Identifying Soccer Players on Facebook Through Predictive Analytics

References

• Baatarjav EA, Phithakkitnukoon S, Dantu R (2008) Group recommendation system for facebook. Meersman R, Tari Z, Herrero P, eds. On the Move to Meaningful Internet Systems: OTM 2008 Workshops (Springer, Berlin), 211–219.Crossref, Google Scholar
• Babyak MA (2004) What you see may not be what you get: A brief, nontechnical introduction to overfitting in regression-type models. Psychosomatic Medicine 66(3):411–421.Google Scholar
• Baldi P, Hornik K (1989) Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks 2(1):53–58.Crossref, Google Scholar
• Ballings M, Van den Poel D (2012) Customer event history for churn prediction: How long is long enough? Expert Systems Appl. 39(18):13517–13522.Crossref, Google Scholar
• Ballings M, Van den Poel D (2013) Kernel factory: An ensemble of kernel machines. Expert Systems Appl. 40(8):2904–2913.Crossref, Google Scholar
• Ballings M, Van den Poel D (2015a) CRM in social media: Predicting increases in Facebook usage frequency. Eur. J. Oper. Res. 244(1): 248–260.Crossref, Google Scholar
• Ballings M, Van den Poel D (2015b) R-package rotationForest: Fit and deploy rotation forest models, v. 0.1.3. Retrieved December 2, 2015, https://CRAN.R-project.org/package=rotationForest.Google Scholar
• Ballings M, Van den Poel D, Hespeels N, Gryp R (2015) Evaluating multiple classifiers for stock price direction prediction. Expert Systems Appl. 42(20):7046–7056.Crossref, Google Scholar
• Bauer HH, Stokburger-Sauer NE, Exler S (2008) Brand image and fan loyalty in professional team sport: A refined model and empirical assessment. J. Sport Management 22(2):205–226.Crossref, Google Scholar
• Belzer J (2016) Sports industry 101: Breaking into the business of sports. Forbes (February 5), http://www.forbes.com/sites/jasonbelzer/2014/02/05/sports-industry-101-breaking-into-the-business-of-sports/.Google Scholar
• Berjani B, Strufe T (2011) A recommendation system for spots in location-based online social networks. Proc. 4th Workshop Soc. Network Systems (Association for Computing Machinery, New York), 1–6.Crossref, Google Scholar
• Berk RA (2008) Statistical Learning from a Regression Perspective (Springer Science and Business Media, Berlin).Google Scholar
• Bogaert M, Ballings M, Van den Poel D (2016a) The added value of Facebook friends data in event attendance prediction. Decision Support Systems 82(February):26–34.Crossref, Google Scholar
• Bogaert M, Ballings M, Van den Poel D (2016b) Evaluating the importance of different communication types in romantic tie prediction on social media. Ann. Oper. Res., ePub ahead of print August 17, https://dx.doi.org/10.1007/s10479-016-2295-0.Crossref, Google Scholar
• Breiman L (2001) Random forests. Machine Learn. 45(1):5–32.Crossref, Google Scholar
• Bu J, Tan S, Chen C, Wang C, Wu H, Zhang L, He X (2010) Music recommendation by unified hypergraph: Combining social media information and music content. del Bimbo A, Chang S-F, Smeulders A, eds. Proc. 18th ACM Internat. Conf. Multimedia (Association for Computing Machinery, New York), 391–400.Crossref, Google Scholar
• Burez J, Van den Poel D (2007) CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services. Expert Systems Appl. 32(2):277–288.Crossref, Google Scholar
• Carmagnola F, Vernero F, Grillo P (2009) Sonars: A social networks-based algorithm for social recommender systems. Houben G-J, McCalla G, Pianesi F, Zancanaro M, eds. User Modeling, Adaptation, Personalization (Springer, Berlin), 223–234.Crossref, Google Scholar
• Cesario E, Congedo C, Marozzo F, Riotta G, Spada A, Talia D, Trunfio P, Turri C (2015) Following soccer fans from geotagged tweets at FIFA World Cup 2014. Zhou M, Lin J, Wu K, Fang L, eds. 2015 2nd IEEE Internat. Conf. Spatial Data Mining and Geographical Knowledge Services (ICSDM) (IEEE, Hoboken, NJ), 33–38.Crossref, Google Scholar
• Cesario E, Iannazzo AR, Marozzo F, Morello F, Riotta G, Spada A, Talia D, Trunfio P (2016) Analyzing social media data to discover mobility patterns at EXPO 2015: Methodology and results. Zeljkovic V, ed. 2016 Internat. Conf. High Performance Comput. Simulation (HPCS) (IEEE, Hoboken, NJ), 230–237.Crossref, Google Scholar
