Statistical Analysis of Classification Algorithms for Predicting Socioeconomics Status of Twitter Users
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".