A Logistic-Regression-Based Reduced Model for Predicting Online News Popularity
Bibliographic record
Abstract
Consuming news through social media has become an integral part of our lives, thus online news popularity prediction is now a crucial issue for marketing strategies and social media platforms. This paper proposes a real-time prediction model, based on a dataset from Mashable, it uses regression analysis to find out whether an article is of high-popularity or low-popularity. In this paper, we first solve the problem of imbalanced data via Synthetic Minority Oversampling Technique (SMOTE); then various feature selection and engineering methods are employed to extract key features. A comparison experiment is conducted to verify the validity of the data enhancement. And the results show that the reduced logistic model with SMOTE and dimension reduction outperforms other models with an Accuracy of 80.6% and a Recall of 78.6%. Our work can help the software application on evaluating the popularity of online news.
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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".