• Chan JCW, Paelinckx D (2008) Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sensing Environ. 112(6):2999–3011.Crossref, Google Scholar
• Chang J, Sun E (2011) Location 3: How users share and respond to location-based data on social networking sites. Nicolov N, Shanahan JG, eds. Proc. Fifth Internat. AAAI Conf. Weblogs Soc. Media (Association for the Advancement of Artificial Intelligence, Menlo Park, CA), 74–80.Google Scholar
• Coussement K, Benoit DF, Van den Poel D (2010) Improved marketing decision making in a customer churn prediction context using generalized additive models. Expert Systems Appl. 37(3): 2132–2143.Crossref, Google Scholar
• Culp M, Johnson K, Michailidis G (2006) ada: An r package for stochastic boosting. J. Statist. Software 17(2):1–27.Crossref, Google Scholar
• De Bock KW, Van den Poel D (2011) An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction. Expert Systems Appl. 38(10):12293–12301.Crossref, Google Scholar
• Deloitte (2016) Deloitte football money league 2017. Accessed October 6, 2016, http://www2.deloitte.com/uk/en/pages/sports-business-group/articles/deloitte-football-money-league.html.Google Scholar
• Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(December):1–30.Google Scholar
• D’Haen J, Van den Poel D, Thorleuchter D, Benoit DF (2016) Integrating expert knowledge and multilingual web crawling data in a lead qualification system. Decision Support Systems 82(February):69–78.Crossref, Google Scholar
• Dietterich TG (1998) Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7):1895–1923.Crossref, Google Scholar
• Dietterich TG (2000) Ensemble methods in machine learning. Kittler J, Roli F, eds. Multiple Classifier Systems (Springer, Berlin), 1–15.Crossref, Google Scholar
• Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: A methodology review. J. Biomedical Informatics 35(5):352–359.Crossref, Google Scholar
• Dudoit S, Fridlyand J, Speed TP (2002) Comparison of discrimination methods for the classification of tumors using gene expression data. J. Amer. Statist. Assoc. 97(457):77–87.Crossref, Google Scholar
• Egan K (2016) The difference between Facebook, Twitter, Linkedin, Google+, YouTube, and Pinterest. (May 19), https://www.impactbnd.com/blog/the-difference-between-facebook-twitter-linkedin-google-youtube-pinterest.Google Scholar
• Facebook (2017) Company info Facebook newsroom. Accessed January 27, 2017, http://newsroom.fb.com/company-info/.Google Scholar
• Filo K, Lock D, Karg A (2015) Sport and social media research: A review. Sport Management Rev. 18(2):166–181.Crossref, Google Scholar
• Freund Y, Schapire RE, others (1996) Experiments with a new boosting algorithm. Internat. Conf. Machine Learning 96(July):148–156.Google Scholar
• Friedman J, Hastie T, Simon N, Tibshirani R (2015) R-package glmnet: Lasso and elastic-net regularized generalized linear models, v.2.0-13. Retrieved July 29, 2015, https://CRAN.R-project.org/package=glmnet.Google Scholar
• Friedman JH (2002) Stochastic gradient boosting. Comput. Statist. Data Anal. 38(4):367–378.Crossref, Google Scholar
• Friedman JH, Meulman JJ (2003) Multiple additive regression trees with application in epidemiology. Statist. Medicine 22(9):1365–1381.Crossref, Google Scholar
• Funk DC (1998) Fan loyalty: The structure and stability of an individual’s loyalty toward an athletic team. Doctoral dissertation, Ohio State University, Columbus.Google Scholar
• Gao H, Tang J, Hu X, Liu H (2013) Exploring temporal effects for location recommendation on location-based social networks. Yang Q, King I, Li Q, eds. Proc. 7th ACM Conf. Recommender Systems (Association for Computing Machinery, New York),93–100.Crossref, Google Scholar
• Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recognition Lett. 27(4):294–300.Crossref, Google Scholar
• Golbeck J, Hansen D (2011) Computing political preference among Twitter followers. Tan D, ed. Proc. SIGCHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 1105–1108.Crossref, Google Scholar
• Guisan A, Edwards TC Jr, Hastie T (2002) Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecological Modelling 157(2–3):89–100.Crossref, Google Scholar
