Adang, O., Livingstone, A. & Schreiber, M. (2008). Tackling football hooliganism: A quantitative study of public order policing and crowd psychology. Psychology Public Policy and Law, 14(2), 115-141. http://dx.doi.org/10.1037/a0013419
  • Üstünel, R. & Alkurt, Z. (2015). Futbolda şiddet ve düzensizliğin önlenmesi için 6222 sayılı yasanın getirdiği yeni bir uygulama: Elektronik bilet ve yaşanan sorunlar. Science Journal of Turkish Military Academy, 25(2), 141-175.
  • Wakefield, K. L. & Wann, D. L. (2006). An examination of dysfunctional sport fans: Method of classification and relationships with problem behaviors. Journal of Leisure Research, 38(2), 168-186. DOI:10.1080/00222216.2006.11950074
  • Wang, X. (2015). Using attitude functions self-efficacy and norms to predict attitudes and intentions to use mobile devices to access social media during sporting event attendance. Mobile Media and Communication, 3, 75-90. https://doi.org/10.1177/2050157914548932
  • Workewych, A. M., Muzzi, M. C., Jing, R., Zhang, S., Topolovec-Vranic, J.& Cusimano, M. D. (2017). Twitter and traumatic brain injury: A content and sentiment analysis of tweets pertaining to sport-related brain injury. SAGE Open Medicine, 5, 1-11. https://doi.org/10.1177/2050312117720057
  • Yu, Y. & Wang, X. (2015). World Cup 2014 in Twitter world: A big data analysis of sentiments in U.S. sports fans’ tweets. Computer in Human Behavior, 48(2015), 392-400. https://doi.org/10.1016/j.chb.2015.01.075
  • Yu, Y., Duan, W. & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support System, 55(4), 919-926. https://doi.org/10.1016/j.dss.2012.12.028
  • Zeng, B. & Gerritsen, R. (2014). What do we know about social median tourism? A review. Tourism Management Perspective, 10, 27-36. https://doi.org/10.1016/j.tmp.2014.01.001

Taraftarların Twitter'daki Davranışları: 2018 Dünya Kupası Final Maçı Taraftar Duygularının Büyük Veri Analizi

Yıl 2021, , 62 - 75, 30.06.2021

Ahmet Atalay

https://doi.org/10.25307/jssr.892337
Cited By: 1

Öz

Bu çalışmanın amacı, 2018 Dünya Kupası Final Maçında taraftar paylaşımında öne çıkan kelimeleri, bu sözlerle birlikte en sık kullanılan ifadeleri ve taraftarların duygusal eğilimlerini belirlemektir. Bu amaçla, İngilizce yazılan ve 2018 Dünya Kupası Final Maçı'nda Twitter'da paylaşılan 56.877 adet tweet, “The R-Project” adı verilen yazılım üzerinden alınarak analizleri yapıldı. Analiz sonuçlarına göre taraftar paylaşımında en yüksek sıklıkta kullanılan toplam yirmi ifadenin olumlu olduğu; final maçının sonucu ne olursa olsun, olumlu duygusal eğilimin olumsuz eğilime göre baskın olduğu da belirlendi. Sonuç olarak taraftarların Dünya Kupası Final Maçı ile ilgili algılarının ve tepkilerinin kulüpler arası ulusal düzeydeki yarışmalardan farklı olduğu ve daha çok olumlu duyguların ön plana çıktığı iddia edilebilir.

