Abstract
FIFA world cup is the most prestigious football tournament and widely viewed sporting event in the world. People support different teams (countries) of FIFA world cup based on players’ skills, number of winning trophies, and deliberate strategies that are applied by these teams during the tournament. These people share their opinion, criticism, love, and affection on the social media, i.e., Twitter. In this paper, we predict users’ FIFA world cup supporting preference from their tweets. First, we analyze user’s tweets and build two different types of classifiers by using LIWC and ELMo Word Embedding based techniques. These classifiers predict which team a user prefers from her word usage pattern in tweets. We find that Random Forest classifier performs the best for LIWC based model. We also find deep learning based word embedding technique, ELMo, achieves decent potential to predict users’ team supporting preference. Later, we build a multi-level weighted ensemble model to integrate both of the independent models, i.e., LIWC and ELMo. Our ensemble model shows substantial prediction potential (average accuracy-83.5%) to predict users’ FIFA world cup supporting preference from their tweets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
For the sake of brevity, we write supporting preference instead of FIFA world cup supporting team preference throughout the paper.
- 2.
- 3.
- 4.
- 5.
- 6.
- 7.
- 8.
References
Abbar, S., Mejova, Y., Weber, I.: You tweet what you eat: studying food consumption through Twitter. In: ACM CHI, pp. 3197–3206. ACM (2015)
Back, M.D., et al.: Facebook profiles reflect actual personality, not self-idealization. Psychol. Sci. 21(3), 372–374 (2010)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artifi. Intel. Res. 16, 321–357 (2002)
Conover, M.D., Gonçalves, B., Ratkiewicz, J., Flammini, A.: Predicting the political alignment of Twitter users. In: PASSAT, pp. 192–199. IEEE (2011)
Fast, E., Chen, B., Bernstein, M.S.: Empath: understanding topic signals in large-scale text. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 4647–4657 (2016)
Gambrell, L.B.: Getting students hooked on the reading habit. Read. Teach. 69(3), 259–263 (2015)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Islam, A.N., Mäntymäki, M., Benbasat, I.: Duality of self-promotion on social networking sites. Inf. Technol. People 32(2), 269–296 (2019)
Joulin, A., Grave, E.: FastText. zip: Compressing text classification models. arXiv preprint arXiv:1612.03651 (2016)
Lachenbruch, P.A., Goldstein, M.: Discriminant analysis. Biometrics 35, 69–85 (1979)
Leung, C.K., Joseph, K.W.: Sports data mining: predicting results for the college football games. Procedia Comput. Sci. 35, 710–719 (2014)
Menardi, G., Torelli, N.: Training and assessing classification rules with imbalanced data. Data Min. Knowl. Disc. 28(1), 92–122 (2012). https://doi.org/10.1007/s10618-012-0295-5
Miljković, D., Gajić, L., Kovačević, A., Konjović, Z.: The use of data mining for basketball matches outcomes prediction. In: 8th International Symposium on Intelligent Systems and Informatics (SISY), pp. 309–312. IEEE (2010)
Mukta, M.S.H., Ali, M.E., Mahmud, J.: User generated vs. supported contents: which one can better predict basic human values? In: Spiro, E., Ahn, Y.-Y. (eds.) SocInfo 2016. LNCS, vol. 10047, pp. 454–470. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47874-6_31
Mukta, M.S.H., Khan, E.M., Ali, M.E., Mahmud, J.: Predicting movie genre preferences from personality and values of social media users. In: Eleventh International AAAI Conference on Web and Social Media (2017)
Nichols, J., Mahmud, J., Drews, C.: Summarizing sporting events using Twitter. In: Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces, pp. 189–198. ACM (2012)
Ornstein, D.: Dutch substance over style (2008). https://bbc.in/2SaodXq
Pennebaker, J.W., Booth, R.J., Francis, M.E.: Linguistic inquiry and word count: LIWC [computer software]. liwc.net, Austin, TX (2007)
Pennington, J., Socher, R., Manning, C.: Glove: Global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
Peters, M.E., Neumann, M.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018)
Polikar, R.: Ensemble learning. In: Zhang, C., Ma, Y. (eds.) Ensemble Machine Learning, pp. 1–34. Springer, Boston (2012). https://doi.org/10.1007/978-1-4419-9326-7_1
Rangel, A., Camerer, C., Montague, P.R.: A framework for studying the neurobiology of value-based decision making. Nature Rev. Neuro. 9(7), 545–556 (2008)
Rong, X.: word2vec parameter learning explained. arXiv:1411.2738 (2014)
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)
Verplanken, B., Holland, R.W.: Motivated decision making: effects of activation and self-centrality of values on choices and behavior. J. Pers. Soc. Psychol. 82(3), 434 (2002)
Weaver, J.B., Brosius, H.B., Mundorf, N.: Personality and movie preferences: a comparison of american and german audiences. Personality Individ. Differ. 14(2), 307–315 (1993)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Rabbi, M.F., Mukta, M.S.H., Jenia, T.N., Islam, A.K.M.N. (2020). Predicting Fans’ FIFA World Cup Team Preference from Tweets. In: Bhuiyan, T., Rahman, M.M., Ali, M.A. (eds) Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-52856-0_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-52855-3
Online ISBN: 978-3-030-52856-0
eBook Packages: Computer ScienceComputer Science (R0)