• Guy I, Zwerdling N, Ronen I, Carmel D, Uziel E (2010) Social media recommendation based on people and tags. Crestani F, Marchand-Maillet S, eds. Proc. 33rd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York), 194–201.Crossref, Google Scholar
• Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36.Crossref, Google Scholar
• He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans. Knowledge and Data Engrg. 21(9):1263–1284.Crossref, Google Scholar
• Hernandez-Orallo J, Flach P, Ferri C (2012) A unified view of performance metrics: Translating threshold choice into expected classification loss. J. Machine Learn. Res. 13(October):2813–2869.Google Scholar
• Hoch SJ (1988) Who do we know: Predicting the interests and opinions of the american consumer. J. Consumer Res. 15(3):315–324.Crossref, Google Scholar
• Hsu CC, Chen HC, Huang KK, Huang YM (2012) A personalized auxiliary material recommendation system based on learning style on Facebook applying an artificial bee colony algorithm. Comput. Math. Appl. 64(5):1506–1513.Crossref, Google Scholar
• James G, Witten D, Hastie T, Tibshirani R (2013) An Introduction to Statistical Learning, Vol. 112 (Springer, Berlin).Crossref, Google Scholar
• Janitza S, Strobl C, Boulesteix AL (2013) An AUC-based permutation variable importance measure for random forests. BMC Bioinformatics 14(1):1–11.Crossref, Google Scholar
• Kalampokis E, Tarabanis K, Tambouris E (2013) Understanding the predictive power of social media. Internet Res. 23(5):544–559.Crossref, Google Scholar
• Kayaalp M, Özyer T, Özyer ST (2009) A collaborative and content based event recommendation system integrated with data collection scrapers and services at a social networking site. Harkiolakis N, ed. Proc. 2009 Internat. Conf. Advances Soc. Network Anal. Mining (IEEE Computer Society, Washington, DC), 113–118.Crossref, Google Scholar
• Kim HN, Saddik AE (2013) Exploring social tagging for personalized community recommendations. User Model User-Adap. Inter. 23(2–3):249–285.Crossref, Google Scholar
• Kisilevich S, Keim D, Rokach L (2010) A novel approach to mining travel sequences using collections of geotagged photos. Painho M, Santos MY, Pundt H, eds. Geospatial Thinking (Springer, Berlin), 163–182.Crossref, Google Scholar
• Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation. Allan J, Aslam J, eds. Proc. 32nd Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval (Association for Computing Machinery, New York),195–202.Crossref, Google Scholar
• Lampe CA, Ellison N, Steinfield C (2007) A familiar Face(Book): Profile elements as signals in an online social network. Rosson MB, ed. Proc. SIGCHI Conf. Human Factors Comput. Systems (Association for Computing Machinery, New York), 435–444.Crossref, Google Scholar
• Langley P (2000) Crafting papers on machine learning. Internat. Conf. Machine Learning (ICML), 1207–1216.Google Scholar
• Lemmens A, Croux C (2006) Bagging and boosting classification trees to predict churn. J. Marketing Res. 43(2):276–286.Crossref, Google Scholar
• Liaw A, Wiener M (2002) Classification and regression by random-Forest. R news 2(3):18–22.Google Scholar
• Malthouse EC, Haenlein M, Skiera B, Wege E, Zhang M (2013) Managing customer relationships in the social media era: Introducing the social CRM house. J. Interactive Marketing 27(4):270–280.Crossref, Google Scholar
• McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annual Rev. Sociol. 27(1):415–444.Crossref, Google Scholar
• Mesnage CS, Rafiq A, Dixon S, Brixtel RP (2011) Music discovery with social networks. Anglade A, Celma O, Fields B, Lamere P, McFee B, eds. Workshop on Music Recommendation and Discovery (Association for Computing Machinery, New York), 1–6.Google Scholar
• Neslin SA, Gupta S, Kamakura W, Lu J, Mason CH (2006) Defection detection: Measuring and understanding the predictive accuracy of customer churn models. J. Marketing Res. 43(2):204–211.Crossref, Google Scholar
• Oztekin A, Delen D, Turkyilmaz A, Zaim S (2013) A machine learning-based usability evaluation method for eLearning systems. Decision Support Systems 56(December):63–73.Crossref, Google Scholar
• Oztekin A, Kizilaslan R, Freund S, Iseri A (2016) A data analytic approach to forecasting daily stock returns in an emerging market. Eur. J. Oper. Res. 253(3):697–710.Crossref, Google Scholar