Anahtar Kelimeler

Duygu Analizi Twitter World Cup Football R Project

Kaynakça

  • Afacan, E., Onağ, Z., Demiran, D. & Çobanoğlu, G. (2017). The reasons of violence in football and the ways to prevent them according to Professional football players views: The match between Tarsus İdman Yurdu Erkutspor and Yeni Salihlispor. International Journal of Social Science Research, 6(2), 124-141.
  • Aldayel, H. & Azmi, A. M. (2016). Arabic tweets sentiment analysis – a hybrid scheme. Journal of Information Science, 42(6), 782–797. https://doi.org/10.1177/0165551515610513
  • Baudad, N., Faizi, R., Thami, R. & Chibeb, R. (2017). Sentiment analysis in Arabic: A review of the literature. Ain Shams Engineering Journal, 9(4), 2479-2490. https://doi.org/10.1016/j.asej.2017.04.007
  • Ceron, A., Curini, L., Iacus, S. M. & Porro, G. (2014). Every tweet counts? How sentiment analysis of social media can improve our knowledge of citizens’ political preferences with an application to Italy and France. New Media & Society, 16(2), 340–358. https://doi.org/10.1177/1461444813480466
  • Deiner, M. S., Fathy, C., Kim, J., Niemeyer, K., Ramirez, D., Ackley, S. F. & Porco, T. C., (2017). Facebook and Twitter vaccine sentiment in response to measles outbreaks. Health Informatics Journal. https://doi.org/10.1177/1460458217740723
  • Fişek, K. (1998). Spor yönetimi (1. Baskı). Ankara: Bağırgan Yayınevi.
  • Goig, R. L. (2017). Football and politics in Spain: An empiricial analysis of the social base of the main football clubs. Journal of Iberian And Latin American Literary and Cultural Studies, 1(4), 79-100.
  • Himelboim, I., Sweetser, K. D., Tinkham, S. F., Cameron, K., Danelo, M. & West, K. (2016). Valence-based homophily on Twitter: Network analysis of emotions and political talk in the 2012 presidential election. New Media & Society, 18(7), 1382–1400. https://doi.org/10.1177/1461444814555096
  • İnce, M. (2016). Spor ile siyasetin ilişkisi üzerine bir analiz; Sporu siyasete alet etmek. Karabük Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 6(2), 456-464.
  • Ji, Q. & Raney, A. A. (2014). Morally judging entertainment: A case study of live tweeting during downton abbey. Media Psychology, 18(2), 221-242. https://doi.org/10.1080/15213269.2014.956939
  • Kaynar, O., Yıldız, M., Görmez, Y. & Albayrak, A. (2016). Sentiment Analysis with Machine Learning Techniques. Paper presented International Artificial Intelligence and Data Processing Sysmposium, September 28-30, 2018, Malatya, Turkey.
  • Khoo, C. S. & Johnkhan, S. B. (2018). Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons. Journal of Information Science, 44(4), 491–511. https://doi.org/10.1177/0165551517703514
  • Kim, E. H., Jeong, Y. K., Kim, Y., Kang, K. Y. & Song, M. (2016). Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news. Journal of Information Science, 42(6), 763–781. https://doi.org/10.1177/0165551515608733
  • Kreiss, D. (2016). Seizing the moment: The presidential campaigns’ use of Twitter during the 2012 electoral cycle. New Media & Society, 18(8) 1473–1490. https://doi.org/10.1177/1461444814562445
  • Krilenko, A. P. & Stepchenkova, S. O. (2017). Sochi 2014 Olympics a twitter: Perspective of hosts and guests. Tourism Management, 63(2017), 54-65. https://doi.org/10.1016/j.tourman.2017.06.007
  • Liu, B. (2012). Sentiment analysis and opinion mining (1. Edt.). Williston: Morgan & Claypool Publishers.
  • Lucas, G. M., Gratch, J., Malandrakis, N., Szablowski, E., Fessler, E. & Nichols, J. (2017). GOAALLL! Using sentiment in the World cup to explore theories. Image and Vision Computing, 65(2017), 58-65. https://doi.org/10.1016/j.imavis.2017.01.006
  • Mackey, T., Kalyanam, J., Klugman, J., Kuzmenko, E. & Gupta, R. (2018). Solution to detect, classify, and report ıllicit online marketing and sales of controlled substances via Twitter: using machine learning and web forensics to combat digital opioid access. Journal of Medical Internet Research, 20(4), e10029.
  • Mostafa, M. M. (2018). Clustering halal food consumers: A Twitter sentiment analysis. International Journal of Market Research, 61(3), 320-337. https://doi.org/10.1177/1470785318771451
  • Newson, M., Bortolini, T., Buhrmester, M., Da Silva, S. R., Da Aquino, J. N. Q. & Whitehouse, H. (2018). Brazil’s football worriors: Social bonding and inter-group violence. Evolution and Human Behavior, 39(2018), 675-683. https://doi.org/10.1016/j.evolhumbehav.2018.06.010
  • Öztürk, N. & Ayvaz, S. (2017). Sentiment analysis on twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), 136-147.
  • Pandey, A. C., Rajpoot, D. S. & Saraswat, M. (2017). Twitter sentiment analysis using hybrid cuckoo search. Information Processing and Management, 53(2017), 764-779. https://doi.org/10.1016/j.ipm.2017.02.004
  • Pang, B. & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1), 1-135.
  • Raney A.A. (2006). Why we watch and enjoy mediated sports. In: AA Raney & J Bryant (Eds), Handbook of Sports and Media. New York: Lawrence Erlbaum Associates, pp 313-329.
  • Raspaud, M. & Bastos, F. (2013). Torcedores de futebol: Violence and public policies in Brazil before the 2014 FIFA world cup. Sport in Society, Culture Commerce Media Politics, 16(2), 192–204. https://doi.org/10.1080/17430437.2013.776251
  • Sekulic, M., Kühl, S., Connert, T., Krastl, G. & Filippi, A. (2015). Dental and jaw injuries sustained by hooligans. Dental Traumatology, 31(6), 477–481. 10.1111/edt.12205.
  • Souza, A., Figueredo, M., Cacho, N., Araújo, D. & Prolo, C. A. (2016). Using big data and real-time analytics to support smart city initiatives. IFAC-PapersOnLine, 49(30), 257-262.
  • Spaaij, R. (2008). Men like us, boys like them: Violence, masculinity, and collective ıdentity in football hooliganism. Journal of Sport and Social Issues, 32(4), 369–392. https://doi.org/10.1177/0193723508324082
  • Stott, C., Adang, O., Livingstone, A. & Schreiber, M. (2008). Tackling football hooliganism: A quantitative study of public order policing and crowd psychology. Psychology Public Policy and Law, 14(2), 115-141. http://dx.doi.org/10.1037/a0013419
  • Üstünel, R. & Alkurt, Z. (2015). Futbolda şiddet ve düzensizliğin önlenmesi için 6222 sayılı yasanın getirdiği yeni bir uygulama: Elektronik bilet ve yaşanan sorunlar. Science Journal of Turkish Military Academy, 25(2), 141-175.
  • Wakefield, K. L. & Wann, D. L. (2006). An examination of dysfunctional sport fans: Method of classification and relationships with problem behaviors. Journal of Leisure Research, 38(2), 168-186. DOI:10.1080/00222216.2006.11950074
  • Wang, X. (2015). Using attitude functions self-efficacy and norms to predict attitudes and intentions to use mobile devices to access social media during sporting event attendance. Mobile Media and Communication, 3, 75-90. https://doi.org/10.1177/2050157914548932
  • Workewych, A. M., Muzzi, M. C., Jing, R., Zhang, S., Topolovec-Vranic, J.& Cusimano, M. D. (2017). Twitter and traumatic brain injury: A content and sentiment analysis of tweets pertaining to sport-related brain injury. SAGE Open Medicine, 5, 1-11. https://doi.org/10.1177/2050312117720057
  • Yu, Y. & Wang, X. (2015). World Cup 2014 in Twitter world: A big data analysis of sentiments in U.S. sports fans’ tweets. Computer in Human Behavior, 48(2015), 392-400. https://doi.org/10.1016/j.chb.2015.01.075
  • Yu, Y., Duan, W. & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support System, 55(4), 919-926. https://doi.org/10.1016/j.dss.2012.12.028
  • Zeng, B. & Gerritsen, R. (2014). What do we know about social median tourism? A review. Tourism Management Perspective, 10, 27-36. https://doi.org/10.1016/j.tmp.2014.01.001
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Turizm (Diğer)
Bölüm Orijinal Makale
Yazarlar

