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The Wisdom of the Silent Crowd: Predicting the Match Results of World Cup 2018 through Twitter

by Abdullah Talha Kabakus, Mehmet Simsek, Ibrahim Belenli
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 27
Year of Publication: 2018
Authors: Abdullah Talha Kabakus, Mehmet Simsek, Ibrahim Belenli
10.5120/ijca2018918125

Abdullah Talha Kabakus, Mehmet Simsek, Ibrahim Belenli . The Wisdom of the Silent Crowd: Predicting the Match Results of World Cup 2018 through Twitter. International Journal of Computer Applications. 182, 27 ( Nov 2018), 40-45. DOI=10.5120/ijca2018918125

@article{ 10.5120/ijca2018918125,
author = { Abdullah Talha Kabakus, Mehmet Simsek, Ibrahim Belenli },
title = { The Wisdom of the Silent Crowd: Predicting the Match Results of World Cup 2018 through Twitter },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2018 },
volume = { 182 },
number = { 27 },
month = { Nov },
year = { 2018 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number27/30151-2018918125/ },
doi = { 10.5120/ijca2018918125 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:12:40.111552+05:30
%A Abdullah Talha Kabakus
%A Mehmet Simsek
%A Ibrahim Belenli
%T The Wisdom of the Silent Crowd: Predicting the Match Results of World Cup 2018 through Twitter
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 27
%P 40-45
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social networks have attracted much attention thanks to their availability, being the source of information in almost everything related to life, and giving the chance for users to discover the things and people related to them. Global and highly popular events such as the FIFA World Cup are always welcomed by social networking sites. Furthermore, these social networking sites customize themselves and provide some specific content for this global events. From this point of view, we have collected 38,371,358 tweets during the FIFA World Cup 2018 which were posted by 7,876,519 unique Twitter users. The aim of this study is proposing a prediction system that evaluates the teams who qualified for the FIFA World Cup 2018 through their squad, and their performances in the early stages of the competition in order to predict the match results of the further stages. For this reason, a number of different types of machine learning algorithms are utilized after building a machine learning model which is based on novel features. According to the experimental results, the best accuracy of the proposed system for the match result prediction is calculated as high as 87.5% which is quite better than the related work. The experimental results demonstrate the effectiveness of social network based features.