• Passant A, Raimond Y (2008) Combining social music and semantic web for music-related recommender systems. Finin T, ed. 7th Internat. Semantic Web Conf. Citeseer (Semantic Web Science Association, Karlsruhe, Germany), 1–6.Google Scholar
• Predd JB, Osherson DN, Kulkarni SR, Poor HV (2008) Aggregating probabilistic forecasts from incoherent and abstaining experts. Decision Anal. 5(4):177–189.Link, Google Scholar
• Quijano-Sanchez L, Recio-Garcia JA, Diaz-Agudo B (2011) Happymovie: A Facebook application for recommending movies to groups. Grégoire E, ed. 2011 IEEE 23rd Internat. Conf. Tools Artificial Intelligence (IEEE, Hoboken, NJ), 239–244.Crossref, Google Scholar
• Ripley B, Venables W (2015) R-package nnet: Feed-forward neural networks and multinomial log-linear models, v. 7.3-12. Retrieved July 30, 2015, https://CRAN.R-project.org/package=nnet.Google Scholar
• Ripley BD (2007) Pattern Recognition and Neural Networks (Cambridge University Press, Cambridge, UK).Google Scholar
• Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: A new classifier ensemble method. Pattern Anal. Machine Intelligence, IEEE Trans. 28(10):1619–1630.Crossref, Google Scholar
• Rosaci D, Sarne GML (2014) Matching users with groups in social networks. Zavoral F, Jung JJ, Badica C, eds. Intelligent Distributed Computing, Vol. 7 (Springer International Publishing, Berlin),45–54.Crossref, Google Scholar
• Said A, De Luca EW, Albayrak S (2011) Using social and pseudo-social networks for improved recommendation quality. IJCAI 2011—9th Workshop Intelligent Techniques Web, 45–48.Google Scholar
• Sevim C, Oztekin A, Bali O, Gumus S, Guresen E (2014) Developing an early warning system to predict currency crises. Eur. J. Oper. Res. 237(3):1095–1104.Crossref, Google Scholar
• Shapira B, Rokach L, Freilikhman S (2012) Facebook single and cross domain data for recommendation systems. User Model User-Adapted Interaction 23(2–3):211–247.Crossref, Google Scholar
• Spackman KA (1991) Maximum likelihood training of connectionist models: Comparison with least squares back-propagation and logistic regression. Clayton PD, ed. Proc. Annual Sympos. Comput. Appl. Medical Care (American Medical Informatics Association, Bethesda, MD), 285–289.Google Scholar
• Specht DF (1990) Probabilistic neural networks. Neural Networks 3(1): 109–118.Crossref, Google Scholar
• Tu JV (1996) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clinical Epidemiology 49(11):1225–1231.Crossref, Google Scholar
• Tumasjan A, Sprenger TO, Sandner PG, Welpe IM (2010) Predicting elections with twitter: What 140 characters reveal about political sentiment. Hears M, ed. 4th Internat. Conf. Weblogs Social Media (ICWSM), Vol. 10 (Association for the Advancement of Artificial Intelligence, Menlo Park, CA), 178–185.Google Scholar
• Ulvila JW, Gaffney JE Jr (2004) A decision analysis method for evaluating computer intrusion detection systems. Decision Anal. 1(1):35–50.Link, Google Scholar
• Venkatesh K, Ravi V, Prinzie A, Van den Poel D (2014) Cash demand forecasting in ATMs by clustering and neural networks. Eur. J. Oper. Res. 232(2):383–392.Crossref, Google Scholar
• Verbraken T, Verbeke W, Baesens B (2013) A novel profit maximizing metric for measuring classification performance of customer churn prediction models. IEEE Trans. Knowledge Data Engrg. 25(5):961–973.Crossref, Google Scholar
• Wang G, Kulkarni SR, Poor HV, Osherson DN (2011) Aggregating large sets of probabilistic forecasts by weighted coherent adjustment. Decision Anal. 8(2):128–144.Link, Google Scholar
• Wei P, Lu Z, Song J (2015) Variable importance analysis: A comprehensive review. Reliability Engrg. System Safety 142:399–432.Crossref, Google Scholar
• Yoshida M, Gordon B, Nakazawa M, Biscaia R, others (2014) Conceptualization and measurement of fan engagement: Empirical evidence from a professional sport context. J. Sport Management 28(4):399–417.Crossref, Google Scholar
• Zhang Y, Wu H, Sorathia V, Prasanna VK (2013) Event recommendation in social networks with linked data enablement. Hammoudi S, Maciaszek L, Cordeiro J, Dietz J, eds. Proc. 15th Internat. Conf. Enterprise Inform. Systems (ICEIS), Vol. 2 (Springer, Berlin),371–379.Google Scholar