Ahmet Atalay 0000-0003-0263-1677

Yayımlanma Tarihi 30 Haziran 2021
Kabul Tarihi 24 Nisan 2021
Yayımlandığı Sayı Yıl 2021

Kaynak Göster

APA Atalay, A. (2021). Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final. Journal of Sport Sciences Research, 6(1), 62-75. https://doi.org/10.25307/jssr.892337
AMA Atalay A. Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final. JSSR. Haziran 2021;6(1):62-75. doi:10.25307/jssr.892337
Chicago Atalay, Ahmet. “Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final”. Journal of Sport Sciences Research 6, sy. 1 (Haziran 2021): 62-75. https://doi.org/10.25307/jssr.892337.
EndNote Atalay A (01 Haziran 2021) Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final. Journal of Sport Sciences Research 6 1 62–75.
IEEE A. Atalay, “Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final”, JSSR, c. 6, sy. 1, ss. 62–75, 2021, doi: 10.25307/jssr.892337.
ISNAD Atalay, Ahmet. “Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final”. Journal of Sport Sciences Research 6/1 (Haziran 2021), 62-75. https://doi.org/10.25307/jssr.892337.
JAMA Atalay A. Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final. JSSR. 2021;6:62–75.
MLA Atalay, Ahmet. “Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final”. Journal of Sport Sciences Research, c. 6, sy. 1, 2021, ss. 62-75, doi:10.25307/jssr.892337.
Vancouver Atalay A. Sports Fans’ Behavior on Twitter: A Big Data Analysis of Sentiments in the 2018 World Cup Final. JSSR. 2021;6(1):62-75.

Cited By

Sosyal Ağlar Büyük Veriden Nasıl Yararlanır: Facebook ve Twitter
Bilgi Yönetimi
https://doi.org/10.33721/by.983553

26355    18836       18837       8748

Dergi indirme İstatistikleri 

indir.png