References
  1. Yin Z, Cao L, Han J, et al (2011) Diversified Trajectory Pattern Ranking in Geo-Tagged Social Media. In: Proceedings of the 2011 SIAM International Conference on Data Mining. Mesa, Arizona, USA.
  2. Kurashima T, Iwata T, Irie G, Fujimura K (2010) Travel route recommendation using geotags in photo sharing sites. In: Proceedings of the 19th ACM international conference on Information and knowledge management (CIKM ’10). ACM, Toronto, ON, Canada.
  3. Schreck T, Keim D (2013) Visual analysis of social media data. Computer (Long Beach Calif) 20:68–75. doi: 10.1109/MC.2012.430.
  4. Pak A, Paroubek P (2010) Twitter as a Corpus for Sentiment Analysis and Opinion Mining. Lrec 1320–1326. doi: 10.1371/journal.pone.0026624.
  5. Desai M, Mehta MA (2016) Techniques for Sentiment Analysis of Twitter Data: A Comprehensive Survey. In: 2016 International Conference on Computing, Communication and Automation (ICCCA 2016). Noida, India, pp 149–154.
  6. (2018) Records Broken at World Cup in Russia as Attendance Neared 100% - Deputy Prime Minister. In: FourFourTwo. https://www.fourfourtwo.com.au/feature/records-broken-at-world-cup-in-russia-as-attendance-neared-100---deputy-prime-minister-498625. Accessed 23 Aug 2018.
  7. Dewan P, Kumaraguru P (2017) Facebook Inspector (FbI): Towards automatic real-time detection of malicious content on Facebook. Soc Netw Anal Min. doi: 10.1007/s13278-017-0434-5.
  8. Rogers S (2014) Insights into the #WorldCup conversation on Twitter. In: Twitter. https://blog.twitter.com/official/en_us/a/2014/insights-into-the-worldcup-conversation-on-twitter.html. Accessed 23 Aug 2018.
  9. Bavishi J, Filadelfo E (2018) Insights into the 2018 #WorldCup conversation on Twitter. In: Twitter. https://blog.twitter.com/official/en_us/topics/events/2018/2018-World-Cup-Insights.html. Accessed 23 Aug 2018.
  10. (2018) England´s World Cup penalty win against Colombia draws. In: Dly. Mail Online. http://www.dailymail.co.uk/wires/pa/article-5917361/England-s-World-Cup-penalty-win-against-Colombia-draws-huge-TV-audience.html. Accessed 23 Aug 2018.
  11. Purucker MC (1996) Neural network quarterbacking. IEEE Potentials. doi: 10.1109/45.535226.
  12. Kahn J (2003) Neural Network Prediction of NFL Football Games. http://homepages.cae.wisc.edu/~ece539/project/f03/kahn.pdf. Accessed 28 Aug 2018.
  13. Godin F, Zuallaert J, Vandersmissen B (2015) Beating the Bookmakers: Leveraging Statistics and Twitter Microposts for Predicting Soccer Results. In: Proceedings of the 2014 KDD International Workshop on Large-Scale Sports Analytics.
  14. Tax N, Joustra Y (2015) Predicting The Dutch Football Competition Using Public Data: A Machine Learning Approach. Trans Knowl data Eng. doi: 10.13140/RG.2.1.1383.4729.
  15. Herzog SM, Hertwig R (2011) The wisdom of ignorant crowds: Predicting sport outcomes by mere recognition. Judgm Decis Mak.
  16. Gilliam D (2014) Predictions for the #WorldCup2014. In: Troubad. Res. Consult. http://troubadourconsulting.com/initial-predictions-for-the-worldcup2014/. Accessed 23 Aug 2018.
  17. Schläfli S (2014) Predicting World Cup results with the wisdom of the crowd. In: ETH Zurich. https://www.ethz.ch/en/news-and-events/eth-news/news/2014/07/viele-tippen-weiser.html. Accessed 23 Aug 2018.
  18. Radosavljevic V, Grbovic M, Djuric N, Bhamidipati N (2014) Large-scale World Cup 2014 outcome prediction based on Tumblr posts. Sunnyvale, CA, USA.
  19. Bothos E, Apostolou D, Mentzas G (2010) Using Social Media to Predict Future Events with Agent-Based Markets. IEEE Intell Syst. doi: 10.1109/MIS.2010.152.
  20. Schumaker RP, Jarmoszko AT, Labedz CS (2016) Predicting wins and spread in the Premier League using a sentiment analysis of twitter. Decis Support Syst. doi: 10.1016/j.dss.2016.05.010.
  21. Sachs G (2014) The World Cup and Economics 2014.
  22. (2014) World Cup 2014 Predictions. In: Bloomberg. https://www.bloomberg.com/graphics/2014-world-cup/#0,0,-1. Accessed 23 Aug 2018.
  23. Tumasjan A, Sprenger T, Sandner P, Welpe I (2010) Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. In: Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. Washington, DC, USA, pp 178–185.
  24. (2018) Twitter4J - A Java library for the Twitter API. http://twitter4j.org/en/. Accessed 23 Aug 2018.
  25. Dixon MJ, Coles SG (1997) Modelling association football scores and inefficiencies in the football betting market. J R Stat Soc Ser C Appl Stat. doi: 10.1111/1467-9876.00065.
  26. Boshnakov G, Kharrat T, McHale IG (2017) A bivariate Weibull count model for forecasting association football scores. Int J Forecast. doi: 10.1016/j.ijforecast.2016.11.006.
  27. (2018) DB-Engines Ranking - popularity ranking of database management systems. In: DB-Engines. https://db-engines.com/en/ranking. Accessed 23 Aug 2018.
  28. Boicea A, Radulescu F, Agapin LI (2012) MongoDB vs Oracle - Database comparison. In: Proceedings of 3rd International Conference on Emerging Intelligent Data and Web Technologies, EIDWT 2012. Bucharest, Romania, pp 330–335.
  29. Parker Z, Poe S, Vrbsky S V. (2013) Comparing NoSQL MongoDB to an SQL DB. In: Proceedings of the 51st ACM Southeast Conference on - ACMSE ’13.
  30. Ming Wu C (2015) Comparisons Between MongoDB and MS-SQL Databases on the TWC Website. Am J Softw Eng Appl 4:35–41. doi: 10.11648/j.ajsea.20150402.12.
  31. Nyati SS, Pawar S, Ingle R (2013) Performance evaluation of unstructured NoSQL data over distributed framework. In: Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013.
  32. Schläfli S (2014) Predicting World Cup results with the wisdom of the crowd. In: ETH Zurich. https://www.ethz.ch/en/news-and-events/eth-news/news/2014/07/viele-tippen-weiser.html. Accessed 24 Aug 2018.
  33. Chow J (2014) World Cup 2014 Data Analysis and Predictions. https://github.com/woobe/wc2014. Accessed 28 Aug 2018.
Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Sports Result Prediction Social Network Analysis Twitter FIFA World Cup.

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