Statistical Analysis of Classification Algorithms for Predicting Socioeconomics Status of Twitter Users

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Zhou, Ying




The purpose of this study is to compare a series of well-known statistical machine learning techniques that classify online social network (OSN) Twitter users based on their socioeconomic status (upper/middle/lower). In the experiments, five classification algorithms are employed for the classification task. Logistic Regression, Support Vector Machine (SVM), Naïve Bayes (NB), k-Nearest Neighbors, and Decision Tree are applied on high-dimensional data set extracted from the users’ platform-based and profile-based behavior on Twitter. These algorithms are theoretically investigated and experimentally evaluated in terms of four performance measures: accuracy, precision, recall and, AUC. Ensemble methods are employed to improve the performance of the aforementioned algorithms. MANOVA is employed to examine if their performance measures are significantly different. ANOVA is used to analyze the differences of the classifiers for each performance measure. The analyses indicate a significant difference among these algorithms; both SVM and NB achieve good performance on our high-dimensional OSN data.






Carleton University

Thesis Degree Name: 

Master of Science: 

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Thesis Degree Discipline: 

Probability and Statistics

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Theses and Dissertations

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